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Sustaining life on the Earth with its ever-growing population is forcing changes in people’s way of life, industrial and agricultural production, exploitation of energy resources, and approaches to ecology. We face continual growth in the world’s population, and the demand for materials is growing even more rapidly. Every manufacturing and consumer sector is looking for ways to save energy and materials, attempting to minimize their negative impacts on the environment. In the pulp and paper industry, one of the segments in which progress can be made and help protect the planet is to reduce the brightness of paper. Such a reduction would lead to a lowering in the energy and material costs associated with paper production. [ABSTRACT FROM AUTHOR]

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]

The paper shows how the agri-sector is influenced by the emergence and development of the digitisation process. This is presented using a bibliometric analysis. The importance of studying and understanding the current challenges facing the agricultural sector including the emergence and development of the digitisation process of the sector is the main aim of this paper. All these challenges are felt by both the agricultural sector and the economy as a whole. This paper contributes to the literature. This is due to the bibliometric analysis through which the most important aspects of the challenges facing the entire agricultural sector today are observed. Among the current challenges facing the agricultural sector, climate change is among the most important. The results of the bibliometric analysis show that the keywords searched and their clustering confirm that the emergence and rapid development of digitisation brings both positive and negative effects to the whole agricultural sector. Following the search of Web of Science and Scopus databases, 203 papers were identified in Web of Science and Scopus using the phrase "the impact of digitization on agriculture". The bilbiometric analysis comprises several steps, including the analysis of the years of emergence of studies on the emergence of the digitisation process in agriculture. Thus, within this stage, the analysis of the years, it is signalled the transition from some aspects that concern the technological side to other economic and even human aspects. On the whole, there is a shift in research topics towards studying how the emergence and development of the digitisation process brings both positive and negative influences between the agricultural sector and society. [ABSTRACT FROM AUTHOR]

This paper elaborates on a digital simulation study on supernormal particle flow used to investigate and analyze the process of picking up jujube branches, which was a meaningful attempt to search for accurate and effective advanced numerical analogy methods in the agricultural field. In this paper, the meshless technology based on the element-free Galerkin method was used for the first time to present the effects of particle size, particle number and particle acting force on the movement of irregular particles, and the influence of the gear rotation speed, the feeding amount, and the jujube branch size on the movement behavior as well as the picking rate. It can describe not only the particles' dynamic movement in the process of picking up jujube twigs, such as feeding, collision, throwing and rolling, but also the effect of the quality and shape caused by the particle size, which in turn affects the surface force of particles and interparticle acting force, thereby affecting the weight function in the analytical solution, the total feeding amount and the effect of the acting force resulting from the particles' contact, roll and collision caused by gear rotation. The findings reveal that the digital simulation, based on the meshless Galerkin technology and Rocky software, is effective in dealing with issues related to supernormal particle flow. By eliminating the influence of geometric shapes on calculations, the method boasts an effective solution to the movement problems of irregularly shaped particles, which would be further applied in the agriculture field. [ABSTRACT FROM AUTHOR]

This article reports the first occurrence of Sordaria macrospora causing collar rot of peanut in Shandong province, China. The study describes the symptoms observed on peanut plants, the isolation and identification of the fungus, and the pathogenicity testing conducted. The research was supported by various funding sources, and the sequence data has been deposited in GenBank. This finding expands our knowledge of the distribution and impact of S. macrospora on peanut crops in China. [Extracted from the article]

The Virtuous Incubation System is a solution aimed at transforming poultry farming by integrating Internet of Things (IoT) technology into the incubation process. This paper seeks to improve the efficiency and success rate of poultry egg incubation while minimizing manual intervention, reducing resource consumption, and ensuring optimal conditions for embryo development by providing a smart incubator system. The smart incubator addresses the challenges of traditional manual monitoring and adjustments by leveraging IoT devices, sensors, and data analytics to create an automated and intelligent incubation system. It provides energy efficiency by incorporating energy-efficient components to minimize power consumption, thereby reducing operational costs for farmers, and by providing notifications and alerts where critical deviations in environmental conditions or equipment malfunction, enabling proactive intervention to prevent losses. A cross-section of small- to medium-scale poultry producers, particularly those engaged in hatching chicks, were interviewed to highlight the main challenges regarding the business of hatching chicks. It was learned through the interviews that chick production on a small to medium scale was relatively expensive due to the labor employed to monitor the production and losses due to adverse conditions resulting in a poor success rate in hatching the chicks. Thus, the implementation of the virtuous incubation system comes in to solve the main challenges being faced by the local poultry producers. [ABSTRACT FROM AUTHOR]

