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Microfluidic analytical devices have gained significant traction by virtue of their unique merits in terms of miniaturization and attractive phenomena at microscale. The utilization of paper as a substrate material has witnessed substantial progress majorly due to its fast fluid transport, capillary action kinetics and improved detection limits bringing forward its techno-economic viability. This article provides light to the state-of-the-art developments in paper-based lab-on-a-chip (pLOC) devices for the detection of agri-food contaminants. Various fabrication processes for paper-based devices are elaborated along with their research progress. An analysis of its potential in detecting pesticides, viruses, food contaminants, foodborne pathogens, mycotoxins, and heavy metals in agricultural and food products has also been highlighted. Finally, the existing challenges and future prospects trending towards digitalization is emphasized to understand its feasibility and commercialization. pLOC devices offer numerous advantages due to its abundant availability of substrate, ease of fabrication, improved surface functionalization processes, as well as the ability to detect multiple analytes simultaneously (i.e., multiplexing) with low-volume requirements. There lies widespread opportunity with paper due to its porous cellulosic matrix and spontaneous channel flow of reagents or analytes enabling the development of standalone analytical devices. The sensitivity, selectivity, and repeatability are the crucial deciding parameters for evaluating the analytical performance of lab-on-a-chip sensors. A frugal engineering approach and protocol is ideally suited for the development of miniaturized sensors for on-site monitoring of contaminants in resource-limited settings. The monitoring of agri-food contaminants may further be combined with nanotechnology for signal enhancement, artificial intelligence and use of algorithms for the smart sensing to make it more user friendly for point-of-care applications. The use of pLOCs for the detection of agri-food contaminants has indeed paved the way to facilitate sustainable precision agriculture. [Display omitted] • pLOCs offer fast fluid transport, capillary action kinetics and high detection limit. • Notable progress is achieved on integrating pLOCs with nanomaterials to improve LOD. • A rapid, straightforward, and high-throughput manufacturing process is needed. • Cost-effective and high sensitive detection/readout mechanisms are required in pLOCs. • Integration of AI & ML can support the design, optimization and fabrication of pLOCs. [ABSTRACT FROM AUTHOR]

• Reviewed "precision agriculture", "smart agriculture" and "digital agriculture" concepts. • Suggested "smart agriculture" and "digital agriculture" be phases of "precision agriculture". • Proposed a few research focuses on smart and digital technologies for agriculture. [ABSTRACT FROM AUTHOR]

Precision dairy tools (PDT) can provide timely information on individual cow's physiological and behavioral parameters, which can lead to more efficient management of the dairy farm. Although the economic rationale behind the adoption of PDT has been extensively discussed in the literature, the socio-psychological aspects related to the adoption of these technologies have received far less attention. Therefore, this paper proposes a socio-psychological model that builds upon the theory of planned behavior and develops hypotheses regarding cognitive constructs, their interaction with the farmers' perceived risks and social networks, and their overall influence on adoption. These hypotheses are tested using a generalized structural equation model for (a) the adoption of automatic milking systems (AMS) on the farms and (b) the PDT that are usually adopted with the AMS. Results show that adoption of these technologies is affected directly by intention, and the effects of subjective norms, perceived control, and attitudes on adoption are mediated through intention. A unit increase in perceived control score is associated with an increase in marginal probability of adoption of AMS and PDT by 0.05 and 0.19, respectively. Subjective norms are associated with an increase in marginal probability of adoption of AMS and PDT by 0.009 and 0.05, respectively. These results suggest that perceived control exerts a stronger influence on adoption of AMS and PDT, particularly compared with their subjective norms. Technology-related social networks are associated with an increase in marginal probability of adoption of AMS and PDT by 0.026 and 0.10, respectively. Perceived risks related to AMS and PDT negatively affect probability of adoption by 0.042 and 0.16, respectively, by having negative effects on attitudes, perceived self-confidence, and intentions. These results imply that integrating farmers within knowledge-sharing networks, minimizing perceived risks associated with these technologies, and enhancing farmers' confidence in their ability to use these technologies can significantly enhance uptake. [ABSTRACT FROM AUTHOR]

• Significant reduction (18.44%) in overall Spanish GHG emissions from 2008 to 2018, highlighting some differences depending on the considered phase of the crisis. • The total intensity effect has been noticeable negative, especially in Agriculture, Transport, and Other Services. • The total carbonization effect has contributed to increase GHG emissions by 5.86%, being particularly negative in the case of Industry. • Per capita production effect was strongly negative in Industry and slightly positive in the rest of the economic sectors. • R&D, more eco-friendly technologies, green energies, circular economy and consumer green attitudes: the best strategies to reduce GHG emissions. The reduction of GHG emissions to reverse the greenhouse effect is one of the main challenges in this century. In this paper we pursue two objectives. First, we analyze the evolution of GHG emissions in Spain in 2008–2018, at both the global and sectoral levels, with the variation in emissions decomposed into a set of determining factors. Second, we propose several actions specifically oriented to more tightly controlling the level of emissions. Our results showed a remarkable reduction (18.44%) in GHG emissions, mainly due to the intensity effect, but also to the production-per-capita effect. We detected somewhat different patterns among the various sectors analyzed. While the intensity effect was the most influential one in the agricultural, transport, and others sectors, the production-per-capita effect was predominant in the case of industry. The carbonization effect was revealed as crucial in the commerce sector. The above findings highlight the importance of the energy efficiency measures taken in recent years in the Spanish economy, also pointing to the need to deepen those strategies and to propose new measures that entail greater efficiency in emissions. Additional efforts in areas like innovation, R&D, diffusion of more eco-friendly technologies, and a greater use of greener energies all prove to be essential reduction actions to fight the greenhouse effect. [ABSTRACT FROM AUTHOR]

Two important risks faced by many smallholder farmers in Sub-Saharan Africa are erratic weather patterns and insecure land tenure. It is likely these risks will increasingly interact as projections of more erratic weather make small-scale farming more difficult and demand for rural land grows. This paper asks how farmers in Western Tanzania view these compound risks and the influence this has on levels of investment in adaptive agricultural technologies and the demand for land certification in a lab-in-the-field setting. Presenting novel data from a series of framed decision tasks linked to a household survey, this paper explores the relationship between individual risk preferences, adaptive investment and the demand for land certification from a group of 645 rural households in Kigoma, Tanzania. While adaptive investment increases with weather related risk, we find it responds negatively to land tenure risk. Individual risk preferences and past experiences of land disputes play significant roles in adaptive investment. Demand for land certification is high, investment increases significantly after certification, especially for risk-averse individuals. • In a lab-in-the-field setting, adaptive investment responds strongly to land tenure and weather risk. • Investment in adaptive technologies is much lower than under expected income maximization. • Participants show high demand for land certification, but levels of willingness to pay are lower than current costs. • Risk-averse individuals show larger increases in investment after obtaining land certification. [ABSTRACT FROM AUTHOR]

Dairy farms have become more reliant on technology. The overall aim of this study was to better understand how dairy farmers view technology and its effects on animal care, including their views on the prospect of integrating gene-editing technology in the future. Virtual-semistructured interviews were conducted with dairy farmers (n = 11) from British Columbia and Alberta. To facilitate discussion, the participants were asked to develop and discuss a timeline describing when and why various technologies were adopted on their farm. Although farmers defined technology broadly and affecting multiple aspects of farm management, this paper focuses on their views regarding how technology can affect animal care. Following thematic analysis of the data, the following 3 themes emerged: (1) the changing role of the farmer (including intergenerational considerations and learning new technology), (2) the effect of technology on the cow and her relationship with the farmer and, (3) technology as the future of the farm. The discussions also highlight the concerns that some farmers have regarding challenges associated with reduced human-animal interactions and effective use of the large amounts of data that are collected through technology. We also specifically asked the participants their views about gene editing as a potential future technology. Most of the participants did not specifically address their views on gene editing, but they spoke about the effect on genetic technologies more generally, often making references to genomic testing. However, some questioned how this technology may affect farmers more generally and spoke about how it could affect human-animal relationships. These results illustrate differences among farmers in the way they view technology and how this can affect the dairy cattle they care for. [ABSTRACT FROM AUTHOR]

• This paper introduced the functional role of robotic arms in precision agriculture. • This paper summarized the hardware and software technologies of agricultural robotic arms. • This paper presented the application of robotic arms in various agricultural environments, including greenhouses, fields, and orchards. • This paper discussed the challenges and future prospects of robotic arms in precision agriculture. In precision agriculture, robotic arms exhibit significant technical advantages, such as enhancing operational precision and efficiency, reducing labor costs, and supporting environmental sustainability. This paper provides a comprehensive overview of the application of ground-based robotic arms in precision agriculture, analyzing the hardware and software aspects and current application status across various agricultural settings, and discussing challenges and prospects in this field. First, this paper explores precision agriculture and agricultural robotic arms, highlighting their critical roles in enhancing agricultural efficiency and automation. Further, it addresses the challenges plaguing the practical applications of robotic arms and compares innovative robotic arm technologies with traditional models to establish a foundation for understanding these advancements in modern agriculture. Additionally, this paper analyses the hardware of robotic arms, including rigid and flexible manipulators, drivers, end-effectors, sensors, and controllers, emphasizing the importance of innovation and optimization for improved performance. For the software systems, this paper focused on classic workflows and advanced algorithms for perception, motion planning, and control, as these are essential for the precise and adaptable functioning of robotic arms in diverse agricultural environments. Furthermore, this paper reviews the research and application status of robotic arms across various settings, including greenhouses (e.g., ground planting, desktop planting, and vertical planting), fields (e.g., dry fields, moist, and paddy fields), and orchards (e.g., fruit tree orchards, vineyard orchards, and ground-level orchards) to demonstrate their broad applicability and efficient operational capabilities in diverse conditions. Lastly, this paper discusses the challenges and prospects of robotic arms, emphasizing the significance of integrating disciplines, such as agronomy and biomimetics, big data, artificial intelligence, digital twinning, and human–machine interaction. Advancements in these areas are pivotal for the progress of robotic arm technology and for introducing innovative, efficient solutions to precision agriculture. In summary, this review reveals the immense potential of the application of robotic arms in precision agriculture. With ongoing technological advancements, these robotic arms are expected to play an increasingly crucial role in future agricultural production, making substantial contributions to achieving more efficient, innovative, and sustainable farming practices, heralding a new era in agricultural technology. This paper will serve as a valuable guide for researchers and practitioners, offering comprehensive insights into the use of robotic arms in precision agriculture and providing essential knowledge for advancing the field. [ABSTRACT FROM AUTHOR]

