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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]

[Display omitted] • A real-time grading method of roasted tobacco quality based on near infrared spectroscopy. • A key indicators selection method based on information gain and Fisher criterion was proposed. • A chemical indicator grading table was designed for reference. • Chemical indicator grading based on near infrared spectroscopy is a convenient, rapid and promising grading method. Traditional tobacco quality grading methods have limitations such as high human resource consumption and poor consistency, resulting in the grading accuracy being affected. Near infrared spectroscopy (NIRS) technology, as a rapid and non-destructive analytical method, has been widely used for online inspection. This study aims to accomplish rapid, accurate and reliable real-time grading of tobacco quality by employing online near-infrared spectroscopy to detect key chemical indicators. A key indicators selection method based on information gain and Fisher criterion (IFRI) was proposed in this paper by associating tobacco chemical indicators with traditional quality grades. The key chemical indicators were selected from many indicators by two classical feature selection functions, and a chemical indicator grading table was designed based on a large number of statistical data on tobacco chemical indicators, and the table was applied to grade the tobacco quality of real data from different years. The results show that the IFRI algorithm is an effective method for selecting key indicators, and the selected reducing sugars-to-total nitrogen ratio and nicotine are the key indicators for tobacco quality grading. Moreover, we demonstrated that the designed grading table is reasonable and feasible. Compared with traditional grading, chemical indicator grading based on near infrared spectroscopy can not only be realized quickly by using an online near infrared instrument, but also the tobacco of the same grade is less discrete, better consistency and more representative after grading, and it is applicable to tobacco samples of different years, which is a convenient, rapid and promising grading method that can provide a reference for tobacco quality grading. Furthermore, the formulation design by utilizing different grades after grading can greatly improve the utilization rate of tobacco leaves. Notably, the method can also be applied to other fields, such as quality control of agricultural products, monitoring of food processing processes, etc. [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]

Output uncertainty is a major concern for industries prone to exogenous, persistent and large fluctuations in output, such as agriculture, wind and solar power generation, while technology adoption aimed at mitigating output uncertainty can improve social welfare. This paper constructs a competitive market model with random output fluctuations to examine the scale of technology adoption at the long-term equilibrium and its comparison with the social optimum. We show that the First Welfare Theorem no longer holds in general, and depending on the characteristics of the demand function, the scale of technology adoption in the competitive market may be greater or less than the socially optimal scale. • We consider technology adoption aimed at mitigating output uncertainty in the competitive market. • We show that the First Welfare Theorem no longer holds in general. • We show that the scale of technology adoption in the competitive market may be too high or too low. [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]

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]

Low carbon agricultural techniques, which aim to reduce agricultural emissions without compromising productivity, are being widely adopted as one strategy to promote more sustainable food systems. Evaluating the impact of such practices requires an effective monitoring, reporting and verification (MRV) system. To our knowledge, no MRV system has been designed or implemented for monitoring and measuring the impacts of the adoption of low carbon agricultural practices. The goal of this paper is to explore the opportunities and challenges of developing a large scale MRV system for low carbon agriculture in Brazil - a major global food producer. The analysis presented in this paper is based on primary data, collected through interviews and conversations with experts, and secondary data from a literature review as well as from public and private databases. Hence, we discuss the three phases of monitoring, reporting and verification. In each case, we analyze the informational needs of an MRV system, evaluate the strengths and limitations of available tools to collect and process these data, and present a vision of how these constituent parts could form a comprehensive MRV system for Brazil. Our analysis reveals technical and governance challenges. On the technical side, challenges include the need to develop emissions factors to calculate mitigation across a range of biomes and low carbon agricultural techniques, and the high costs of gathering and verifying information on farm level. On the governance side, there is a need to generate economic incentives for farmers to share private, farm-level data with a public MRV system. The MRV system could help producers to demonstrate adoption and mitigation, giving them improved access to low-interest credit or certification programs. We conclude that the implementation of a large scale MRV system for low carbon agriculture demands an integrative approach based on a hybrid public-private governance system that prioritizes cost-effectiveness and facilitates smooth information transfer. • Low carbon agriculture needs Monitoring Reporting and Verification (MRV) systems for measuring and reporting GHG emissions. • We propose an MRV system for Brazil, a country that has huge mitigation potential due to its agricultural activity. • Primary farm-level data about adopted techniques could be combined with remotely-sensed and secondary data about land use. • A hybrid public-private governance strategy could reduce costs and create incentives. • Brazil's case is a relevant benchmark for MRV systems in other countries. [ABSTRACT FROM AUTHOR]

Smart farming is a new concept that can be described as applying different kinds of modern technology to make farming more efficient. One such technology is the Internet of Things, an extension of the Internet and other connections into mundane devices. This paper uses dedicated sensors and cloud computing technologies to model a smart farming system that helps farmers keep track of state of the crops and livestock. It also covers implementing and testing an Android tracker app that is supposed to become the first component of the mentioned system, a client application that communicates with the cloud computing services yet to be implemented. The end product provides its users with an easy way of keeping track of the state of their farms, simplifying the farmers' work and enhancing productivity. [ABSTRACT FROM AUTHOR]

Supply chain management (SCM), product traceability, and product quality certification are some of the initial prominent uses of blockchain technology. This is because blockchain technology is built on trust and cannot be changed. The popularity of blockchain technology is increasing as it is trustworthy and cannot be changed; hence making traceability easier for information such as where food came from, thus essential for managing the agricultural food supply chain (SC). On the contrary, suggested SCM systems premised on smart contract technology and blockchain tend to be specifically apply to production and production processes. Through qualitative research, this paper suggests framework for adoption of smart contracts based on Ethereum in the agri-food SC, with the aim to establish means to adopt blockchain for checking food quality and origins in the agri-food supply chain. Remix IDE is utilised to build and test the smart contract. The significance of the framework is realised through ensuring the traceability of product safety and quality. [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]