This paper mainly aims to identify the key factors that should be included in building of smart cities in a proper efficient way. Due to rapid increase in urban population, it should be responsible to provide a better way in building of a smart city. Smart city is an idea of providing urban development that includes social, financial, ecological parts of a society. A smart and sustainable city has goals to be achieved in an adaptable, reliable, scalable, accessible, and in resilient way which will be discussed in the methodology section. This paper mainly focuses on some important factors in improving sustainability of smart cities such as climate and environmental issues and governance authorities for improving quality of life of citizens. Also, this paper explains 3E's which are environment, economy and equity are plays a crucial role in rapid development of smart cities. This research is focused on discussing various smart technologies recently used in India and the challenges involved in them. Recent literature on smart city development is reviewed, and the challenges that prevailed in recent technologies are analysed. This paper includes how Internet of Things (IoT), Connectivity, Cloud computing and AI etc., should be effectively used for developing a smart city. This paper deals with the requirements to build smart cities in India – regarding various aspects like available resources, lifestyle, transport management system, smart healthcare system, smart waste management, smart Sanitation, smart railways, smart agriculture, home automation, smart energy consumption etc. This paper also discusses 'smart living,"smart infrastructure,"smart governance,"smart economy,' and other various systems which are need for Smart India in particular. When adopted with proper planning, all the above catalysing with smart services and solutions, build a sustainable city. [ABSTRACT FROM AUTHOR]

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

• 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]

3D printing or additive manufacturing process is known as part of the digital manufacturing process, involves gradually adding materials to a geometric representation to manufacture physical objects. A quickly developing technology is 3D printing. The world now uses 3D printing extensively in the many fields such as automotive, locomotive, healthcare, agriculture mass customization, aviation sectors and manufacture of any form of open source designs using 3D printing technology are becoming more common. 3D printing process produce an object directly from a computer-aided design model by depositing material layer by layer. In this world mostly many countries are quickly advancing and heavily investing in 3D printing. The advancement of 3D printing technology in Pakistan will be accelerated by strong government assistance, both in the form of laws that are welcoming to investors and subsidies during the early stages of growth. The use of this technology will bear fruit with a few years. Economy will boost as a result of strong manufacturing base. Pakistan will be able to contribute to the international market with skilled professionals and complex products, thus paving way for a sustainable future. The varieties of 3D printing technologies, the materials utilised in 3D printing, the applications for 3D printing technology, and the difficulties associated with adopting 3D printing technologies in Pakistan are all covered in this article. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative IoT-enabled crop storage monitoring system designed to revolutionize the preservation and quality assurance of stored agricultural produce. Central to this system are IoT sensors strategically deployed within storage facilities. These sensors are tasked with the continuous tracking and monitoring of crucial environmental parameters, specifically temperature, humidity, and gas levels. Leveraging this real-time data, the system is engineered to promptly detect and respond to any deviations from the prescribed optimal storage conditions. The core strength of this system lies in its ability to generate instantaneous alerts upon detecting irregularities. These alerts serve as preemptive measures, effectively averting potential spoilage and curtailing post-harvest losses by enabling timely interventions and corrective actions. By harnessing IoT technology, this paper aims to create a proactive, automated, and responsive framework that ensures the integrity and safety of stored agricultural produce, ultimately contributing to enhanced food security and sustainability. [ABSTRACT FROM AUTHOR]

Oyster Farming is one of important fisheries and aquaculture industries in Taiwan. Each year, approximately 4000–5000 tons of discarded bamboo scaffolding (BS) used in oyster farming, are generated, so the treatment and utilization of BS should be taken seriously. This study evaluates the suitability of BS for pulp and papermaking by assessing the chemical compositions, microstructural, and fiber morphology. The pulping properties is investigated by soda pulping. The chemical composition of BS shows the potential for application in pulping. The BS microstructure shows that can enhance pulping reactions, while the fiber morphology indicates the possibility of producing high-strength paper. Through the pulping experiment, it demonstrated that BS is suitable for pulping with lower NaOH dosage and longer digestion time. The condition at 170 °C with 14% NaOH dosage for 90 min digestion has the highest yield. After refining the highest pulping yield BS pulp, it can improve the handsheet strength and bulk of the OCC-BS mixed pulp, which can achieve the strength property required for industrial paper. In summary, BS exhibits the potential for pulping application and produces a better paper strength than OCC pulp, exhibiting the feasibility of enhancing the circular utilization value of BS in Taiwan. [ABSTRACT FROM AUTHOR]