Urbanisation is leading to a concentration of growing city populations that contribute significantly to economic growth, while becoming epicentres of waste generation, greenhouse gas emissions, and food consumption. Nutrient smart city circular economy is currently an understudied intersection of growing city populations of food consumers, nutrient recovery technologies, Internet of Things (IoT), and agriculture. Meanwhile, machine learning has exploded with popularity over the years, with many circular economy literatures examining its usefulness in its predictive qualities to support management, optimisation, and recovery of useful resources from organic waste. This review paper examines advancements in machine learning for macronutrient recovery in city organic waste systems for a circular economy. The use of ML will greatly improve the scalability, transparency, productivity and accuracy of nutrient: recovery technologies, logistics, dissemination, and reuse. ML can also be combined with hardware to automate tedious waste separation, recovery and agricultural tasks using drones, hydroponics and satellites. Meanwhile, crop yields, nutrient demand-supply efficiencies, food security, environmental soil monitoring, and prosumer involvement could all increase. However, ML applications for urine, anaerobic digestion and prosumer economics are lacking. [Display omitted] • Lack of ML urine, membrane-AD, and prosumer studies for nutrient recovery. • IoT hardware, drones, democratised data sharing to address ML underfitting. • Lack of digital twins; CE BIM; good technologies and economics were barriers. • ML could increase transparency and food security for building consumer trust. • ML IoT to predict nutrients for soil, hydroponics, water treatment and farming. [ABSTRACT FROM AUTHOR]

Under the dual challenges of food security crisis and environmental costs, promoting agricultural green development (AGD) is an urgent need. Agricultural cleaner production place-based policy (ACPBP) is an important tool for promoting AGD. Based on the entropy value method to measure the AGD, DID is applied in this paper to investigate whether ACPBP can promote AGD. The conclusions of the paper are as follows: (1) The AGD is impacted differently by various types of ACPBP. The policy of replacing chemical fertilizers with organic fertilizers for fruits, vegetables and teas (RCFOFFVT) can inhibit AGD, national agricultural green development pioneer zone policy (NAGDPZP) can promote AGD. There is persistence in the effects of both RCFOFFVT and NAGDPZP. (2) There are regional heterogeneities in the effects of RCFOFFVT and NAGDPZP on AGD. The RCFOFFVT has a stronger inhibitory effect on AGD in low economic development counties and major grain-producing counties. The NAGDPZP has a stronger promotional effect on AGD in high economic development counties and non-major grain-producing counties. For low-carbon policy counties, the inhibitory effect of RCFOFFVT is insignificant and the promotional effect of NAGDPZP is weaker. (3) Agricultural Science and Technology Innovation (ASTI) moderates the impact of RCFOFFVT and NAGDPZP. ASTI would weaken the inhibitory effect of RCFOFFVT on AGD, and would strengthen the role of the NAGDPZP in promoting the AGD. (4) There is a policy mix effect of RCFOFFVT and NAGDPZP that can jointly promote AGD. The conclusions of the paper indicate that ACPBP are effective approaches to deal with the food crisis and environmental pressures, but in the process of policy implementation, different types of ACPBP need to be implemented based on the realities of the situation. [ABSTRACT FROM AUTHOR]

• Adopting ecological agricultural technologies is an effective way to achieve sustainable agriculture. • The ecological effect of digital economy empowerment should be emphasized. • We analyze how farmers' participation in the digital economy affects their adoption of ecological agricultural technologies. • The treatment effect model is used to address the endogeneity issue. • Farmers' participation in the digital economy can promote ecological agricultural technologies adoption. Adopting ecological agricultural technologies (EATs) is an effective way to guarantee food security and protect the environment. With the advancement of the digital village strategy, the continuous integration of the digital economy with the rural economy has gradually become an emerging driving force for sustainable agriculture. However, few studies have examined the impact of farmers' participation in the digital economy in different links of the agricultural industry chain on EATs adoption from a micro perspective. Using data from 2,825 grain farmers from the 2020 China Rural Revitalization Survey, this paper applies the treatment effect model and the mediating effect model to empirically analyze the impact and mechanism of participation in the digital economy on farmers' EATs adoption from an ecological effect perspective. This paper characterizes farmers' participation in the digital economy in terms of digital production, digital sales, and digital finance. The results show that participation in the digital economy significantly promotes farmers' EATs adoption. The sub-dimensional test found that participation in digital production, digital sales, and digital finance significantly promotes farmers' EATs adoption. The above findings remain robust after a series of robustness tests, such as changing the estimation method and replacing the dependent variable. Mechanism analysis shows that participation in the digital economy, especially digital sales and digital finance, could promote farmers' EATs adoption by increasing the demand for agricultural machinery services, improving information availability, and enhancing food safety cognition; participation in the digital production could promote farmers' EATs adoption by increasing the demand for agricultural machinery services. Results of heterogeneity analysis show that the promotion effect of participation in the digital economy on EATs adoption is more substantial among farmers with higher digital literacy and larger cultivated land scales. The findings of this paper provide insights for China and other countries to effectively use digital technology to promote sustainable agriculture under the pressure of achieving sustainable development goals. [ABSTRACT FROM AUTHOR]

Fertiliser has been a vital part of agriculture due to it boosting crop productivity and preventing starvation throughout the world. Despite this huge contribution, the application of nitrogen (N) fertilisers results in N leaching and the formation of greenhouse gases, which threaten the environment and human health. To minimise the impacts, slow/controlled release fertilisers (S/CRFs) have been being developed since the beginning of the 20th century. Despite the efforts made over a century, the basic terminological and classification information of these fertilisers remains vague. The scientific knowledge published in S/CRF patents has also been overlooked since the beginning. This review focused on the information gaps, clarified the definitions, differentiation and classification methods that have been randomly used in previous literature. The objectives, formulations and technologies of 109 controlled release urea patents involving sulphur coated urea, polymer coated urea and urea matrix fertilisers published in the years since these products emerged were also reviewed to 1) highlight the overlooked scientific knowledge in the patents; 2) understand the evolutionary processes and current research states of the products; 3) clarify research preferences and challenges to date; 4) identify remaining gaps for the future direction. It is expected that the organised basic information and the patent knowledge highlighted in this paper can be new resources and foster the development of S/CRFs in the future. [Display omitted] • There has not been a definition of and differentiation between slow release fertilisers and controlled release fertilisers. • Most literature classifies the fertilisers according to synthetic methods or material types used in manufacturing. • This review highlights scientific information published in patents. • Currently research is emerging for urea matrix, developing for polymer coated urea, and established for sulphur coated urea. • This review identifies the research preferences and challenges to date, and future research gaps. [ABSTRACT FROM AUTHOR]

Studies in the rheology of natural aqueous dispersions (soils and their mineral substrates) as well as the approaches to control rheological parameters of these systems are of the prime importance to progress in the modern agricultural technologies, geoengineering, construction, etc. To advance in these fundamental and applied directions both original processing of the experimental results and search for the promising modifying agents are required. In this paper, rheological behavior of a representative range of virgin and modified aqueous soils, quartz sand and clays was studied. Modification of the above aqueous dispersed systems with polyelectrolytes and their complexes provided variation of the basic viscoelastic parameters corresponding to the linear viscoelastic region (initial storage modulus G 0 ′ , initial loss modulus G 0 " , and strain γ 0) and to the transition to steady-state flow (storage modulus G in ′ and strain γ in) within 1 – 2 orders of magnitude. For samples studied, correlations for these mechanical characteristics were found – with the growth in the values of G 0 ′ and G 0 ′ ′ , the increase in the values of G in ′ and decrease in the values of γ 0 and γ in were observed. Based on these correlations the master curves for the strain dependences of storage and loss modulus as well as for loss tangent were constructed. The appearance of the master curves reflected the unified regularities of the rheological response of dispersed systems which were attributed to the general features of structural evolution caused by the mechanical loading. Procedure for prediction of the rheological behavior of aqueous soil samples based on the experimental measurement of only two experimental characteristics (initial values of storage and loss modulus G 0 ′ and G 0 ′ ′ respectively) was proposed. The validity of this approach was tested using independent experimental data. The original approach obtained can be used for express analysis of the rheology of natural aqueous dispersed materials. [Display omitted] • The rheological parameters of various aqueous soil substrates are in good correlation. • This correlation reflects the patterns of rheology of aqueous soil substrates. • These patterns provide the construction of rheological master curves. • Master curves model the rheology of native and polymer-modified soil substrates. [ABSTRACT FROM AUTHOR]