Given persistent gender inequalities that influence how the benefits of technologies are distributed, the expansion of small-scale irrigation technologies requires the consideration of important gender dynamics and impacts. Women's lack of agency and access to resources relative to men, and other social constraints, often limit their ability to adopt and benefit from agricultural technologies. At the same time, expanding access to agricultural technology to women may provide a pathway for empowerment. This paper explores the potential for small-scale irrigation technologies to increase women's empowerment by evaluating the impacts of an intervention that distributed motor pumps to small groups of farmers in Northern Ghana. The paper draws on two rounds of survey data that included the Women's Empowerment in Agriculture Index, before and after the motor pump intervention was implemented. To control for possible selection bias at the baseline, the difference-in-difference method is used to estimate the impact of the program on indicators of women's empowerment. Spillover effects are estimated by comparing outcomes of farmers in treatment villages that did not receive the pumps with farmers in control villages, where no motor pumps were distributed. The results show no significant impact of the program on measures of women's empowerment. However, there are potential negative impacts, including among households that did not benefit from the intervention. The results highlight the need to pair interventions that distribute agricultural technologies with complementary investments in infrastructure that increase access to water for irrigation, as well as other activities and approaches that ensure women can reap the benefits. • Supplying men and women farmers with motor pumps for small-scale irrigation did not increase women's empowerment. • Rather, the intervention had negative spillover effects on women from households that did not receive pumps. • Women's empowerment in the study communities is generally increasing over time irrespective of the irrigation intervention. • The benefits of the motor pump intervention for women are indirect, including increasing household asset holdings. • More careful planning and implementation of irrigation interventions are needed facilitate women's empowerment. [ABSTRACT FROM AUTHOR]

• Cybernetics systems have been developed to monitor animal production. • Machine vision methods are key for precision poultry welfare detection. • Robotics is an emerging method for poultry farm management. • Sustainable poultry farming requires continuous innovation in cybernetics. As global demands on the poultry production and welfare both intensify, the precision poultry farming technologies such as computer vision-based cybernetics system is becoming important in addressing the current issues related to animal welfare and production efficiencies. The integration of computer vision technology has become a catalyst for transformative change in precision farming, particularly concerning productivity and welfare. This review paper delineates the central role of computer vision in precision poultry farming, focusing on its applications in non-contact monitoring methods that employ advanced sensors and cameras to enhance farm biosecurity and bird observation without disturbance. We delved into the multifaceted advancements such as the utilization of convolutional neural networks (CNNs) for behavior analysis and health monitoring, evidenced by the high accuracy sorting of eggs and identification of health concerns within target-dense farm environments. The review paper underscores advancements in precision agriculture, including accurate egg weight estimation and egg classification within cage-free systems, paralleling the poultry sector's evolution towards more ethical farming practices. Moreover, it addresses the progress in poultry growth monitoring and examines case studies of commercial farms, showcasing how these innovations are being practically applied to enhance productivity and animal welfare. Challenges remain, particularly in terms of environmental variability and data annotation for deep learning models. Nevertheless, the review emphasizes the scope for future innovations like voice-controlled robotics and virtual reality applications, which have the potential to enhance poultry farming to new levels of efficiency, humanity, and sustainability. The insights assert that the continued exploration and development in computer vision technologies are not only instrumental for the poultry sector but also offer a blueprint for agricultural enhancement at large. [ABSTRACT FROM AUTHOR]

• Agriculture-BERT is compared to other BERT models on agricultural term extraction. • With a few exceptions, Agriculture-BERT performs better than the other BERT models. • Embedding + encoder layers update scores best for extracting terms seen in training. • Embedding layer frozen + encoder layers updated scores best for extracting synonyms. • Embedding layer + top 4 encoder layers updated scores best for novel term extraction. This paper compares different transformer-based language models for automatic term extraction from agriculture-related texts. Agriculture is an important economic sector faced with severe environmental and societal challenges. The collection, annotation and sharing of agricultural scientific knowledge is key to enabling the agricultural sector to address its challenges. Automatic term extraction is a Natural Language Processing task that can provide solutions to text tagging and annotation towards better knowledge and information exchange. It is concerned with the identification of terms pertaining to a domain, or area of expertise, in text and is an important step in knowledge base creation and update pipelines. Transformer-based language modeling technologies like BERT have become popular for automatic term extraction, but limited work has been undertaken so far in applying these methods to agriculture. This paper systematically compares Agriculture-BERT to Sci-BERT, RoBERTa, and vanilla BERT, which were fine-tuned for the automatic extraction of agricultural terms from English texts. The greatest challenge faced in our research was the scarcity of agriculture-related gold standard corpora for measuring automatic term extraction performance. Our results show that, with a few exceptions, Agriculture-BERT performs better than the other models considered in our research. Our main contribution and novelty of the presented research is the investigation of the impact that different language model fine-tuning configuration scenarios had on the term extraction task. More specifically, we tested different scenarios related to the model layers kept frozen, or being updated, during training, to measure the impact they may have on Agriculture-BERT's performance in automatic term extraction. Our results show that the best performance was achieved by: (i) the "embedding layer updated + all encoder layers updated" scenario for the identification of terms also seen during training; (ii) the "embedding layer frozen + all encoder layers updated" scenario for the identification of terms being synonyms to those seen during training; and (iii) the "embedding layer updated + top 4 encoder layers updated" scenario for identifying terms neither seen during training nor being synonyms to those seen during training (novel terms). [ABSTRACT FROM AUTHOR]