Corn crop row recognition during different growth stages is a major difficulty faced by the current development of visual navigation technology for agricultural robots. In order to solve this problem, an algorithm for recognizing corn crop rows during different growth stages is presented based on the ST-YOLOv8s network. Firstly, a dataset of corn crop rows during different growth stages, including the seedling stage and mid-growth stage, is constructed in this paper; secondly, an improved YOLOv8s network, in which the backbone network is replaced by the swin transformer (ST), is proposed in this paper for detecting corn crop row segments; after that, an improved supergreen method is introduced in this paper, and the segmentation of crop rows and background within the detection frame is achieved utilizing the enhanced method; finally, the corn crop row lines are identified using the proposed local–global detection method, which detects the local crop rows first, and then detects the global crop rows. The corn crop row segment detection experiments show that the mean average precision (MAP) of the ST-YOLOv8s network during different growth stages increases by 7.34%, 11.92%, and 4.03% on average compared to the MAP of YOLOv5s, YOLOv7, and YOLOv8s networks, respectively, indicating that the ST-YOLOv8s network has a better crop row segment detection effect compared to the comparison networks. Corn crop row line detection experiments show that the accuracy of the local–global detection method proposed in this paper is improved by 17.38%, 10.47%, and 5.99%, respectively, compared with the accuracy of the comparison method; the average angle error is reduced by 3.78°, 1.61°, and 0.7°, respectively, compared with the average angle error of the comparison method; and the average fitting time is reduced by 5.30 ms, 18 ms, and 33.77 ms, respectively, compared with the average fitting time of the comparison method, indicating that the local–global detection method has a better crop row line detection effect compared to the comparison method. In summary, the corn crop row recognition algorithm proposed in this paper can well accomplish the task of corn crop row recognition during different growth stages and contribute to the development of crop row detection technology. [ABSTRACT FROM AUTHOR]

Green is the foundation of agricultural development. By leveraging modern information technology, digital village construction injects new momentum into the green growth of agriculture. Using balanced panel data from 31 provinces in China from 2011 to 2022, this paper employs the entropy approach and SBM-GML index to measure the indicators of digital village construction and agricultural green total factor productivity (AGTFP). The impacts and mechanisms of digital village construction on AGTFP are examined through fixed effect, mediating effect, and threshold models. The findings are as follows: (1) AGTFP in China shows an overall increasing trend from 2011 to 2022, with significant growth in the western region compared to the eastern and central regions. (2) Digital village construction significantly accelerates the improvement of AGTFP, a conclusion supported by robustness tests. (3) Mechanism analysis indicates that digital village construction advances AGTFP by fostering agricultural technology innovation, enhancing agricultural human capital, and improving agricultural productive services. (4) Digital village construction has a more significant impact on major grain-producing areas, economically underdeveloped areas, and northern region compared to other regions. (5) Environmental regulation acts as a threshold effect on the impact of digital village construction on AGTFP. Digital village construction substantially impacts AGTFP when environmental regulation is below the threshold, with the positive effect continuing beyond this point but at a reduced intensity. [ABSTRACT FROM AUTHOR]

This document is an introduction to a special issue of the journal "Plants" on the topic of "The Application of Spectral Techniques in Agriculture and Forestry." The special issue includes twelve research papers that explore various applications of spectral analysis in agricultural and forestry environments. The papers cover topics such as crop health monitoring, disease detection, forest parameter estimation, soil quality assessment, water stress analysis, and nutrient management. The studies highlight the use of advanced spectral indices, remote sensing techniques, and machine learning algorithms to monitor plant physiological parameters and improve resource management in agricultural ecosystems. The research presented in this special issue demonstrates the broad applicability of spectral technologies in different geographical regions and crops, emphasizing the efforts of scientists from multiple countries to promote precision agriculture and forestry practices. [Extracted from the article]

The agricultural sector is undergoing a transformative paradigm shift with the integration of advanced technologies, particularly artificial intelligence (AI), to enhance data analysis techniques and streamline decision-making processes. This paper delves into the integration of advanced technologies in agriculture, focusing specifically on optimizing data analysis through artificial intelligence (AI) to strengthen decision-making processes in farming. We present a novel AI-powered model that leverages historical agricultural datasets, utilizing a comprehensive array of established machine learning algorithms to enhance the prediction and classification of agricultural data. This work provides tailored algorithm recommendations, bypassing the need to deploy and fine-tune numerous algorithms. We approximate the accuracy of suitable algorithms, highlighting those with the highest precision, thus saving time by leveraging pre-trained AI models on historical agricultural data. Our method involves three phases: collecting diverse agricultural datasets, applying multiple classifiers, and documenting their accuracy. This information is stored in a CSV file, which is then used by AI classifiers to predict the accuracy of new, unseen datasets. By evaluating feature information and various data segmentations, we recommend the configuration that achieves the highest accuracy. This approach eliminates the need for exhaustive algorithm reruns, relying on pre-trained models to estimate outcomes based on dataset characteristics. Our experimentation spans various configurations, including different training–testing splits and feature sets across multiple dataset sizes, meticulously evaluated through key performance metrics such as accuracy, precision, recall, and F-measure. The experimental results underscore the efficiency of our model, with significant improvements in predictive accuracy and resource utilization, demonstrated through comparative performance analysis against traditional methods. This paper highlights the superiority of the proposed model in its ability to systematically determine the most effective algorithm for specific agricultural data types, thus optimizing computational resources and improving the scalability of smart farming solutions. The results reveal that the proposed system can accurately predict a near-optimal machine learning algorithm and data structure for crop data with an accuracy of 89.38%, 87.61%, and 84.27% for decision tree, random forest, and random tree algorithms, respectively. [ABSTRACT FROM AUTHOR]