• AI-driven tools are pivotal for optimizing agriculture productivity in developing and emerging economies. • Big data and ML serve as catalysts for sustainable practices. •.Standardized frameworks is essential for comparing AI in agriculture, fostering collaboration among stakeholders. • Tailored digital solutions are imperative for overcoming specific agricultural challenges. • Collaboration of government, private sector, and educational institutions is essential to bring innovative technologies and methodologies. Agriculture is the backbone of numerous developing and emerging economies, supporting millions of livelihoods and playing a crucial role. The transformative integration of AI-driven tools, big-data analytics, and advanced technologies, such as drones and satellites, can transform agricultural decision-making in these developing and emerging economies and globally. Acknowledging the intricate web of biophysical and socioeconomic factors that influence agricultural systems, we highlight the pressing need for innovative approaches. We explore the critical role of big-data analytics, Computational Fluid Dynamics (CFD), Machine Learning (ML), and remote sensing in transforming the sector, with a focus on enhancing efficiency, sustainability, and productivity. In this context, continuous real-time monitoring becomes essential, allowing farmers to manage their agricultural systems with precision. Implementing AI-driven tools and devices enables the collection and analysis of data in real time, leading to timely decisions that can significantly improve crop yields and resource management. This study discusses the impacts of big-data analytics on agriculture, including reduced pesticide use and improved crop yields. It presents concrete examples of AI applications in crop and animal farming, emphasizing precision irrigation, fertilizer management, animal health monitoring, and reproductive efficiency. We advocate for customizing these technologies to suit local contexts, which can lead to ensuring deep integration into developing and emerging economies' agricultural practices. In conclusion, the paper calls for collaborative efforts from governments, the private sector, and educational institutions to make these technologies accessible, affordable, and culturally relevant. [ABSTRACT FROM AUTHOR]

[Display omitted] • Farmers must be an allrounder for plant growth, plant protection, legislation and different economic fields. • AI service integration holds great potential for assistance, documentation, education, interpretation forecasts or data-driven predictions. • LLMs depict a fundamental step to reduce the gap between AI-driven data analysis and common user. • Reproduction of processed data would increase confidence in the model support farmer's interpretation and guide the LLM for more precise results. • Introducing these technologies requires not only new training but also significant effort in structural transformation. Since the launch of the "Generative Pre-trained Transformer 3.5", ChatGPT by Open, artificial intelligence (AI) has been a main discussion topic in public. Especially large language models (LLM), so called "intelligent" chatbots, and the possibility to automatically generate highly professional technical texts get high attention. Companies, as well as researchers, are evaluating possible applications and how such a powerful LLM can be integrated into daily work and bring benefits, improve their business or to make the research outcome more efficient. In general, underlying models are trained on large datasets, mainly on sources from websites, and online books and articles. In combination with information provided by the user, the model can give an impressively fast response. Even if the range of questions and answers look unrestricted, there are limits to the models. In this paper, possible use cases for agricultural tasks are elucidated. This includes the textual preparation of facts, consulting tasks, interpretation of decision support models in plant disease management, as well as guides for tutorials to integrate modern digital techniques into agricultural work. Opportunities and challenges are described, as well as limitations and insufficiencies. The authors describe a map of easy-to-reach topics in agriculture where the integration of LLMs seems to be very likely within the next few years. [ABSTRACT FROM AUTHOR]

Despite accelerated developments in agricultural innovations, there has been a limited progress in their adoption by smallholder farmers, especially in the Global South. Innovations are not adequately reaching farmers, and new technologies are often unsuitable for farmers, or the overall innovation landscape is discouraging the deployment of innovations at scale. The methodological approaches to designing, testing and scaling of innovations play an important role. Involving farmers in the innovation process from the start increases the likelihood of them being more relevant and having a higher chance of adoption. Whereas there is much literature on participatory approaches, there is a limited guidance on choosing an appropriate level of farmer participation across the innovation process. This study aims to develop practical guidance for researchers, development agents and project managers on how to choose an appropriate methodological approach with a suitable level of farmer participation for designing, testing and scaling of agricultural innovations, according to levels of innovation readiness and innovation use. Relevant scientific literature on participatory design, research and scaling of agricultural innovations was reviewed, followed by compiling lessons learned from selected vegetable innovation projects implemented by the World Vegetable Center (WorldVeg) in Asia and Africa. A list of methodological approaches for participatory design, testing and scaling of innovations was generated, with various levels of farmer participation. A simple guiding framework was then developed for choosing an appropriate approach with a suitable level of farmer participation, according to the readiness and use of the innovations. Examples of selected approaches and application of the proposed guidance are given in project case studies. Based on the literature review and experience at WorldVeg, we believe it is critical to choose the right level of participation based on how ready are the innovations for application in the local setting. This dimension of how ready the innovations are has been largely overlooked in earlier frameworks on farmer participation. The developed guidance generally recommends increasing participation with higher levels of technology readiness and use. In addition, farmer consultation in early research design should also be strengthened, to co-design locally relevant innovations. The overall innovation systems' landscape must be considered when researching and scaling new innovations. The guidance, experiences and lessons provided in this paper will be useful to various scholars, researchers, development agents, funders and project managers who aim to design, test and scale agricultural innovations in a more inclusive and responsible way. [Display omitted] • Despite rapid developments in agricultural innovations, there has been limited adoption by smallholder farmers. • There is limited guidance on choosing an appropriate level of farmer participation in the innovation processes. • A list of participatory approaches for design, testing and scaling of innovations was created. • A guidance for choosing a suitable level of participation based on 'innovation readiness and use' was developed. [ABSTRACT FROM AUTHOR]

• A real-time mapping tool was implemented using infrared sensors installed on a center pivot. • The developed tool includes IRT and soil perception systems, mapping, visualizing and data storage. • Each IRT sensor location is determined using the geometrical dimensions of the central pivot. • The dynamic mapping allows for real-time irrigation prescription using neural network models. Efficient irrigation is essential for sustainable food production and regional water security. Temperature measurements provide information about crop water status, which allows the estimation of water needs and precise irrigation scheduling. This paper proposes a technology for agricultural irrigation management that combines cost-effective implementation with the ability to map crop canopy temperatures using a wireless network of infrared thermometers on a center pivot. The technology integrates Wireless Sensor Networks (WSN) with scalability options, IoT capabilities, and web data availability, enabling precise plant water stress quantification based on canopy temperature and soil water content. It facilitates real-time irrigation prescriptions while the center pivot is in motion, with canopy temperature sensors providing accurate readings of +/- 0.5 °C without additional calibration. Nonlinear Autoregressive Network with Exogenous Inputs (NARX) and Long Short-Term Memory (LSTM) neural network models were trained to predict irrigation prescriptions, achieving Root Mean Squared Error (RMSE < 4.18 mm) and correlation coefficient (r^2 > 0.82). This technology will allow the definition of real-time and site-specific prescriptions for variable-rate irrigation systems using infrared temperature sensors. Future work includes exploring LoRaWAN technologies, implementing a Differential Global Positioning System (DGPS), improving spatial resolution with data fusion algorithms, refining irrigation prescription models, reducing sensor costs and developing models considering soil and crop effects. [ABSTRACT FROM AUTHOR]

The global industrialization and modernization have witnessed a rapid progress made in agricultural production, along with the issue of soil heavy metal (HM) pollution, which has posed severe threats to soil quality, crop yield, and human health. Phytoremediation, as an alternative to physical and chemical methods, offers a more cost-effective, eco-friendly, and aesthetically appealing means for in-situ remediation. Despite its advantages, traditional phytoremediation faces challenges, including variable soil physicochemical properties, the bioavailability of HMs, and the slow growth and limited biomass of plants used for remediation. This study presents a critical overview of the predominant plant-based HM remediation strategies. It expounds upon the mechanisms of plant absorption, translocation, accumulation, and detoxification of HMs. Moreover, the advancements and practical applications of phyto-combined remediation strategies, such as the addition of exogenous substances, genetic modification of plants, enhancement by rhizosphere microorganisms, and intensification of agricultural technologies, are synthesized. In addition, this paper also emphasizes the economic and practical feasibility of some strategies, proposing solutions to extant challenges in traditional phytoremediation. It advocates for the development of cost-effective, minimally polluting, and biocompatible exogenous substances, along with the careful selection and application of hyperaccumulating plants. We further delineate specific future research avenues, such as refining genetic engineering techniques to avoid adverse impacts on plant growth and the ecosystem, and tailoring phyto-combined strategies to diverse soil types and HM pollutants. These proposed directions aim to enhance the practical application of phytoremediation and its integration into a broader remediation framework, thereby addressing the urgent need for sustainable soil decontamination and protection of ecological and human health. [ABSTRACT FROM AUTHOR]