This study explores the potential of Precision Agriculture (PA) and Smart Farming (SF) using cutting-edge technologies like Artificial Intelligence (AI), the Internet of Things (IoT), and Unmanned Aerial Vehicles (UAVs) to address global challenges such as food shortages and population growth. The research focuses on recent developments in SF, including data collection, analysis, visualization and viable solutions, highlighting the role of IoT and 5G networks. The paper also discusses the application of robots and UAVs in agricultural tasks, showcasing their integration with IoT, AI, Deep Learning (DL), Machine Learning (ML), and wireless communications. Moreover, Smart Decision Support Systems (SDSS) are explored for real-time soil analysis and decision-making. The study underscores the significance of these technologies in PA, propelling traditional farming practices into an era of intelligent and sustainable farming solutions. This Overview is grounded in a thorough analysis of 80 recent research articles, covering the period from 2019 to 2023, within the domain of SF. This study highlights notable trends and advancements in this ever-evolving sector. Furthermore, this paper delves into the nuances of addressing particular challenges prevalent in developing nations, strategies aimed at surmounting constraints related to infrastructure and resource availability, and the pivotal role of governmental and private sector support in fostering the growth of Smart Agriculture (SA). [ABSTRACT FROM AUTHOR]

Digital technologies promise to transform agri-food systems such that they contribute to a more inclusive, economically, and/or environmentally sustainable future. However, without careful attention to the approaches for their introduction, development, and rollout, they are likely to fail to address key aspects of inclusion. Contemporary inclusive innovation mechanisms focus on the involvement of farmers in the innovation processes, but do not elucidate how to operationalize a deeper inclusivity that takes account of the nature of engagement with the farmers and dynamics of the relationships required for successful outcomes. This article analyzes the concept of inclusivity and its operationalization using the case of the Kenya Agricultural Livestock and Research Organization (KALRO). We develop lines of inquiry on how inclusivity can be defined, sought and operationalized in digital agri-food systems. Based on a case study approach, we interviewed 36 stakeholders in the KALRO digital transformation initiative reflecting on literature discussions from secondary data. Primary and secondary data were synthesized to develop lines of inquiry that enabled a deeper exploration of inclusivity. KALRO's rollout and scale-up of digital agriculture technologies for farmers hinges on three evaluative criteria – usability, accessibility, and relevance – which are associated with aspirations for inclusive innovation. Despite these efforts, some technologies they developed included some farmers and excluded others. For the included, inclusion was limited to the accessibility of certain types of information while desired forms of information lacked sufficient mechanisms for access or were not made available. The practice of promoting access, usability and relevance of the technologies while has supported innovation for and with the farmers, there are still challenges in innovating with the farmer. The lines of inquiry "inclusive innovation with" and "inclusive innovation for" introduced in this paper not only help stakeholders gain a deeper, more nuanced and holistic understanding of inclusive innovation, they also reveal the complexity of inclusivity and its operationalization in agri-food systems that are digitalizing. This paper analyzes inclusivity from a practice perspective highlighting areas where the theoretical perspective and empirical observations diverge. The paper makes the case for meaningful changes in how inclusivity is approached, considering the practical needs and preferences of those directly impacted by innovation and the potential of operationalizing 'inclusive innovation with ' as a deeper and more effective approach to inclusive innovation. [Display omitted] • Operationalizing deeper inclusivity in digital technologies can transform agri-food systems. • Using a case study, we develop lines of inquiry for understanding how inclusivity can be understood and sought. • The study reveals the complexity of inclusivity and its operationalization. • Pursuing an approach that focusses on 'innovation for' the farmer has limitations in enhancing inclusivity. • The study identifies benefits of distinguishing and pursuing 'inclusive innovation with' beneficiaries. [ABSTRACT FROM AUTHOR]

• Explain the economic value of the quadratic cost of MECM in terms of marginal cost and elasticity. • Adopt an opinion modification mechanism based on social network. • The MECMs with uncertain adjustment costs are developed under three uncertain scenarios. • The proposed models are applied to the agricultural insurance premiums subsidy policymaking in China. In the realm of group decision making (GDM), the maximum expert consensus model (MECM) emerges as a potent tool for consensus optimization. The complexity of decision-making environment leads to the uncertainty of adjustment costs and the intricate social relationships between decision makers (DMs). Therefore, this paper aims to develop the MECM that integrates both social relationships and uncertain adjustment costs to support social network group decision making (SNGDM) problems. Specifically, we propose a MECM with quadratic cost that can more accurately reflect DMs' sensitivity for opinion adjustment. Additionally, we adopt an opinion modification mechanism based on the information obtained from the social network. The paper also develops the robust MECM (RMECM) to handle the uncertainty of the unit adjustment cost under three uncertain scenarios. Finally, the efficiency of the proposed models is demonstrated by applying them to the agricultural insurance premiums subsidy policymaking in China, further substantiated by sensitivity analysis and comparative analysis showcasing their robust performance. [ABSTRACT FROM AUTHOR]

Nowadays, the Internet of Things (IoT) environments are evolving and becoming popular. The number of devices connected to the Internet continues to raise. IoT is an interrelated network of numerous devices in which data is automatically gathered from the environment by the sensors and transferred over the internet without human support and intervention. The IoT eases individuals interacting with real-world applications over the internet in the IoT environment. Modern innovations in IoT have added computers, sensors, streets, buildings, and even communities to the impression of smartness. IoT appliances function in distinct environments to fulfill several purposes; result in the variety of computational devices and communication technologies employed in healthcare, education, military, agriculture, and commerce. Thus, IoT holds a lot of promise for enhancing social and corporate life. Nevertheless, IoT equipment are a soft target and prone to attacks due substantially to their resource limitations, and the nature of their networks. There are many approaches and technologies utilized to preclude IoT from varied attacks and assaults, Intrusion Detection System (IDS) and Intrusion Preventions System (IPS) are some of them, which can ensure the security, privacy, and reliability of the IoT. In this paper, we provide a deep study of many recent and pertinent IDS/IPS proposed between 2019 and 2022 for IoT networks, giving their key specifics, strengths, shortcomings, and challenges in order to spot the issues that still require to be handled. The paper also lines the mainstream research direction and opens the way for new avenues of research for forthcoming researchers. [ABSTRACT FROM AUTHOR]