This paper will examine the social and ethical impacts of using artificial intelligence (AI) in the agricultural sector. It will identify what are some of the most prevalent challenges and impacts identified in the literature, how this correlates with those discussed in the domain of AI ethics, and are being implemented into AI ethics guidelines. This will be achieved by examining published articles and conference proceedings that focus on societal or ethical impacts of AI in the agri-food sector, through a thematic analysis of the literature. The thematic analysis will be divided based on the classifications outlined through 11 overarching principles, from an established lexicon (transparency, justice and fairness, non-maleficence, responsibility, privacy, beneficence, freedom and autonomy, trust, dignity, sustainability, and solidarity). While research on AI agriculture is still relatively new, this paper aims to map the debate and illustrate what the literature says in the context of social and ethical impacts. It aim is to analyse these impacts, based on these 11 principles. This research will contrast which impacts are not being discussed in agricultural AI and which issues are not being discussed in AI ethics guidelines, but which are discussed in relation to agricultural AI. The aim of this is to identify gaps within the agricultural literature, and gaps in AI ethics guidelines, that may need to be addressed. [ABSTRACT FROM AUTHOR]

The article discusses the growing interest in establishing smart cities based on the Internet of Things (IoT) and the challenges they face, particularly in terms of security. The article highlights a special issue of the IETE Journal of Research that focuses on IoT, sensor networks, and blockchain technology for smart cities and societies. The special issue contains 10 full papers that cover various topics such as urban land use change detection, air quality data imputation, steganographic approaches for secured communication, and agricultural applications using blockchain. The article expresses gratitude to the organizers and contributors of the special issue and hopes that it will be a valuable resource for researchers and engineers. [Extracted from the article]

[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]

This special issue features 14 new research papers investigating the role of farmers' organizations (e.g., collective action, self‐help groups, producer companies/organizations, and cooperatives) in supporting sustainable development. The key findings include: (1) farmer groups and cooperatives promote farmers' adoption of good farm management practices, new agricultural technologies and sustainable farming practices, although not substantially improving farm yield; (2) outsourcing services provided by agricultural cooperatives help to increase the technical efficiency of crop production; (3) cooperative membership enhances members' bargaining power and enables them to sell their products at higher prices; (4) cooperatives motivate rural laborers to work in off‐farm sectors, while self‐help groups empower rural women in decision‐making; (5) internet use improves agricultural cooperatives' economic, social, and innovative performances; (6) direct administrative intervention supporting cooperative development may lead to the emergence of shell cooperatives; (7) participation in forest farmer organizations enables wood value chain upgrading; (8) increasing the cooperative size in terms of income, equity, and assets increases the profitability of savings and credit cooperatives; and (9) creating cross‐border cooperation between cooperatives generates benefits for all parties involved. These findings can inspire the design of policies aimed to support farmers' organizations in achieving sustainable development goals. [ABSTRACT FROM AUTHOR]

Avocado is one of the most commercialized and profitable fruits in the international market. Its cultivation and production are centered in countries characterized by tropical and subtropical climatic conditions, many of them with emerging economies. Moreover, the use of technology is key to agricultural production improvement strategies. Using avocado crop data to forecast the potential impacts of biotic and abiotic factors, combined with smart farming technologies, growers can apply measures during a single production phase to reduce the risks caused by pests and weather variations. Therefore, this paper aims to distinguish the most relevant variables related to agroclimatic and phytosanitary events in avocado crops, their incidence on production and risk management, as well as the emerging technologies used for the identification and analysis of pests and diseases in avocados. A scientific literature search was performed, and the first search found 608 studies, and once the screening process was applied, 37 papers were included in this review. In the results, three research questions were answered that described the pests and diseases with high impact on avocado production, along with the data sources and the principal enabling technologies used in the identification of agroclimatic and phytosanitary events in avocados. Some challenges and trends in the parameterization of the technology in field conditions for data collection are also highlighted. [ABSTRACT FROM AUTHOR]