• An unequal grouping algorithm is introduced in this work to address the energy-hole problem in Internet-of-things (IoT) and wireless sensor networks (WSNs). • The work introduced an energy level-based group head selection model that considers multi-objective parameters such as hop count, energy, and packet loss ratio. • An energy and delay aware routing optimization mechanism is introduced to reduce delay and minimize energy consumption. • Finally, a multipath-based route is established, and effective load balancing is done for transmitting packets with QoS (quality of service) and without QoS. • The existing methods' performance is generally studied considering total node death; however, the EDAR model is studied considering different scenarios such as first node death and total node death. • The simulation results show the EDAR is very efficient in reducing communication overhead, delay, enhanced lifetime, and improved throughput in comparison with the recent state-of-art routing mechanisms such as low energy adaptive clustering hierarchy (LEACH), unequal clustering algorithm (UCA), and reliable and energy efficient routing (REER). The adoption of modern technology into farming has seen significant expansion to enhance the quality and quantity of crop production. In this research work, wireless sensor networks (WSNs) and the Internet of Things (IoTs) are integrated to provide a real-time crop monitoring system to farmers. Usually, battery-powered IoT devices equipped with sensors like temperature, humidity, motion sensors, etc. are placed across agriculture areas for real-time monitoring of crops and cattle. In improving energy efficiency and reducing delay clustering multi-hop routing has been used with good effect; However, the existing methods failed to enhance network lifetime and target coverage due to failure in addressing load-balancing and hotspot problems. In addressing research problems this paper presents an Energy and Delay Aware Routing (EDAR) scheme for heterogenous WSN-IoTs leveraging unequal grouping of networks and adoption of multipath routing for provisioning IoT applications. The simulation outcome shows the EDAR model achieves very good performance in terms of throughput and lifetime performance with minimal delay and communication overhead. Graphical abstract of energy and delay aware routing design. [Display omitted] [ABSTRACT FROM AUTHOR]

• Described the current status of land-leveling implements. • Analyzed the current status of laser-controlled leveling technology. • Analyzed the current status of GNSS-control leveling technology. • Analyzed the benefits of the application of land-leveling technology. • Discussed the challenges and future trends of land-leveling technology. Land leveling technology is necessary for land cultivation and is an important support for sustainable agricultural development. First, this paper reviews the current status of land-leveling implements, including dry-land and paddy-field leveling implements. Second, two precision land-leveling technologies, laser-controlled systems and the Global Navigation Satellite System (GNSS), are reviewed. The current status of laser-controlled leveling technology is considered in terms of its three primary components, the laser transmitter, laser receiver, and control terminal. The current status of GNSS-controlled leveling technology is also considered in terms of three aspects: three-dimensional (3D) topographical measurement of farmland, calculation of reference height and soil volume, and planning of work paths. The actual benefits of land-leveling technology applied to the production of different crops are statistically analyzed using metrics including improved land utilization, water savings, and increased yields. Finally, the challenges and future trends in land-leveling implements and technologies are discussed. [ABSTRACT FROM AUTHOR]

• Comprehensive review of IoT-based agriculture management techniques for sustainable farming. • Detailed discussion on the introduction of AI and IoT-based intelligent techniques in achieving the UN SDGs. • Comparative discussions on manual versus technology-driven smart agriculture practices. • Identifying the research gaps that are essential for the sustainable implementation of IoT-based smart farming. The sustainable development goals (SDGs) of the United Nations seek to promote an agriculture sector that can lead change on both a local and global scale. By helping farmers to produce more food sustainably, Internet of Things (IoT)-based agriculture management solutions are significantly contributing to the achievement of these SDGs. By 2050, the world's population is expected to reach 9.7 billion, necessitating a 60% increase in food production efficiency and security in order to meet the growing population's demands. Smart farming powered by the IoT and utilizing artificial intelligence (AI) and machine learning (ML) algorithms is becoming a viable and sustainable method for increasing agricultural productivity and reducing environmental impact. The newest research and development initiatives in IoT-based smart farming are summarized in this comprehensive review article, along with how they might change the agricultural sector. The study gives a general overview of the machine abilities that smart farming is supported by, including perception, reasoning and learning, communication, task planning and execution, and systems integration. The use of robots and unmanned aerial vehicles (UAVs) in diverse agricultural contexts is also demonstrated in the paper, indicating the potential for improved productivity and diminished environmental effect. The report concludes by highlighting the significance of utilizing wireless communication LoraWan technology in the creation of smart systems that can further enable sustainable agriculture management. [ABSTRACT FROM AUTHOR]

Climate services are integral to climate change adaptation in agriculture. Inclusive innovation prioritises inclusion of marginalised actors, grassroots knowledge and accessibility of use. Yet many innovation processes are multi-stakeholder, complex and may not prioritise inclusion from the outset. In these circumstances, researchers and designers may seek to influence existing projects towards greater inclusivity. This paper explores efforts to increase the inclusivity of existing innovation processes, focusing on the design of My Climate View, an online Australian climate services platform for agriculture. Findings relate to contemporary issues in inclusive innovation literature, including: (1) Who should be included in innovation processes (within given resourcing)?; (2) How requirements for inclusion or exclusion can emerge through the design and use of a technology; and (3) The influence of program level structures upon inclusivity and design. Our approach is informed by Responsible Innovation and Inclusive Innovation theory and enacted through user-centred design. Findings are synthesised from analysis of 60 semi-structured interviews with farmers, advisors and extension officers. We additionally draw on observations as researchers in the multi-actor My Climate View development team, where extensive engagement separate to research activities has included agricultural and Indigenous stakeholders. Engaging with a broad sample of potential target users enabled diverse and sometimes unexpected use cases. Customisation of interface parameters allowed the incorporation of tacit knowledge and positively affected the inclusivity of My Climate View, extending the value of the platform to growers of niche commodities outside of the original scope. Inclusivity in innovation can be impacted by diverse factors, including design team decisions, structural factors, budget and external factors. Requirements for inclusion and exclusion can emerge and evolve throughout a design process, supporting Responsible Innovation's framing of inclusivity alongside principles of reflexivity and responsiveness. The findings enrich the evidence base for climate services and broader agricultural technology development processes by providing insights for those seeking greater impact by practicing inclusivity in innovation. We provide four implications for increasing inclusivity in the design of climate services: (1) Maximising customisation and avoiding prescriptive interpretation enables greater flexibility of use; (2) Sampling broadly within target users to uncover unanticipated values and opportunities for inclusion and exclusion; (3) It is important to embed efforts towards inclusion alongside processes of reflexivity; and (4) Responsible and inclusive approaches to climate services design are valuable even if brought in once development has commenced. [Display omitted] • Explores the challenges of steering a largescale climate services innovation project towards greater inclusivity. • Maximising customisation options for users and avoiding prescriptive interpretation enables greater flexibility of use. • Responsible and inclusive approaches to climate services design are valuable even if adopted once development has commenced. [ABSTRACT FROM AUTHOR]

As climate change and population growth intensify, the agricultural sector's need for sustainable solutions is paramount. This paper presents an overview of smart agriculture, a strategy leveraging technology and data analytics, and its potential to advance sustainability socially, economically, and environmentally. Drawing on recent research, we examine the integration of smart technologies in agriculture, their impact on sustainability, and the architectural design and key factors influencing their adoption. We critically review the literature on the effectiveness of smart agriculture, illuminating how smart farming strategies can curb environmental harm, stimulate economic growth, and promote social inclusivity. We identify existing research gaps and propose directions for future investigation, emphasising the need for rigorous studies on implementation strategies and long-term impacts of smart agriculture on rural landscapes. Our comprehensive overview aims to engage and guide diverse stakeholders, enhancing understanding of sustainable agriculture practices and encouraging more sustainable farming implementations. This paper contributes to global efforts towards harvesting a sustainable future. • Climate change and population growth drive the need for sustainable solutions in agriculture. • Smart tech in agriculture lessens environmental impact and boosts economic and social inclusivity. • Existing literature reveals the effectiveness of smart agriculture in achieving sustainability goals. • Guides stakeholders towards more sustainable farming practices and contributes to global sustainability efforts. [ABSTRACT FROM AUTHOR]

This research paper details the response to a central challenge of the digitalization of agricultural systems which stems from information overload and the broad heterogeneity of agricultural technologies and the classification thereof. It is the result of four years of effort on the Horizon 2020 SmartAgriHubs (SAH) project, whose purpose was the development and connection of Digital Innovation Hubs (DIHs) and Competence Centres (CCs) related to the agricultural sector in Europe. CCs form the backbone of DIHs and provide the digital technological infrastructure by offering advanced technical expertise, access to the latest knowledge and information on digital technologies, and other technological and scientific infrastructure. However, the lack of a general framework for technology classification in agriculture needed to be remedied, not only to specifically advance the general aim of SmartAgriHubs and DIHs to "connect the dots", but in general, so as to facilitate a broad range of actors involved in digitalisation of agriculture. This paper sets out the conceptual framework for the classification of technologies in the agricultural sector, and the development of the "Agricultural Technology Navigator" (ATN) as an iterative tool for gathering and searching for information to support such classification, so that actors involved in the digitalization of agriculture may more easily find and communicate technologies related to digital solutions. A multi-actor, multi-disciplinary expert group was formed to identify technologies and define keywords. A literature, patent, foresights studies, and grey literature review was also carried out. Text mining techniques, using the JCR "Tool for Innovation Monitoring", were used based on keywords selected by experts for documents analysis (+170,000,000 publications; + 22,000,000 patent applications; + 77,000 projects). Gaps in previous studies were identified to form the basis upon which to create a new classification system. Taking into account the fact that technologies are continually developing and evolving, an iterative approach was also necessary. To this end, a new tool was developed, compatible with agricultural supply chains, to gather "bottom up" information from CCs (and other actors) on innovative digital technologies and solutions. The main results of all the publications, patents and projects summarized herein, provide a scientifically sound foundation for a much-needed classification of digital technologies used in agriculture. This paper analyses different dimensions or categories in which the diverse technologies can be classified and selects the most appropriate classification to describe the technologies which could be used and managed by CCs and other agricultural actors, in offering and searching for systems and competences which provide digital solutions. Given the importance of DIHs and CCs, and more recently European DIHs (EDIHs), in the European agricultural policy agenda and the development of the sustainable digitalization of agriculture, it is necessary to have a mechanism to appropriately classify digital technologies relevant for agriculture so that providers and users can "match-make" and easily offer, find, and decide on relevant digital tools and solutions. CCs are not the only entities that benefit from such classification. A diverse range of actors involved in the sustainable digitalisation of agriculture, such as technology companies, farm advisors, machinery and other input suppliers who develop and deal in novel and different technology innovations, can benefit from being able to describe appropriately their technologies as part of agricultural systems and solutions. [Display omitted] • A conceptual framework for the classification of technologies in Agriculture is proposed. • The classification is based on information provided by Competence Centres and heterogeneous information sources. • Technologies are treated in the framework of systems and competences. • The ATN has been developed as a new tool for gathering information about systems and competences in agriculture. • Results come from the Horizon 2020 SmartAgriHubs (SAH) project dealing with Digital Innovation Hubs in agriculture. [ABSTRACT FROM AUTHOR]