This paper discusses how the theory of affordances can be used to investigate how a spectrum of opportunities, benefits, costs and risks is generated and unevenly distributed by different kinds of technology (where 'technology' is understood as techniques, processes and practices of doing and making, rather than technical artefacts and systems). Affordances are possibilities for action, which arise from relations between humans and entities that surround them. This paper discusses three kinds of affordances: material, cultural and socio-economic. The theory of affordances offers a coherent way to explain why different technologies have different implications, and why those implications vary for different stakeholders. Applied to the domain of development-oriented agricultural research and innovation, the theory of affordances could be used by researchers and practitioners to examine the differentiated implications of different kinds of farming technology and alternative programmes of technological change in agriculture, both ex ante (e.g. in their design, development and implementation) and ex post (e.g. in their evaluation). To illustrate the argument, the paper uses the example of weeding in the System of Rice Intensification. Since affordances in theory are generated relationally and situationally for each person, the full array of implications arising from the introduction of new technology could be wide and diverse. A practical challenge, therefore, is whether and how the theory of affordances can be used practically and operationally to design, implement and evaluate the appropriateness, accessibility, utility and value of agricultural technology and technological change for specific people and groups of interest. • The theory of affordances is applied to agricultural technology and innovation. • Affordances are perceived action possibilities, which arise relationally. • Affordances can be studied through material, cultural and socio-economic lenses. • The theory of affordances could be applied to development interventions ex ante and ex post. • Weeding in the System of Rice Intensification is used to illustrate the argument. [ABSTRACT FROM AUTHOR]

Digital transformation in manufacturing necessitates the integration of advanced fields such as robotics, the Internet of Things (IoT), and data science. The latter two, in particular, introduce a unique set of challenges related to data acquisition and processing, especially for emerging manufacturing sectors that lack substantial data volumes for analytical software. This paper specifically addresses the challenges associated with limited data within the context of precision poultry farming. We begin by identifying the specific challenges encountered during the development of a smart poultry management software system. Subsequently, we propose potential solutions for each identified challenge. The outcome of this study is a comprehensive algorithm for data processing that encapsulates these proposed solutions, offering a practical approach to overcoming data limitations in precision poultry farming. [ABSTRACT FROM AUTHOR]

The objective of this paper is to assess the readiness of Italian farms to actively engage in the journey of "Agriculture 4.0" (Agri 4.0). Specifically, the focus lies on analyzing the levels of knowledge and adoption of specific Agri 4.0 enabling solutions, while also considering the underlying business needs and the primary benefits of the paradigm. A descriptive survey was conducted involving 543 respondents associated with agricultural companies of varying sizes. The collected data was analyzed to address four specific research questions. The survey findings indicate that Italian farms are at different stages in their progression towards the Agri 4.0 paradigm, primarily influenced by their revenue and land size. Furthermore, notable similarities exist in both the benefits associated with adopting the Agri 4.0 paradigm and business needs that bring farmers in adopting such technologies. In terms of future research, conducting a similar study in other countries would be beneficial for comparative and benchmark analyses. Despite scholarly discussions regarding technology adoption and the benefits of the Agri 4.0 paradigm are present to the authors' knowledge few empirical surveys have been conducted thus far to evaluate the level of digital solution adoption in agriculture within a specific country. Specifically, this preliminary study follows research conducted two years ago, paving the way for a longitudinal comparison of the two datasets. [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]

Agricultural production has dual functions as a carbon emitter and sink, and fully exploiting the carbon sink function of agriculture is highly valuable for ensuring carbon neutrality. The layout of net carbon in agriculture shows an imbalance, i.e., underscaling in areas with a net carbon advantage or overscaling in areas with a net carbon disadvantage, which restricts the fulfillment of agricultural carbon sinks. Clarifying the global pattern of net carbon imbalances in agriculture and their drivers is highly important for improving the overall net carbon. In this regard, this paper focuses on the global net carbon in agriculture in 155 countries and regions, and a spatial mismatch index of net carbon in agriculture (MNCA) is constructed based on the relative difference between net carbon and cultivated land. Spatial autocorrelation analysis and the standard deviation ellipse are used to reflect its spatiotemporal evolution pattern. In addition, through geographically weighted regression, we explored the effects of agricultural structure, agricultural economic development, the scale of labor and cultivated land and organized the mismatches into carbon and area effects. The results show that (1) global agricultural production represents net carbon emissions, while developing countries represent net carbon sinks, and there is a mismatch of net agricultural carbon in most regions; (2) the MNCA is characterized by low-low clustering in North America, Asia and Europe, with its center of gravity falling in western Africa and migrating northeastward; and (3) the main factors influencing the MNCA are the agricultural structure and economic level. Underscaling in areas with a net carbon advantage is a major problem. To increase the global agricultural carbon sink capacity, we suggest that North America, South Asia and other regions strictly implement agricultural emission reduction policies and that Central America, South America and Africa tentatively upgrade agricultural technology and their economies to alleviate farmland pressure through transnational cooperation. In this paper, the global net carbon imbalance in agriculture is identified and the overall implementation path of increasing carbon sinks is explored to provide a reference for achieving carbon neutrality. This study reports a global mismatch in net carbon in agriculture and identifies the wasted carbon sink advantage of developing countries as the root problem. • It is a new indicator to measure the efficiency of net carbon allocation in the agricultural production process. • Agricultural production in developing countries shows a net carbon sink advantage. • Under-scaling of net carbon advantage is major problem. [ABSTRACT FROM AUTHOR]