Background: The adoption of new agricultural technologies is one of the key factors for achieving sustainable agricultural development. With the rapid development of China's economy and the widening gap between urban and rural areas, it has become the norm for farmers to seek employment outside their home villages. This phenomenon not only changes the lifestyle of farmers but may also affect their attitudes toward and adoption behavior of new agricultural technologies. However, existing research in this field is not yet sufficient. This paper aims to assess the overall impact, heterogeneous effects, and mechanisms of action of migrant work experience on farmers' willingness to adopt new agricultural technologies. Methods: Based on the 8,391 sample data from 243 counties in the 2018 China Labor Force Dynamic Survey (CLDS2018), this paper uses the Linear Probability Model (LPM), Two Stage Least Square (2SLS), Conditional Mixed Process (CMP) methods, and omitted variable test to estimate the impact of migrant work experience on farmers' willingness to adopt new agricultural technologies. Simultaneously, it explored the heterogeneity in the relationship between migrant work experience and the willingness to adopt new agricultural technologies from the perspectives of gender, age, and educational level. Lastly, the Karlson-Holm-Breen (KHB) model was utilized to analyze the mechanisms through which migrant work experience affects farmers' willingness to adopt new agricultural technologies. Results: Migrant work experience significantly enhances farmers' willingness to adopt new agricultural technologies, with the probability of willingness among farmers with migrant work experience being 26.65% higher than that of farmers without such experience. At the same time, compared to female farmers, those born before 1980, and those with primary education or less, this enhancing effect is more pronounced among male farmers, those born after 1980, and those with more than a primary education. Furthermore, it was found that migrant work experience not only directly enhances farmers' willingness to adopt new agricultural technologies but also indirectly promotes it by increasing capital accumulation, enhancing risk awareness, expanding social networks, and strengthening agricultural cognition, with risk awareness and agricultural cognition playing a larger indirect role. Conclusion: Based on the empirical results, this paper suggests actively guiding farmers with migrant work experience to adopt new agricultural technologies, and encouraging return migrant labor with the intention of resettling in rural areas to engage in agriculture again, to further promote the adoption of new agricultural technologies. [ABSTRACT FROM AUTHOR]

Modern and precision agriculture is constantly evolving, and the use of technology has become a critical factor in improving crop yields and protecting plants from harmful insects and pests. The use of neural networks is emerging as a new trend in modern agriculture that enables machines to learn and recognize patterns in data. In recent years, researchers and industry experts have been exploring the use of neural networks for detecting harmful insects and pests in crops, allowing farmers to act and mitigate damage. This paper provides an overview of new trends in modern agriculture for harmful insect and pest detection using neural networks. Using a systematic review, the benefits and challenges of this technology are highlighted, as well as various techniques being taken by researchers to improve its effectiveness. Specifically, the review focuses on the use of an ensemble of neural networks, pest databases, modern software, and innovative modified architectures for pest detection. The review is based on the analysis of multiple research papers published between 2015 and 2022, with the analysis of the new trends conducted between 2020 and 2022. The study concludes by emphasizing the significance of ongoing research and development of neural network-based pest detection systems to maintain sustainable and efficient agricultural production. [ABSTRACT FROM AUTHOR]

This is the full article based on a conference paper. Pipe robots are used in agricultural engineering for transportation of materials. Also, they are used for cleaning the internal surfaces of the pipes. In the process of numerical investigation of dynamics of a pipe robot, a specific model for viscous friction is used. This influences the results of numerical calculations of dynamics of a pipe robot. A numerical procedure for more precise calculation of viscous friction is proposed in the paper. Results of investigations for two values of time steps are obtained and presented: without the proposed procedure and with it. Advantages of the improved calculation of viscous friction are indicated. [ABSTRACT FROM AUTHOR]

Copyright of Plants, People, Planet is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Comprehensive accounting of carbon emissions and carbon sinks in the agricultural sector is crucial for China to achieve its carbon neutrality goal as early as possible. This paper develops a comprehensive and scientific accounting system to recalculate China's agriculture sector's carbon emissions and sinks from 1995 to 2020, taking into account both resource inputs and productive activities. Subsequently, the STIRPAT model is employed to predict alterations in carbon emissions and sinks across different scenarios. The results show that energy consumption, chemical inputs, and farmland soil management have surpassed livestock and poultry breeding as the main contributors to agricultural carbon emissions. Furthermore, this paper classifies 31 provinces in China into five distinct types based on the variations in agricultural carbon emissions and carbon sinks. These types include carbon sink-dominated regions, paddy planting-dominated regions, livestock farming-dominated regions, resource inputs-dominated regions, and composite factor-dominated regions. In addition, the extent of agricultural technology and the magnitude of agricultural development are the key factors impacting China's agricultural carbon emissions and carbon sinks, respectively. Prior to 2045, agricultural carbon emissions must be directly reduced as much as possible, and their source must be controlled; following that year, the role of carbon sequestration will become more prominent, and the active development of agricultural carbon sinks will be more beneficial in achieving agricultural carbon neutrality. [ABSTRACT FROM AUTHOR]