Multi-stakeholder partnerships (MSPs), which involve the collective action of various institutions such as governments, private companies, NGOs, and donors, have become a popular approach for developing agricultural value chains to improve the sustainability of food systems. However, the growing body of literature presents a range of terms, concepts, and definitions related to MSPs, potentially causing confusion and obscuring the distinctions between different types of partnerships. While the potential benefits of MSPs have been extensively explored in the literature, there remains a need to investigate their realized impacts across regions to enable a more robust synthesis. The objective of this study was to systematically review the literature on MSPs in agricultural development, specifically examining the various concepts employed and assessing their realized impacts on farmers, focusing on developing countries. We systematically reviewed the literature on MSPs and identified 147 studies dealing with a different type of MSPs and 79 studies reporting the impact. We found that twelve terms have been used to describe MSPs. However, a single MSP initiative may be referred to by different names in various documents, indicating a lack of consensus on the defining characteristics of such partnerships. Positive impacts were reported on farmers' economic, social, and technological aspects, as well as environmental adaptation practices to address climate change. Nevertheless, the reported impacts tend to focus primarily on economic, technological, and social dimensions while giving less attention to environmental aspects. This paper presents, firstly, a comprehensive overview of the diverse practices of MSPs in developing countries. Secondly, it analyses the contributions made by MSPs across various commodities and regions in the development of agricultural value chains. [Display omitted] • Multi-stakeholder partnerships (MSPs) have become a popular approach for developing agricultural value chains. • This study examines the various concepts employed and assesses their realized impacts on farmers. • We found that a lack of consensus on the defining characteristics of such partnerships and positive impacts were reported. • This paper presents a comprehensive overview of the diverse practices of MSPs and analyses their contributions. [ABSTRACT FROM AUTHOR]

• Constructed a farmer portrait model based on production performance segmentation. • Proposed and validated an optimized clustering algorithm (GASA-WFCM). • Designed a labeling system to realize the description of farmers' portraits. • Identified typical characteristics of farmers with different production performance. • Findings can provide decision support for improving farmers' production effect. To identify the typical characteristics of farmers with different production performance, this paper constructs a farmer portrait model from the perspective of production performance segmentation. Firstly, we constructed the evaluation index system of production performance and quantified grape farmers' production performance. Then, an optimized clustering algorithm (GASA-WFCM) was proposed to cluster the grape farmers based on their production performance, which was based on fuzzy C-mean algorithm, and incorporating the genetic simulated annealing algorithm to overcome the problem of sensitive initial clustering centers and tendency to fall into local optimum, and the ReliefF algorithm was also integrated to strengthen the contribution ability of effective features to clustering. Finally, we designed a labeling system and feature transformation quantification method, and combined with the visualization effects to realize the description of grape farmers' portraits. The results of models training indicated that the convergence speed and division accuracy of the optimized clustering algorithm were significantly improved. By comparing the clustering results on the typical UCI datasets, it was demonstrated that GASA-WFCM algorithm improved the accuracy, NMI and ARI by 3.0%, 7.2% and 6.1%, respectively. Moreover, the segmentation method was applied in grape farmers dataset, and the findings revealed that Chinese fresh grape farmers may be clustered into three groups, namely high-yield-driven grape farmers, high-input-driven grape farmers and high -income grape farmers, respectively. This paper provided a set of methods of users clustering analysis and portrait from a theoretical point of view. The findings and suggestions can help provide accurate and focused decision support for improving the effect of agricultural technology promotion and farmers' production performance. [ABSTRACT FROM AUTHOR]

Agriculture automation is a primary issue and a rapidly developing field for the nation. The global population is growing swiftly, so there is a severe need to fulfill food demand. Traditional farming methods are insufficient to meet the rising demand, so they pressure using fertilizers to increase crop productivity. That fall impacts agricultural activity; sometimes, land stays barren and lacks fertility. This paper is focused on the deep analysis of smart farming-related components such as the Internet of Things (IoT), wireless communication technology, sensors, and hardware. There is serious concern about selecting technology, sensors, and hardware in the different agriculture practices. This may result in an increase in mechanization among various agricultural practices in an easy way. This paper provides a systematic extensive review of the implication of automation in agriculture. And addressed how agricultural operations may benefit from modern sensors, wireless communication technologies, and hardware. Although, major challenges and components have been discussed. Moreover, future applications for crop health, human health, and machine health are also addressed in this study. [Display omitted] [ABSTRACT FROM AUTHOR]

Nano Sensors are sensing devices with a dimension of less than or equal to 100 nm. They are incredibly tiny devices that transform physical, chemical, or biological substances into detectable signals. Because of this device's capacity to detect physical and chemical changes, nanotechnology has emerged as a technology of choice in a variety of industries. The device provides efficient and cost-effective methods for detecting and measuring chemical and physical characteristics. This overview discusses the status of Nano Sensors, as well as their accomplishments and potential applications toward downstream targets in medical, security, agriculture as well in Covid-19 detection. The paper provides a summary and critical analysis of various architectures (structures) employed in the development and use of Nano Sensors. Surface engineering is used to generate diverse chemistries for both general and specialised purposes. We derived fresh findings from available data on the mechanism, prospective development of various structures, approaches, and applications, and highlighted the contrasts and similarities in their characteristics and working processes. The review further summarised ability and future expected of this sensor in dealing with the various challenges where different nano sensors, types, fabrication techniques and applications with highlighted novelties of these techniques and applications are presented. [Display omitted] [ABSTRACT FROM AUTHOR]

In this paper, we present an overview of several challenges in arable farming that are well suited for research by the control engineering society. We discuss the global needs that these challenges are related to as well as the relation of these challenges to future applications of arable farming. For each of these opportunities we provide several concrete and detailed research questions. Particular attention is paid to the management of resources and sensors in farms. The objective of writing this paper is to further entice control engineers into the domains of agronomy and agricultural technology. [ABSTRACT FROM AUTHOR]

The objective of this study is to identify the system of implementation of small-scale biogas technology from the farmer's perspective, presenting all of the major stakeholders, factors and processes involved and to establish the principle relationships and feedbacks among them. This paper uses an innovative approach to the problems using the methodology of system dynamics and employing causal loop diagrams elaborated using data collected from the target groups (biogas plant owners, facilitators, key informants) in Vietnam from 2016 to 2017. The results show the complete causal loop diagram, where the motivation of farmers is a key variable that influences the final decision regarding purchasing (or not) a biogas plant and keeping it (or not) functional. The important variables and relationships are clustered into technical, financial, and satisfaction aspects. The causal loop diagrams will serve as a decision support and policy-making tool for influence assessments of various measures and decisions. • This paper uses an innovative approach that uses system dynamics. • A system dynamics complex model is proposed according to the motivation of farmers to well-functioning biogas plants. • The causal loop diagrams will serve as a decision support and policy-making tool for influence assessments of various measures and decisions. [ABSTRACT FROM AUTHOR]

Scaling up food security innovations in low-income rural environments has often failed to achieve substantive and lasting results. This poor performance can be attributed to dominant, linear approaches associated with spreading innovations which entail technology research and development and subsequent transfer to farmers. Such approaches tend to overlook complexity elements and non-linear processes in smallholder agriculture, including multiple stress factors such as climate variability and economic risks that make the uptake of new agricultural innovations more unpredictable. This article presents programmatic lessons from the Canadian International Food Security Research Fund (CIFSRF) on scaling up. It considers projects that: i) deployed successfully pilot-tested innovations to reach and benefit large numbers of beneficiaries; and projects that ii) used innovations as an entry point to catalyse systematic change in the food and agricultural sector. The paper also outlines several key scaling up principles that can encourage better understanding of relevant socio-ecological dynamics and complexities in intervention areas as a way to support innovations (at scale) that can contribute to more sustainable system outcomes. Finally, the paper reflects on how predominant definitions of impact at scale, centered around rather narrow indicators around economic growth and technology transfer, might consider more holistic goals that encompass integrated agricultural interventions that advance sustainable agri-food system outcomes. [ABSTRACT FROM AUTHOR]