• This special issue examines the benefits, limitations and problems of novel smart-farming solutions. • The papers investigate the relations and processes that lie behind the introduction of smart technologies in agriculture. • This introduction focuses on two main aspects of smart farming: interconnectivity and automation. • This collection questions the micro-politics involved in the innovation and diffusion of smart-farming solutions. • The papers study the power dynamics that unfold from the proliferation of smart technologies in farming. [ABSTRACT FROM AUTHOR]

Taking Yantai Cherry as an example, this paper studies the influencing factors of customer satisfaction in online shopping of characteristic agricultural products, and analyzes five influencing factors: pre-purchase expectation, product quality, brand image, e-commerce platform and logistics distribution, and designs a scale, puts forward assumptions and verifies them. The results show that pre-purchase expectation has no significant negative influence on customer satisfaction, product quality, brand image, e-commerce platform and logistics distribution have significant positive effects on customer satisfaction. Finally, based on the research conclusions, this paper puts forward some countermeasures and suggestions on improving customer satisfaction of characteristic agricultural products e-commerce from four aspects, which is of certain significance for increasing sales of specialty agricultural products and promoting the development of agricultural e-commerce. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has fundamentally transformed our interaction with technology by connecting a diverse array of devices and objects through the internet, presenting boundless possibilities in various domains such as healthcare, agriculture, transportation, and industrial automation. However, this interconnected network of smart devices also brings forth complex security and privacy challenges, encompassing the potential for infringements on privacy, data breaches, and unauthorized access. To address these concerns and ensure successful adoption, this paper proposes a novel mechanism that involves an Audit Manager for transparency and accountability. This comprehensive approach aims to safeguard user data, promote trust, and strengthen the security of IoT environments, facilitating their seamless integration into various aspects of our lives. The security analysis of the proposed mechanism has proved it to be secure and reliable. [ABSTRACT FROM AUTHOR]

• Regulating soil pH, etc. can reduce the bioavailability of heavy metals. • Reconstructing appropriate mathematical models for risk assessment is critical. • The application potentials of remediation techniques have been systematically compared. • Heavy metal-polluted soil can be cost-effectively remediated by phytoremediation. • Effective risk assessment can guide the choice of remediation techniques. Controlling heavy metal pollution in agricultural soil has been a significant challenge. These heavy metals seriously threaten the surrounding ecological environment and human health. The effective assessment and remediation of heavy metals in agricultural soils are crucial. These two aspects support each other, forming a close and complete decision-making chain. Therefore, this review systematically summarizes the distribution characteristics of soil heavy metal pollution, the correlation between soil and crop heavy metal contents, the presence pattern and migration and transformation mode of heavy metals in the soil-crop system. The advantages and disadvantages of the risk evaluation tools and models of heavy metal pollution in farmland are further outlined, which provides important guidance for an in-depth understanding of the characteristics of heavy metal pollution in farmland soils and the assessment of the environmental risk. Soil remediation strategies involve multiple physical, chemical, biological and even combined technologies, and this paper compares the potential and effect of the above current remediation technologies in heavy metal polluted farmland soils. Finally, the main problems and possible research directions of future heavy metal risk assessment and remediation technologies in agricultural soils are prospected. This review provides new ideas for effective assessment and selection of remediation technologies based on the characterization of soil heavy metals. [ABSTRACT FROM AUTHOR]

Agriculture must overcome escalating problems in order to feed a growing population while preserving the environment and natural resources. Recently, it has become clear that sensors and the Internet of Things (IoT) are effective tools for boosting agricultural sustainability and food security. This study provides insights into the global market size for smart agriculture in future years from 2021 to 2030, In addition, this research offered four levels of the IoT architecture for smart agriculture: the perception or sensing and actuator layer, the network layer, the cloud layer, and the application layer. The state of the art in IoT and sensor technologies for agriculture is examined in this review paper, along with some of their potential uses, including 1) irrigation monitoring systems, 2) fertilizer administration, 3) crop disease detection, 4) monitoring (yield monitoring, quality monitoring, processing monitoring logistic monotoring), forecasting, and harvesting, 5) climate conditions monitoring, and 6) fire detection. Additionally, this review offers a number of sensors for agriculture that can detect parameters like soil NPK, moisture, nitrate, pH, electrical conductivity, CO2, temperature, humidity, light, weather station, water level, livestock, plant disease, smoke, flame, flexible wearable. Subsequently, this study highlights the advantages of IoT in smart agriculture, including superior efficiency, expansion, reduced resources, cleaner method, agility, and product quality improvement. However, there are still issues that need to be resolved in order for IoT technology to be used in agriculture where covered in this paper, and also provide insights into future research directions and opportunities. This study will contribute to helping future readers and researchers to better understand the state of academic achievement in this subject. [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]

Classification of Indonesian crops is a critical task in developing farming and getting more understanding of agriculture. However, there is no clear task in classifying types of crops in Indonesia. Transfer learning has been used successfully in a variety of image classification applications. Thus, in this paper, we collected images of Indonesian crops from the internet randomly and proposed a classification by using transfer learning of deep learning with four pre-trained models: EffficientNet- B0, ResNet18, VGG19, and AlexNet. In the experiment, augmentation techniques such as random horizontal flip, random vertical flip, and random affine were utilized to prevent the network from overfitting. The result found that EfficientNet-B0 outperformed other models with an accuracy of 82.55. Then, the model struggled to distinguish between crops in the same family. According to the results, although transfer learning can work well to classify images of Indonesian agricultural crops, some improvements are still required to address existing issues. [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]

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]