Based on the monthly panel data of China's imports of agricultural machinery products from 2016–2022, this paper uses a multi-period double-difference model to assess the impact of China's imposition of counter-tariffs on China's imports of agricultural machinery in the context of U.S.–China trade friction. It is found that China's implementation of counter-tariffs significantly reduces China's imports of agricultural machinery products from the U.S. and significantly increases imports from 16 other countries, but the trade diversion effect is lower than the trade suppression effect. Mechanism analysis finds that China–U.S. trade friction affects the technological innovation capacity of agricultural machinery enterprises and the degree of uncertainty in the Chinese economy, which in turn affects China's agricultural machinery imports. Heterogeneity analysis finds that China–U.S. trade friction has a more significant inhibitory effect on seeding and planting and fertilizing machinery, drainage, irrigation, and water lifting machinery, and other machinery imported from the U.S., and a more significant diversionary effect on agricultural primary processing machinery and harvesting machinery imported from 16 other countries. The imposition of countervailing tariffs mainly affected imports of complete machinery products rather than machinery spare parts. [ABSTRACT FROM AUTHOR]

Purpose: This case study is based on the Women in Agri-Tech programme, a teacher professional development programme that aimed to build teachers' capabilities to use Agri-Tech in their agriculture/STEM classes to increase student awareness of agriculture technology and its associated career paths. Teachers and agriculture industry partners co-created Agri-Tech modules, which were implemented and evaluated from teachers' and students' perspectives. This paper demonstrates how work-related learning that emphasises technology can increase the visibility of career pathways and how multi-stakeholder benefits can evolve from teacher–industry partnerships. Design/methodology/approach: An action research design using quantitative and qualitative methods was used to construct this case study. Data collection methods included surveys, interviews, peer evaluation and teacher reflections. Findings: Teacher professional development that incorporates teacher-industry partnerships can increase teachers' self-efficacy and build confidence to support authentic work-related learning in their classes. Integrating technology into agriculture/STEM classes can increase the visibility of agriculture career paths for students, which is critical given the serious skills shortage in this sector. Research limitations/implications: This study is limited in that the Women in Agri-Tech professional development programme was one standalone programme tailored specifically for women agriculture/STEM teachers through a competitive process. However, the beneficial implications of such programmes that support teacher–industry engagement have far-reaching benefits. Teacher professional development programmes that provide opportunities to partner with industry can support improvements in integrating career-aligned learning into the curriculum and can specifically address industry skills and knowledge gaps by addressing school-based learning requirements for the future workforce. Originality/value: This paper contributes to the literature on education–industry partnerships and considers how teachers can contribute to an early intervention sector workforce development strategy for future industry sustainability. [ABSTRACT FROM AUTHOR]

The income effect of rice–crayfish co-culture technology (RCT) is directly related to rate of adoption of farmers and the process of China's green development of agriculture. The aim of this study is to explore the income effect and income growth mechanism of rice–crayfish co-culture technology from the perspective of continuous adoption. With the treatment effect model (TEM), this paper empirically analyzes the income effect and income-generating mechanisms of RCT using field survey data from 736 farmers in the Jianghan Plain. As a result of this study, it was discovered that RCT will increase farmers' net agricultural income by RMB 83,430 if they continue to adopt it. Further examinations indicate that the optimal adoption period for RCT is four and a half years. Additionally, it has also been shown that non-farm employment positively moderates the relationship between continuous adoption of RCT and net agricultural income. Farmers who participate in non-farm employment and continue to adopt the RCT will experience an increase in net agricultural income by RMB 104,510. Therefore, our results suggest that it is necessary to encourage farmers to continuously adopt RCT and actively participate in non-farm employment to enhance the income effect of RCT. [ABSTRACT FROM AUTHOR]

The Republic of Moldova has made significant strides in adopting organic farming practices, supported by government subsidies and strategic focus on market demand. This paper aims to analyze the economic performance of organic farms in Moldova and their impact on the sustainable development of the agricultural sector, using primary data collected from 63 organic farms benefiting from subsidies between 2020 and 2022. The agricultural sector in Moldova has experienced a gradual increase in cultivated land, with a noteworthy 9.5% dedicated to organic farming. Economically, farmers have seen substantial growth in income per hectare, credited to favorable climatic conditions and advanced technologies. Although organic farming contributes significantly, constituting approximately one-third of total income, conventional agriculture remains the primary contributor with two-thirds. The impact of subsidies from the National Fund for Agricultural and Rural Development is evident in farm development, as calculated profits per one lei of subsidy have surged from 1.51 to 10.43 lei in 2022. This underscores the effectiveness of government support in fostering growth and development within Moldova's agricultural sector, emphasizing a positive trajectory for sustainable agricultural practices. [ABSTRACT FROM AUTHOR]