This paper investigates the dynamics of energy poverty in Ghana using two nationwide cross-sectional datasets (Ghana Living Standards Surveys Round 5 and Round 6). Employing the Multidimensional Energy Poverty Index (MEPI) as a measure of energy poverty, this paper estimates that the share of energy poor people decreased from 88.4% in 2005/2006 to 82.5% in 2012/2013. The results indicate that although there has been a significant decline in the overall energy poverty in Ghana during the study periods, the incidence of energy poverty remains high. Moreover, the study findings show a large gap between urban and rural energy poverty over the two periods; rural people are almost twice as energy poor as urban people. These findings suggest the need for the incorporation of energy poverty reduction strategies into income poverty reduction strategies to help improve not only access to modern energy but also in terms of affordability. • There is a gradual transition from the use of traditional cooking energy sources to modern sources. • Overall energy poverty in Ghana has significantly reduced but incidence of energy poverty continues to be very high. • Rural people are almost twice as energy poor as urban people. • The goal of reducing energy poverty can be incorporated into income poverty reduction strategies. [ABSTRACT FROM AUTHOR]

The eradication of illicit drug crops in developing countries through international agricultural cooperation in alternative livelihoods is a new mode of clean production. This paper examines the market-oriented alternative livelihood (AL) model implemented by Chinese agricultural companies in Laos, focusing on land-based cooperation between these companies, local authorities, and communities. It aims to reveal the conditions for successful land-based cooperation and mutual trust building with local communities and government agencies. Three Chinese companies involved in alternative cultivation projects in Laos are selected as case studies. The findings highlight the market-oriented AL model as a beneficial approach to transition from illicit crop cultivation to clean production. The study emphasizes the importance of land-based cooperation and trust building for the sustainable operation of market-oriented alternative livelihoods. Challenges faced by the companies include managing international market risks and developing locally-led technical and management teams. The research underscores the need for inclusive innovation in land-based cooperation and alternative livelihoods in the Global South. It calls for further exploration of innovative approaches to inclusive cooperation, local empowerment, and capacity building. The study contributes to understanding the potential of international agricultural cooperation in promoting alternative livelihoods and sustainable development in illicit crop-growing regions. [ABSTRACT FROM AUTHOR]

Recently, smart agriculture has become an essential part of modern agriculture approaches from tillage, via plant seeding and grow support to their collection. With modern technologies, farmers can use substances like pesticides, herbicides, or fertilizers at precise dosages or to identify places on a field with specific production rates. The main objective of this study is to introduce a novel and a unique aerial image dataset of various fields acquired by UAV containing crops/weeds in the early phenophases captured in two different resolutions (2 mm and 7 mm per pixel). Secondly, the best super-resolution technique for high-resolution images, substitution with lower resolution is explored. For data acquisition, we employed DJI Matrice 600 equipped with a full-frame Sony Alpha A7R IV285 image sensor. Data were captured at flight heights of 26 and 95 m from 4 different fields in Central Europe. In addition, we proposed a methodology focused on the selection of an appropriate super-resolution method to enhance low-resolution aerial images to obtain better accuracy of crop/weed detection. As a baseline crop/weed detector for super-resolution effect evaluation, YOLOv5 architecture was used. Next, we explored the performance of several super-resolution models (U-Net++, ESRGAN, SwinIR), and fine-tuned the best-performed one. We present the new dataset named SPAGRI-AI: a novel unique dataset of aerial images for super-resolution experiments in smart precision agriculture. The dataset contains 27,638 aerial images (1024 × 1024 px) and additionally, it contains a subset of 2014 labeled images with 45,548 bounding boxes of 12 classes. The main purpose of the SPAGRI-AI is to provide the scientific community with real-world data to test new methods for super-resolution (SR) and crop/weed detection. During the evaluation of selected super-resolution models, the YOLOv5 model trained on high-resolution images resulted in corn mAP@0.5 of 94.48%. The YOLOv5 model trained on low-resolution images resulted in corn mAP@0.5 of only 51.43%. Nevertheless, if the low-resolution images were pre-processed using the SwinIR super-resolution method, corn mAP@0.5 of 62.36% was achieved. To the best of our knowledge, it is one of the largest datasets available to the paper's publication date. Overall, the SPAGRI-AI dataset and the findings from our experiments contribute to the advancement of super-resolution techniques and crop/weed detection methods in the field of smart agriculture. By utilizing real-world data and optimizing image enhancement approaches, we paved the way for further developments in precision farming practices and applying emerging technologies in agriculture. [Display omitted] • Dataset consists of 27,638 aerial images captured at two different flight heights (26 and 95 m). • Dataset contains a subset of 2014 labeled images with 45,548 bounding boxes across 12 distinct classes. • Baseline model trained on higher resolution data (127 PPI) achieved up to 95% of mAP@0.5 for corn detection. • Model employing super-resolution method on lower resolution data (36 PPI) improved detection performance by 11%. [ABSTRACT FROM AUTHOR]

In smart agriculture, the accurate prediction of evapotranspiration plays a crucial role in optimizing water usage and maximizing crop yield. However, the increasing adoption of IoT sensor technologies has resulted in the accumulation of large amounts of data, which are frequently contaminated by noise and pose a significant challenge to extract reliable knowledge through data modeling. This research addresses the problem of noisy IoT sensor data and its impact on evapotranspiration prediction, an essential aspect of agricultural practices. The effect of noise on sensor variables and evapotranspiration is extensively analyzed by simulating different noise levels in evapotranspiration datasets collected from various agricultural areas in Spain, enabling a comprehensive evaluation of its impact on the performance of data science models. Despite the potential consequences of this type of errors, a noise preprocessing stage is often overlooked in existing literature in this field, which is necessary to improve data quality prior to modeling. In order to address this challenge, this paper proposes the usage of regression noise filters as approach to mitigate the detrimental effects of noisy IoT sensor data on evapotranspiration prediction. Additionally, we introduce the rgnoisefilt R package, which offers a practical and efficient implementation of noise filtering techniques for regression datasets, providing a valuable solution for handling noisy data in smart agriculture applications. The experimental results obtained emphasize the negative impacts of noise on evapotranspiration prediction performance and highlight the importance of an appropriate data treatment to mitigate system deterioration. Furthermore, the findings of this research emphasize the efficacy of the regression noise filters implemented in the rgnoisefilt software, enhancing the performance of the models built and providing a valuable tool for improving data quality in smart agriculture. • Noisy IoT sensor data in evapotranspiration prediction are addressed. • This research analyzes the impact of different types of noise in the data. • Regression noise filters are proposed as a tool to improve data quality. • The rgnoisefilt R package provides efficient solutions for noisy sensor data. • The effectiveness of filters with noisy evapotranspiration data is shown. [ABSTRACT FROM AUTHOR]

Lack of capital hindered the development of agricultural production and the residential energy consumption transition in rural areas, but, with the continuous advancement of digital finance technologies, rural digital credit eased the liquidity constraint of agricultural production, which influenced residential energy consumption. However, few studies explored the relationship between rural digital credit and residential energy consumption. Hence, the instrumental variable (IV) method, the mediation effect model and the survey data that covered 295 effective samples were used to analyze the impact of rural digital credit on residential energy consumption from the agricultural production perspective in this study. The results indicated that rural digital credit resulted in a significant decline of the consumption of traditional energy products, but impacts of rural digital credit on electricity consumption and the new-style energy products consumption were not significant. Further, rural digital credit reduced traditional energy products consumption via boosting the level of agricultural mechanization and the adoption of precision fertilizer applying technique. This paper was conducive to not only developing the green credit markets in rural areas, but also enhancing the residential energy consumption transition and sustainable development. • Digital credit resulted in a significant decline of the consumption of traditional energy products. • Impacts of digital credit on electricity consumption and the new-style energy products consumption were not significant. • Digital credit reduced traditional energy products consumption via boosting agricultural mechanization. • Digital credit reduced traditional energy products consumption by adopting precision fertilizer applying technique. [ABSTRACT FROM AUTHOR]

With the facilitation of agricultural modernization, China's agricultural energy consumption continues to rise. This paper empirically tests the agricultural energy rebound effect and its influencing factors in China during 1999–2020. It is believed that the improvement of energy efficiency has not led to the expected energy-saving effect in agriculture due to the "Jevons paradox" of energy efficiency and energy consumption. The average energy efficiency of China's agricultural energy consumption was 0.446, and the average energy rebound effect was 68.9 %. The level of agricultural mechanization, the effective irrigated area of farmland and agricultural investment have a positive impact on the rebound effect of agricultural energy, and the direct effects are 0.062, 0.008 and 0.064, respectively, while agricultural labor force and energy price have a negative impact on the rebound effect of agricultural energy. In particular, technology spillover and scale spillover led by agricultural technology progress have strong negative space spillover effect. As it is argued, 1 % of agricultural technology progress can reduce 3.5 % of agricultural energy rebound effect in surrounding areas. In order to reduce the energy rebound effect, it is suggested to increase the subsidies to agricultural labors and rationally formulate regional differentiated energy regulation policies. • The internal mechanism of rebound effect is expounded from the view of producers and consumers. • The average energy efficiency of China's agricultural energy consumption was 68.9 % during 1999–2020. • The determinants of the agricultural energy rebound effect were further discussed by considering the spatial spillover effect. [ABSTRACT FROM AUTHOR]

This paper presents a study focused on the analysis of phytosanitary treatment sales in the Souss Massa region of Morocco. The objective of the study is to predict the sales of agricultural products, particularly crop protection solutions, aiming to optimize supply chain operations and meet customer demand effectively. Data for this study are collected from multiple sources, including the Enterprise Resource Planning (ERP) system called Microsoft Dynamics AXAPTA used by a leading agricultural company operating in the region. Information such as the date of sale, farming type, climate, and specific sales locations within the Sous Massa region is gathered. Machine learning techniques are applied for forecasting. Various regression models, including the Gradient Boosting Regressor algorithm, are employed to determine the most accurate predictor. Evaluation of the models reveals promising results, with a Mean Absolute Error (MAE) of 0.0035 and a Root Mean Square Error (RMSE) of 0.0066. The results obtained by applying various regression models, including the Gradient Boosting Regressor algorithm, demonstrate promising prediction scores. These findings contribute to the field of sales prediction in the agricultural industry while considering the impact of climate conditions, farming practices, and regional factors. [ABSTRACT FROM AUTHOR]