This study examines how an agtech startup managed to turn a mobile app designed to diagnose plant damages and recommend treatments into an asset. Rooted in assetization research and the sociology of markets, the paper theorizes agtech startups as organizations that increasingly engage in the construction of markets for pesticides and other inputs. Empirically, the study reconstructs the assetization of the app over a period of eight years, showing how the process gradually and quite radically transformed both the app itself and its socio-technical collective. More specifically, the study describes four phases constituting the analyzed segment of the assetization process in question, over the course of which the app evolved from a technology for disseminating knowledge about alternatives to chemical pesticides into a platform for brokering sales of biological and chemical pesticides on behalf of large agrochemical corporations. To make sense of this radical repositioning of the app vis-à-vis chemical pesticides, the article introduces the concept of exploratory assetization—a concept that draws attention to the fact that the socio-technical collectives through which startup-born innovations are turned into assets are not fixed from the outset but emerge only over time through constant confrontations between a given innovation and the various human and non-human actors with which the assetization process puts it in touch. The article concludes that the exploratory assetization process described in it calls into question the ability of venture capital-funded digital agriculture technologies to contribute to pesticide reduction in a long-lasting way. • Agtech startups are involved in the assetization of agri-food systems. • The assetization of agtech is not predefined but an emergent phenomenon. • Given this non-determinacy, on should speak of a process of exploratory assetization. • Agrochemical groups have strong influence on the exploratory assetization of agtech. • Exploratory assetization of agtech perpetuates agricultural problems of past decades. [ABSTRACT FROM AUTHOR]

Agriculture is the major occupation and the main origin of income in India, and most of the population depends on agriculture for their living. The smart farming system's foremost goal is to monitor farmlands by automatically rinsing the lands with appropriate water and a pest detection framework. The conventional agricultural methods lack higher crop productivity with increased human efforts. Hence, the paper proposes an IoT-based solution for smart farming. The proposed system focuses on automatic irrigation of water and plant disease detection. It uses machine learning algorithms to accurately predict adequate water required by the fields and automatic pest identification based on the requirements of the farmland. K-Nearest Neighbour and support vector machine learning algorithms in the pest detection module are used to precisely predict plant diseases. The convenient features from the leaves of the plants were obtained. Then the obtained features are used for classification purposes. Extracting the suitable features and classifying them helps detect whether the plant is pest infected or not. The proposed system monitors, analyses, assesses and controls agricultural fields for the automatic irrigation of the water and disease identification of the plants. The numerical analysis of the machine learning algorithms and the significance of the classification with accuracy are investigated. From the numerical results, it is demonstrated that an accuracy of 84 % is achieved. [ABSTRACT FROM AUTHOR]

Nowadays, we are in the era of advanced technologies where tremendous amount of data is produced by multiple sources such as sensors, devices, social media, user experiences, etc. Furthermore, this raw data has a low value, and major part is not really useful or important for business. One way to give an added value to this stored data is to extract useful knowledge from it, for the ending-system or the end-users by a process commonly called knowledge Discovery in Database (KDD). Smart Farming uses a large amount of connected technologies producing also a huge amount of data in order to maximize productions by reducing: human efforts, environment impact and wasting natural resources. In this paper, We develop a new data analytic architecture dedicated to Precision Livestock Farming (PLF) to improve in particular the livestock animals production, animals' welfare, and farming processes. We present a new data processing architecture for a knowledge-base management system (KBMS) allowing to ease decision support and monitoring operations that can help farmers and stakeholders to better exploit data and have a long-term view of the evolution of the knowledge it contains. Our main contribution in the present paper is a new architecture specifically developed for the precision livestock farming integrating a periodical data reevaluation which address the problematic of data conservation and the decrease in data value over time. [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]

The subject of this paper is the review of advanced technology used in hydraulic systems. The technology in question is termed Independent Metering (IM); this is used in hydraulically driven mobile machinery, such as agricultural, construction, municipal, and forestry vehicles. The idea behind the concept is to modify the connection between the actuator, which could be a cylinder or a motor, and a flow control valve. Traditionally, spool hydraulic valves were used to control the fluid flow into and out of hydraulic actuators. This keeps the meter-in and the meter-out of the actuator mechanically connected due to the construction of these valves. This connection makes the control system blind to pressure changes in one of the hydraulic chambers in the actuator. This, in turn, reduces the overall system controllability. It also increases energy losses, especially under an overrunning load. These two main weaknesses led researchers to break this mechanical connection and get into a new technology with different characteristics. The proposed technology was called Independent Metering. New and more complex control techniques can now be applied to the hydraulic systems using this technology that were not possible before or could be applied to more conventional servo design. This paper reviews Independent Metering (IM) and the technologies used or developed in this field to date. The paper reviews the state of art hydraulic technologies and indicates the links between them and IM. It also reviews the different types of hydraulic valves used when implementing IM. This review also discusses some control algorithms, IM layouts, IM challenges, and identifies where further improvements may be achieved. • Review the hydraulic Independent metering configurations. • Investigate different technologies related to the IM. • Show the main valves embedded into IM and their effects on the performance. • Review the main IM control techniques and Layouts. • Summarize the IM challenges and research trends. [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]

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]

• 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]

Deep learning (DL) is a robust data-analysis and image-processing technique that has shown great promise in the agricultural sector. In this study, 129 papers that are based on DL applications used in agriculture are discussed, categorizing them into five areas: crop yield prediction, plant stress detection, weed and pest detection, disease detection, and smart farming. Smart farming is sub-categorized as water management, seed analysis, and soil analysis. This study highlights the potential of deep learning in enhancing agricultural productivity and promoting economic growth. The study found that supervised learning networks, such as Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), AlexNet, and ResNet, are primarily used in agriculture to enhance economic growth. However, there is a need to develop new DL techniques that can improve model performance and reduce inference time for practical applications. In this review, critical research gaps, particularly in the development of new techniques, are analyzed. This study emphasizes the importance of continued research in this area to fully leverage DL's potential of DL for smart farming and to achieve sustainable agricultural development. • Deep learning is vital in agriculture. • Reviewed 129 agriculture based-DL papers. • DL propels smart farming. [ABSTRACT FROM AUTHOR]