This paper explores to evaluate how Pakistan's agriculture is affected by climate change and to find techniques to mitigate and adapt to the changes. Pakistan's agriculture industry is drastically affected by climate change. The continuous inclination of temperature, shifting weather patterns, and more frequent climate change events are remarkably reducing crop yields, compromising food security and posing serious threats to rural livelihood. In order to evaluate the effects of climate change on agricultural output, water resources, and controlling pests, this study sought farmers in liable areas, reviewed a variety of literature in detail, and examined climate data. The paper also founds critical declines in crop yields, changes in developing seasons, and expanded irritation and sickness pressure. Farmers were observed using more groundwater, which resulted in a shortage of water and less fertile land. Pakistan needs to prioritize climateresilient agricultural techniques, raise farmer awareness and capacity building, improve water management, and encourage crop variety to ensure agricultural sustainability. [ABSTRACT FROM AUTHOR]

In the European agri-food sector, operators with substantial bargaining power often engage in unfair trading practices (UTPs). Our paper aims to empirically examine the occurrence of UTPs and their influence on the performance of cooperatives. To fulfill the goal of our paper, we collected responses from 109 cooperatives in Greece after the transposition of a specialized EU Directive (i.e., Directive (EU) 2019/633). We found that, on average, the sampled cooperatives encountered three prohibited ("black") UTPs, while all reported at least one prohibited UTP. Moreover, the two most commonly reported practices (i.e., "unduly late payments" and "buyers' demand that suppliers pay for the deterioration or loss of products that occurred after ownership transfer") exerted a significant negative influence on cooperative performance, even in the presence of a proficient Board of Directors. Consequently, policymakers may need to pay more attention to UTPs and ensure that the national enforcement authorities are well-equipped to act rapidly and effectively against offenders. [ABSTRACT FROM AUTHOR]

China's agricultural economy has been hindered by insufficient accumulation of agricultural capital and credit constraints. It is worth investigating whether China's county financial marketization reform policy can alleviate these constraints and promote high-quality development of the agricultural economy (HQDAE). This paper presents an empirical analysis of the impact of county financial marketization reform on the HQDAE, based on county panel data. The focus is on the mechanism of county urbanization in the above relationship. The results show that county financial marketization has a significant non-linear impact on the HQDAE. Specifically, it has a 'U-shaped' impact on the overall growth of the agricultural economy and an inverted 'N-shaped' impact on the quality improvement of the agricultural economy. Secondly, the relationship between county financial marketization and the HQDAE is influenced by a threshold effect based on the level of county urbanization. As the level of county urbanization increases, the promoting effect of county financial marketization on HQDAE also increases significantly. Additionally, county financial marketization helps to promote county urbanization and accelerate urban-rural integration, which in turn leads to HQDAE. The research in this paper suggests that county-level local governments should promote a differentiated county financial system. In the early stages of financial market-oriented reform, the government should enhance the capacity of financial services in rural areas through tax breaks, policy incentives, and other measures to prevent financial leakage from agriculture. In the later stages of financial marketization reform, the government should strengthen financial supervision to prevent financial resources from being diverted from industry to capital. Moreover, to achieve the HQDAE, it is necessary to promote county financial market-oriented reform and accelerate the construction of county urbanization. This will help break the dual economic structure of urban and rural areas and promote the flow of financial capital, technology, and human capital from county cities to rural areas. [ABSTRACT FROM AUTHOR]

The stability of agricultural futures market is of great significance to social economy and agri-cultural development. In view of the complexity of the fluctuation of agricultural futures prices, it is challenging to make up for the shortcomings of the existing data preprocessing technology so as to improve the prediction accuracy of the model. This paper puts forward a new VMDSGMD-LSTM model based on improved quadratic decomposition technology and artificial intelligence model. First of all, in the data preprocessing part, VMD is used to decompose the original futures price data, and SGMD is used to further process the remaining components. Secondly, the LSTM model is used to predict a series of modal components, and the final result is obtained by synthesizing the predicted values of different components. Furthermore, based on the futures trading data of wheat, corn and sugar in China agricultural futures market, this paper makes an empirical study in the 1-step, 2-step and 4-step ahead forecasting scenarios, respectively. The results show that compared with other benchmark models, the VMD-SGMD-LSTM hybrid model proposed in this paper has better forecasting ability and robustness for different agricultural futures, which effectively makes up for the shortcomings of existing research. [ABSTRACT FROM AUTHOR]