• Stepwise improved biophysical model is reusable for reduced and extended cases. • The studied food web models are sensitive to initial concentrations of plankton. • Model improvement requires parallel measurements to decrease sampling errors. • Programmable Process Structure serves as an extensible method to support reuse. • Reusability check contributes to the development of scaling-up and planning models. Given the increasing importance of data- and model-driven design and control in food production systems, this paper addresses the need to improve the reproducibility, replicability, and reusability of datasets, models, and modeling frameworks. While sensor data and machine learning-based control and operation of agricultural and aquacultural systems face reproducibility and replicability challenges, reusability is becoming critical for computational model-based design and planning of complex processes. This study evaluates the reusability of an existing pond aquaculture model and outlines a systematic, stepwise approach for reusability enhancements. The suggested methodology starts with an established reference model of a typical production fishpond and improves its reusability through pilot-scale experiments, covering key aspects of pond farming technologies. The reference model is subjected to stepwise reusability checks using measured data from the respective pilot units, progressing from simpler (reduced) to more complex (extended) cases. Each step concludes with the necessary parameter or sub-model refinements, which remain unchanged in subsequent steps. The refined model is validated with the measured data from other pilot experiments. This process can be repeated until satisfactory results are obtained. The resulting model is then tested to scale up a production pond model using limited case-specific input data. In addition, a hypothetically modified scenario is studied to address discrepancies between measured and simulated data. [ABSTRACT FROM AUTHOR]

• Amaranthus palmeri growth stage recognition was possible with YOLO architectures. • YOLO v8-X achieved the highest mAP@[0.5:0.95] for growth stage recognition of 47.34% • A. palmeri single-class outperformed 8-class recognition by over 20% (v7-original) • The first high-quality, benchmarking, growth stage dataset is provided. Many advanced image-based precision agricultural technologies for plant breeding, field crop research, and site-specific crop management hinge on the reliable detection and phenotyping of plants across highly variable morphological growth stages. Convolutional neural networks (CNNs) have shown promise for image-based plant phenotyping and weed recognition, but the ability to recognize growth stages, often with stark differences in appearance, is uncertain. Palmer amaranth (Amaranthus palmeri) is a particularly challenging weed plant in cotton (Gossypium hirsutum) production; due to high genetic diversity, it exhibits highly variable plant morphology across growth stages over a growing season, as well as between plants at a given growth stage. This paper investigates eight-class growth stage recognition of A. palmeri in cotton as a challenging detection case study for You Only Look Once (YOLO) architectures. In total, 26 architecture variants from YOLO v3, v5, v6, v6 3.0, v7, and v8 are compared on an eight-class growth stage dataset of A. palmeri. The highest mean average precision (mAP@[0.5:0.95]) for recognition of all growth stage classes was 47.34% achieved by v8-X, with inter-class confusion across visually similar classes. With all growth stages grouped as a single class, performance increased, with a maximum mAP@[0.5:0.95] of 67.05% achieved by v7-Original. Single-class recall of up to 81.42% was achieved by v5-X, and precision of up to 89.72% was achieved by v8-X. Class activation maps (CAM) were used to understand model attention on the complex dataset. Fewer classes, grouped by visual or size features improved performance over the ground-truth eight-class dataset. Successful growth stage detection highlights the substantial opportunity for improving plant phenotyping and weed recognition technologies with open-source object detection architectures. Additionally, the first open-access, benchmarking growth stage dataset ' Palmer amaranth growth stages – 8 (PAGS8) ' is presented, which is available here: https://weed-ai.sydney.edu.au/datasets/5c78d067-8750-4803-9cbe-57df8fae55e4. [ABSTRACT FROM AUTHOR]

Integrated Pest Management (IPM) technologies offer effective solutions to reduce the negative effects of crop pests while considering human and environmental health. However, disseminating these technologies faces several barriers, with one of the most significant being the lack of farmer awareness regarding their availability, deployment, and uptake. Digital tools are perceived as a new form of leverage for overcoming these barriers. This study analyzes current IPM digital tools and their potential to boost farmers' awareness of the deployment and adoption of IPM technologies. From a software engineering perspective, this study aims to emphasize the critical functionalities and limitations of various IPM dissemination tools. It provides valuable insights to improve the adoption process and streamline the dissemination of IPM technologies. Through a systematic search in Google, Scopus and Web of Science for journal articles, over 32 dissemination tools were identified. The study thoroughly assesses these tools and identifies 5 main limitations hindering their regular use, especially in developing countries. Among the most significant limitations are the inadequate representation of tools developed in developing countries, lack of agroecological customization, and insufficient offline functionalities. Building on these findings, a user-centered design is employed to propose a software architecture for a novel Decision Support System (DSS) tailored to farmers and experts. The architecture comprises a local database for offline access, a mapping engine for data visualization, a conversation module with a triangulation engine for knowledge sharing, and an agroecology engine for technology recommendation based on an agroecological classification of the user's landscape. Drawing from the review, identified limitations, and the proposed architecture, we illustrate how the resulting novel DSS is anticipated to improve the dissemination of IPM technologies. • First Literature Review that explicitly focuses on web-based Decision Support Systems (DSS) for Integrated Pest Management (IPM) since 2015. • In this literature review, 340 published papers up to April 2023 were retrieved, of which 32 primary studies were selected for a detailed analysis. • The main functionalities and modules of current web-based IPM DSS are discussed. • Five critical limitations are identified, providing insight for future DSS development and paving the way for further research in the field. • A novel three-tier software architecture is proposed as a road map for the development of innovative tools to enhance the dissemination rate of IPM technologies. [ABSTRACT FROM AUTHOR]

[Display omitted] • Constructing a dynamic analysis framework that is independent of the case study. • An empirical study using duration analysis. • The complementary effects of social learning and agricultural technology extension. • Different distributive effects of traditional and new technology extension. • Create a diversified and complementary technical information acquisition environment. Encouraging farmers to adopt environmentally friendly technology through the rational use of social learning and agricultural technology extension is an effective way to overcome the bottleneck caused by the slow diffusion of environmentally friendly technology. Based on expanding the existing objects of research on farmers' technology adoption behavior, this paper examines the influence of social learning and agricultural technology extension on farmers' environmentally friendly technology adoption behavior from a dynamic perspective. In doing so, it enriches theoretical and empirical research on farmers' technology adoption behavior. Specifically, this paper takes fertigation technology as an example, constructs a dynamic analysis framework that is independent of the case study, and finds that social learning and agricultural technology extension, as the main channels for farmers to obtain technical information, can shorten the duration from awareness to the adoption of fertigation technology. Then, based on survey data, this paper uses the discrete-time cloglog model to conduct an empirical test. The empirical analysis supports the theoretical analysis results, and there is a complementary effect between social learning and traditional and new agricultural technology extension. Heterogeneity analysis shows that social learning and new agricultural technology extension have a greater marginal improvement effect on farmers' fertigation technology adoption behavior in the middle-aged to young group, middle and high education degree group and above median land scale group. This paper provides not only new empirical evidence to explain farmers' technology adoption behavior under the background of the internet revolution but also a decision-making reference for how to accelerate the construction of multivariate complementary, collaborative and efficient agricultural socialized service systems. [ABSTRACT FROM AUTHOR]

Iraq's population will have reached 50 million by 2030, according to the Ministry of Planning. This population growth must be handled by ensuring food security stability in the face of expected obstacles resulting from global warming. The agricultural sector encounters limitations due to ineffective detection of persistent agricultural obstacles like pathogens, flawed soil analysis, insect infestations, inadequate irrigation, and drainage. More proven farming strategies must be transferred using modern technological leaps from this vantage point. It is clear from the outset that this paper aims to discuss how artificial intelligence might aid in the growth of conventional methods in agriculture. This involves tracking and guiding each stage of agriculture through the adoption of an intelligent system, which in turn includes managing a set of intelligent systems that control and supervise the rest of the processes (System of Systems or SoS), beginning with ploughing, crop pattern, type of fertilizer, and the appropriate type of irrigation. On the other hand, this technology collection could give farmers an accurate view of market trends and customer preferences, resulting in greater output. This paper gives an insight into the concept of a SoS and the application of artificial intelligence in various fields of agricultural engineering in Iraq. It also proposes an intelligence framework model for self-cultivation and incorporates competitive strategies to develop a decent ecosystem to transform traditional agriculture into smart and accurate agriculture. [ABSTRACT FROM AUTHOR]