Agricultural systems are currently confronted with more severe environmental and production difficulties with the population increase and global climate change. The fundamental issue is maintaining ecological sustainability while ensuring food production safety. Modern sustainable green agriculture is now achievable with the development of phytonanotechnology, which are applied in plant protection and cultivation while anticipating phytopathogens and enhancing the immune system in advance. Furthermore, the application of phytonanotechnology in genetic engineering for enhancing the nutritional status and resiliency of plants, thus effectively improving crops and increasing agricultural yields. Moreover, the advantages of edible nano-coatings prepared by phytonanotechnology in post-harvest preservation of crops and other aspects are gradually emerging. And the green phytonanotechnology emphasizes the safety and environmental friendliness of the production process and products. This paper comprehensively reviews an overview of the main applications and most recent advancements of phytonanotechnology in agricultural systems. The economic advantages of phytonanomaterials are also investigated to demonstrate the potential for future widespread use of nanotechnology in agriculture. [Display omitted] • Provides a comprehensive overview of recent research advances in the use of phytonanotechnology for agriculture. • The applications of green synthetic nanomaterials in phytopathogen management and crop resilience are introduced. • Summarizes the toxic effects with the application of phytonanotechnology on ecosystems. • Current challenges in the practical application of phytonanotechnology and further perspectives are discussed. [ABSTRACT FROM AUTHOR]

Since the agricultural sector is one of the main drivers responsible for the transgression of planetary boundaries, there is an urgent need to reduce its environmental impacts. To address this issue, international institutions advocate for digital agriculture, but formalised and comprehensive assessment of its environmental impacts is still lacking. This systematic review aims at studying papers dedicated to the environmental assessment of the use of digital technologies in agriculture (DATs), i.e. of digital agriculture. In the 69 studies selected by the PRISMA method, most of the DATs dealt with crop production, supporting technical production management (e.g. fertilisation or irrigation). Almost a third of DATs were robots or automation tools. Out of the selected studies, 36 applied the Life Cycle Assessment (LCA) framework. A focus was placed on these 36 case studies, which were examined in the light of the four LCA standardised stages, i.e. goal and scope, life cycle inventory (LCI), life cycle impact assessment (LCIA) and interpretation. Our review results show that most of the studies aimed to assess the environmental impacts of digitalising an agricultural system using either a mass or surface-based functional unit, or sometimes both. The system boundaries seldomly included the DAT life cycle which were sometimes modelled with proxies and assumptions at the LCI stage to address data gaps. Assumptions were also needed to quantify the effects of DAT on the agricultural system, such as yield benefits or food waste reduction. At the LCIA stage, midpoint indicators were often selected. Turning to the interpretation step, it is worth noting that few studies carried out uncertainty and sensitivity analysis. Finally, most of these 36 studies concluded that digitalisation reduced the environmental impacts of the agricultural system due to efficiency gains, or the use of electricity instead of fossil fuel. Conversely, three studies determined that digitalisation increased the environmental impacts of their agricultural system in terms of toxicity, mineral resource use, marine eutrophication, land use and ionising radiations, as high technologies (robots and drones) were being assessed in these works. Based on our review, recommendations were made on how to conduct LCA when assessing the environmental impacts of digitalising an agricultural system. Research perspectives for DAT environmental assessment were also discussed that take into consideration their emerging nature, their multifunctionality and the rebound effects they may generate. [Display omitted] • It is the first systematic review of LCA of digital agriculture technologies (DATs). • Precision agriculture DATs for crop management were the most assessed. • DAT life cycle and DATs effects on agricultural systems should be considered in LCA. • Life cycle inventory of DATs faces a lack of data. • DATs multifunctionality remain a challenge for LCA. [ABSTRACT FROM AUTHOR]

• Research status of 3D reconstruction technology in agriculture was summarized. • Sensors, systems and methods used in 3D reconstruction were summarized. • The applications of 3D reconstruction methods in agriculture were summarized. • Challenges and future trends of 3D reconstruction in agriculture were reported. Perceiving the shape and structure of the real three-dimensional world through sensors and cameras is indispensable across various domains. The 3D reconstruction technology is dedicated to realizing this ideal process. 3D reconstruction technology serves as a transformative tool, enriching our ability to perceive the genuine shape and stereo structure of objects and scenes in the real world. Through combining advanced sensors, image processing algorithms and 3D reconstruction methods, it captures the shape and structural information of targets from multiple perspectives and dimensions, and creates highly realistic 3D models in the virtual environment. With the rapid modernization of agriculture and ongoing technological progress, the demand for more efficient and precise management and monitoring methods in agricultural production is increasing. Traditional observation and measurement methods face challenges such as low efficiency and incomplete data. 3D reconstruction technology provides more accurate and intelligent management tools for smart agriculture. This paper provides a detailed introduction to the research progress based on 3D reconstruction technology in smart agriculture. It delves into the characteristics and development of various sensors and sensing systems, discussing various methods to implement 3D reconstruction technology. Different from applications in industrial environments, agricultural environments and crops are usually complex and variable, and consideration of diverse factors is required for the selection of suitable sensors and reconstruction methods. Therefore, several aspects of applications are summarized, such as agricultural robotics, crop phenotyping, livestock, and the food industry. Finally, the challenges and potential future trends of 3D reconstruction in agriculture are given. [ABSTRACT FROM AUTHOR]