• Microwave detection and image processing have great potential for livestock farming. • Continuous detection and early disease and aberrant behavior identification are made possible by non-contact sensing technology. • Integration of non-contact sensing technologies in smart farms to enhance precision livestock farming practices and improve efficiency. • This paper also summarizes the advantages, challenges and prospects of non-contact sensing technology. More efficient, sustainable, and precise agri-sensing technologies will be adopted in the future to promote precision livestock farming (PLF). Through a detailed survey of research related to sensing technology, we believe that non-contact sensing technology (infrared detection technology, Microwave radiometry technology, Image processing and machine vision, and acoustic detection technology) hold promise for application in intensive livestock production system. Animal welfare has always been a concern of the scientific community, as well as improving production efficiency has been the goal of the intensive livestock industry. With the help of the Internet of Things, big data, artificial intelligence, etc., non-contact sensing technology can better play its role. In this paper, we analyzed the background and technical characteristics of non-contact sensing technology and proposed a new application for applying non-contact sensing technology to intensive livestock farming. These technologies are now gradually being applied and practiced in the livestock industry and will facilitate physiological detection in the intensive livestock industry, potentially protecting animal welfare. The precise adoption of management measures based on better knowledge of animal status can also improve production efficiency. In addition, this paper discusses the advantages, challenges, and prospects of non-contact sensing technology in livestock farming. [ABSTRACT FROM AUTHOR]

• Agriculture is crucial for human development and depends heavily on irrigation. This paper presents an autonomous irrigation system using AI and 6G-IoT. • The system uses a prediction algorithm based on weather history data to anticipate rainfall patterns and climate changes. • It creates an intelligent system that irrigates the fields according to environmental conditions. • The developed system achieved an accuracy of 86.34%, sensitivity of 89.28%, and precision of 91%. Agriculture plays a vital role in the growth of humankind. The total world depends upon the agricultural contribution, and wherein the agriculture purely depends upon the necessity of irrigation. This paper develops an autonomous irrigation system using Artificial Intelligence (AI) and the 6G enabled Internet of Things (6G-IoT) transforming to create a hassle-free smart agriculture model. The developed autonomous irrigation system is purely based on prediction algorithm which is mount in a microprocessor, which uses weather history data using IoT to identify and predict rainfall patterns and any climatic changes; hence it creates an intelligent system which irrigates the field depending upon the outer environment. The developed system not only checks the environmental condition, but it also measures the moisture content of the soil to supply the required amount of water to the crops. The developed autonomous AI and 6G-IoT based smart agricultural irrigation system has been tested under control environment with an accuracy of 86.34%, sensitivity of 89.28% and precision of 91%. Hence, the proposed methodology could be the possible solution for autonomous irrigation of crops in future. [ABSTRACT FROM AUTHOR]

• Analyzed the types and characteristics of multi-agricultural scenes. • Summarized the principles and models of agricultural autonomous navigation technology. • Summarized the agricultural autonomous navigation system and its architecture. • Summarized the research status of autonomous navigation technology in multi-agricultural scenes. • Development challenges and future trends of autonomous navigation technology in multi-agricultural scenes are reported. Due to the increasing demand for food, the global shortage of agricultural labor and the reduction in the utilization of agricultural resources, the demand for agricultural autonomous navigation technology and equipment has become increasingly urgent. Compared to the closed express structured environment for self-driving cars, the agricultural scene is complex and diverse, and its navigation modes and core technologies are also significantly different. In view of this, it is of great value to summarize the development characteristics and trends of autonomous navigation technology in fields, orchards, greenhouses and other scenarios. In this paper, (1) firstly, the types and characteristics of multi-agricultural scenes are analyzed, the principle and mode of agricultural autonomous navigation are expounded, and the agricultural autonomous navigation system and architecture are introduced; (2) secondly, the research status of autonomous navigation technology in open field agriculture, orchard agriculture, facility agriculture, livestock and poultry agriculture and aquatic agriculture is investigated; (3) finally, it is predicted that the differentiated autonomous navigation technology scheme for multi-scene agriculture, multi-dimensional perception, self-understanding navigation technology in agricultural scenarios, selective autonomous navigation technology for task self-matching fusion, the cooperative autonomous navigation technology for multi machine ad hoc network clusters, and the fault self-checking remote diagnosis navigation system are the future research hotspots and development trends. At the same time, it is clear that the in-depth practical application of artificial intelligence, the in-depth research and development of high-precision and low-cost sensors, and the collaborative integration technology of agricultural machinery and agronomy are the critical path to promote the breakthrough of agricultural autonomous navigation technology. The summary and prospect of this paper have positive significance for promoting the overall development of agricultural autonomous navigation technology. [ABSTRACT FROM AUTHOR]

This study was motivated by the activation of recent poverty alleviation practices within green supply chains. Specifically, this paper examined supply chain operations when the supply chain participants acted individually and cooperatively in a "green poverty alleviation" supply chain in which the manufacturer initiated product "greening" and provided microfinance to alleviate the poverty of poor raw materials suppliers. A series of supply chain game models were examined to elucidate the impact of the cooperative mechanisms on the greening levels, prices and profits, after which the impact of consumer green sensitivities and microfinance interest rates were examined. Consequently, an optimal cooperative mechanism was developed based on a two-part tariff contract. The key contributions in this paper are the establishment of a decision-making model for a cooperative supply chain mechanism and an optimal cooperative mechanism to initiate "green poverty alleviation" to solve the problems associated with the high microfinance interest rates. Practical implications and managerial insights about improving the economic and environmental performance of supply chain "green poverty alleviation" through cooperation are also presented. • Sustainable supply chain mechanism for green and poverty alleviationinitiatives • Joint action and bargaining cooperative mechanism for the sustainable supply chain. • Cooperative mechanism to solve the problem of high interest rate of microfinance. • Green sensitivity increasing will promote supply chain enterprises to cooperate. • A coordinated cooperation mechanism will further promote cooperation. [ABSTRACT FROM AUTHOR]

Aerostatic bearings have been employed as an essential precision engineering element and enabling technology in numerous applications requiring precision and ultraprecision motions. This review paper aims at presenting the state-of-the-art in aerostatic bearings research and development with the emphasis on analytical and computational approaches for design and optimization of bearing performance, and further critically reviewing their future research directions and development trends in the coming decade and beyond. The paper is concluded with the discussion on the future trends and challenges in the aerostatic bearings research, and their applications and potential in precision engineering industries. [ABSTRACT FROM AUTHOR]

Highlights • A novel power-shift transmission using the hydro-viscous drive (HVD) technology is proposed. • The gear ratios of this novel transmission and the parameters of the HVD device are designed based on the operation requirements of tractor. • The dynamical model of the drivetrain for tractor starting is established and the control strategy for automatic starting is developed to match this novel power-shift transmission. • The availability and effectiveness of this novel power-shift transmission designed in this paper are verified by simulation analysis. Abstract In this paper, a novel power-shift transmission using hydro-viscous drive (HVD) technology is proposed to improve starting comfort without affecting the drivetrain service life of a tractor. The tractor equipped with this novel power-shift transmission can realize stepless speed-regulation crawling. The structure of the tractor drivetrain, transmission scheme, gear ratios, and parameters of the HVD device are designed based on the operation requirements. The dynamical model of the drivetrain for tractor starting is established, and the control strategy for automatic starting of the tractor is developed to match this novel power-shift transmission. To verify the availability and effectiveness of this novel transmission in tractor automatic starting, a simulation model is established and the automatic starting processes under various driver's intentions are simulated. Accordingly, the simulation results show that this novel power-shift transmission cooperated with reasonable automatic control strategy can achieve a better starting effect. The tractor can realise 'soft starting' with small jerk as well as high comfort under starting intention of the driver. Moreover, the HVD device only works in the stage of hydro-viscous friction, which can prevent the failure of transmission caused by large temperature increase and contact friction. [ABSTRACT FROM AUTHOR]

Highlights • Paper presents a web platform that helps farmers with farm management and monitoring. • The architecture of the platform and of the system containing it is presented. • The platform has two main applications, decoupled one from another. • We present the technologies used and a motivation for choosing them. • We analyzed the performance of the platform through several tests. • Tests reveal the load times of the platform and the response times of the services. Abstract Smart farming is a relatively new domain which has popularity nowadays. It emerged from the need to produce more with less effort and it consists of integrating modern technologies in conventional agriculture to raise the quality and the quantity of agricultural products. This paper describes an integrated web-service platform which aims to increase the quality of products grown in farms and to support business development in agriculture related fields. The platform enjoys the benefits of Cloud computing like flexibility, availability or security and it can be accessed at any time, in any place by using just an Internet connection. It allows users to locate and efficiently manage their farms providing access to different types of statistics and predictions. It also acts as a social network allowing users to interact with each other by sending private messages or posting on the forum or on the blog. In addition to describing the services offered by the platform and the interaction between them, this paper also presents the architecture of the system and the performance test results which prove the efficiency of the platform. We have performed in lab validation of the proposed solution, using real equipment and different users with different roles. [ABSTRACT FROM AUTHOR]

• Considering pistachio, a supply chain network is firstly designed in this paper. • A mixed linear mathematical model is formulated based on the proposed problem. • Efficient and well-known old and recent meta -heuristic algorithms are utilized. • Three hybrid meta -heuristic algorithms are developed. • The quality of the proposed algorithms is investigated and compared. Nowadays, recent advances and developments in the agricultural sector have raised concerns regarding the environmental impact of agricultural wastes and the safety of agricultural products. Recently, reverse logistics have been used to address these issues. It is also possible to reduce the environmental impact of these by-products and return them to the network by opening compost centers and designing an optimal supply chain distribution network. In this paper, a new mixed linear mathematical model is developed for an agriculture supply chain network to minimize the total fixed and variable costs of the closed-loop supply chain,. To address the proposed model, efficient and well-known old and recent meta -heuristic algorithms are utilized. Besides, to boost intensification and diversification phases, three hybrid meta -heuristic algorithms are developed. Finally, the quality of the meta -heuristic and hybrid algorithms are investigated and compared. [ABSTRACT FROM AUTHOR]