• A new method for nutrient optimization of hydroponic lettuce based on improved algorithm. • A multi-strategy-based improved Grey Wolf Optimization algorithm (OSGWO) is proposed. • It provides more customized nutrient management basis for hydroponic lettuce at different growth stages. Hydroponic agriculture, as a modern agricultural technology, not only enables crops to better realize their growth potential but also frees them from the traditional agricultural constraints of land resources. Currently, the nutrient solutions for hydroponic crops are mostly prepared using generic formulas, lacking precise ratios based on the specific needs of different crop types and growth stages. It is of great significance to study the precise nutrient requirements of specific crops at different growth stages to enhance yield. To this end, a multi-strategy improved Grey Wolf Optimization algorithm is proposed in this paper to optimize the fertilizer model for hydroponic lettuce at different growth stages, providing more accurate nutrient requirements for hydroponic lettuce at different growth stages. Firstly, to solve the problems of the traditional GWO algorithm, such as insufficient exploration and the tendency to fall into local optima, non-linear functions, Symbiotic Organisms Search (SOS), and Opposition-Based Learning (OBL) were integrated into the GWO algorithm. Secondly, the objective was to minimize the residual function of the nitrogen, potassium, and calcium ternary fertilizer effect equation, the proposed algorithm was used to optimize the coefficients of the equation. Lastly, the optimal concentrations of NO 3 −, K+, and Ca2+ for each growth stage of lettuce were calculated based on the optimized fertilizer effect equations. The proposed algorithm was compared with five other excellent metaheuristic algorithms in optimizing fertilizer model coefficients. Experimental results indicate that the proposed algorithm outperforms the other algorithms used in the experiments. For the nutrient models of three growth stages, the proposed algorithm achieves improvements compared to the GWO algorithm, with R2 increasing by 0.73 %, 0.66 %, and 0.51 %, and RMSE decreasing by 8.53 %, 5.70 %, and 3.83 % respectively. This contributes to providing more accurate nutrient requirements for hydroponic lettuce. [ABSTRACT FROM AUTHOR]

Controlled release technology of chemical fertilizers is an important cornerstone for the efficient development of sustainable agriculture. Damage to the coating layer and fragmentation of particles during the application of controlled release fertilizers can lead to the loss of their controlled release properties, making the development of effective damage reduction operations a key focus in the study of such agricultural materials. Therefore, this paper employs the bonded particle model (BPM) method within the discrete element method (DEM) framework to investigate the mechanism of coating damage in controlled-release fertilizers. It establishes a classification of damage types based on the level of damaged particles as an effective analytical approach. Furthermore, a comparative analysis is conducted between the discrete element particle model (DEPM) method and the bonded particle model method in terms of their performance in discharge mechanics and flow characteristics. The results indicate that the breakage rate of fertilizer discharge is influenced by both the formation of a steady state and the depletion of the fertilizer load within the container, with an average breakage rate of 1.511% over the total period. Differences in the average maximum compression force and mass flow rate between the discrete element particle model group and the bonded particle model group were 60.355 N and 1.998 g/s, respectively, reflecting limitations imposed by the fertilizer models on expected damage deformation behaviors. This study elucidates the process and potential impacts of fertilizer particle damage and deformation, revealing the strengths and limitations of two simulation model approaches under different application conditions, providing insights for sustainable agricultural production and technological innovation in fertilization equipment. [Display omitted] • Construct of model using the reciprocating piston mechanical stacking method. • Breakage rate is affected by stable state formation and fertilizer consumption. • Particle quantity gradation forms the criterion for categorizing types of damage. • Damage types reflect overall trends, predominantly partial and missing types. • The presentation of damage deformations is constrained by the fertilizer model. [ABSTRACT FROM AUTHOR]

Agricultural innovations are inclusive when they are used by any member of society who wants to use them. Conversely, agricultural innovations that can only be used by a specific privileged group within society can be characterized as "exclusive". The first objective of this paper is to examine the inclusivity of agricultural innovations in Ethiopia, using national representative data and considering a wide portfolio of innovations resulting from the collaborative research between CGIAR and its national partners. Second, we also examine how measurement error may affect how we characterize the inclusivity of agricultural innovations. We use nationally-representative survey data from Ethiopia (collected in 2018/19) in which best-practice measures of the adoption of a large number of agricultural innovations were embedded, including the adoption of CGIAR-related improved maize varieties measured using two different approaches: subjective, self-reported survey data; and objective DNA fingerprinting of crop samples taken from the same farmers' plots. A rich set of household variables is also collected in the survey, which allows characterizing the types of farmers that are adopting different innovations, and the extent to which conclusions regarding the inclusivity of innovations depends on the measurement of the latter. Many innovations are not disproportionately more likely to be adopted by male, larger, richer, or more connected farmers. When using self-reported data on adoption of improved maize varieties, adoption appears positively correlated with having larger landholdings and households with lower female participation in agriculture, and negatively correlated with poorer households (being among the bottom 40% of consumption distribution). Substituting survey responses with the results of DNA fingerprinting these correlations disappear, with farm size, gender and poverty status no longer predictive of adoption. The results suggest the potential value of offering a menu of innovations to farmers to increase inclusivity, as it allows each farmer to be a critical consumer of potential innovations and select those that best correspond to their own needs and constraints. We also highlight how important data quality is in ensuring we have correct information about inclusive innovation. [Display omitted] • Nationally representative data on adoption of agricultural innovations for Ethiopia. • Examines importance of representative samples and valid measurement in shaping understanding of inclusive innovation. • Patterns of inclusiveness are innovation-specific and can be subject to measurement error. • Objective measures (e.g. DNA fingerprinting for crop variety adoption) can provided unbiased picture of inclusive innovation. [ABSTRACT FROM AUTHOR]