The interplay of machine learning (ML) and deep learning (DL) within the agroclimatic domain is pivotal for addressing the multifaceted challenges posed by climate change on agriculture. This paper embarks on a systematic review to dissect the current utilization of ML and DL in agricultural research, with a pronounced emphasis on agroclimatic impacts and adaptation strategies. Our investigation reveals a dominant reliance on conventional ML models and uncovers a critical gap in the documentation of methodologies. This constrains the replicability, scalability, and adaptability of these technologies in agroclimatic research. In response to these challenges, we advocate for a strategic pivot toward Automated Machine Learning (AutoML) frameworks. AutoML not only simplifies and standardizes the model development process but also democratizes ML expertise, thereby catalyzing the advancement in agroclimatic research. The incorporation of AutoML stands to significantly enhance research scalability, adaptability, and overall performance, ushering in a new era of innovation in agricultural practices tailored to mitigate and adapt to climate change. This paper underscores the untapped potential of AutoML in revolutionizing agroclimatic research, propelling forward the development of sustainable and efficient agricultural solutions that are responsive to the evolving climate dynamics. [ABSTRACT FROM AUTHOR]

The development of alternative soil tillage technologies, which ensure the conservation and maintenance of its productive capacity as well as the reduction of energy consumption per unit of processed area, is today a necessity in developing a sustainable agriculture. The study presented in this paper was carried out in the area of Oravița, Caraş-Severin County, Romania, and refers to the minimum tillage technology in wheat culture. The study points to the unfavourable effect of intensive soil works -- classic (conventional) system -- on soil erosion. This has led to the development of some research oriented towards new technologies of soil working in the conservation system. The purpose of this paper is to promote the introduction of alternative technologies in agricultural works, by dealing with the main technical, economic and environmental aspects that compete for the achievement of efficient agriculture. From an economic point of view, this method is less expensive because fuel consumption and machine working time are reduced. The use of modern mechanization technologies has a great economic efficiency due to the following aspects. [ABSTRACT FROM AUTHOR]

In the analysis of agricultural product price time series, the detection of abnormal fluctuations is the primary task. Accurately judging the abnormal fluctuations of agricultural product prices can help farmers avoid potential economic losses in a timely manner and improve the circulation efficiency of agricultural products. This paper introduces a convolutional neural network (CNN) classification model based on time series images (TSI), and identifies abnormal fluctuations in agricultural product prices using improved standard deviation–slope judgment. First, a standard deviation–slope (SDS) time series abnormal fluctuation criterion is proposed. Second, the Markov Transfer Field (MTF) method is used to convert the sparse one-dimensional time series of agricultural product prices into a two-dimensional dense image. Third, the CNN model is used to automatically extract features from time series images containing abnormal fluctuations, and they are divided into two categories: "normal" and "abnormal"; Finally, the performance of the proposed model was evaluated using China's corn and wheat price dataset. Comparing with other abnormal fluctuation judgment methods, the accuracy of the proposed algorithm is about 20% higher on average. This confirms the applicability of the standard deviation–slope time series Image-Resnet-34 (SDS-TSI-Resnet34) model in practical scenarios. At the end of the paper, some feasible suggestions for the efficient development of agricultural economy are proposed based on the abnormal fluctuation judgment method. [ABSTRACT FROM AUTHOR]

This paper aims at analyzing the research productivity and scientific knowledge discovery of the COVID-19 pandemic in agriculture using a bibliometric analysis approach. A total of 1514 research papers indexed in the Scopus database, covering a period of 2020 to 2022, are processed using VOSviewer and R-Studio software. The analysis of research productivity indicates that the number of research publications on COVID-19 and agriculture has exponentially increased globally, and about 80% of the research papers have been published in the top 10 countries led by the USA, India, and China. The countries are increasingly collaborating in undertaking research on COVID-19 and agriculture. Furthermore, major journals and articles with citations have been extracted to analyze the leading publication avenues and focused areas of research. The science mapping is done using co-occurrence and thematic map. With the help of co-occurrence analysis, six clusters are identified depicting major research themes, i.e., COVID-19 and agricultural supply chain disruption, COVID-19 and human health issues and coping strategies, COVID-19 and non-human and animal health, COVID-19 pandemic and environment and pollution, COVID-19 and healthcare and treatment, and COVID-19 and food nutrition from dairy and meat products. The thematic map analysis identifies potential research areas such as mental health, anxiety, and depression in the agricultural system, which may help in setting future research agenda and help devising policy supports for managing the agriculture sector better during crisis. The paper also highlights the theoretical and practical implications. [ABSTRACT FROM AUTHOR]

Agriculture plays a pivotal role in our society by providing food, fiber, and raw materials for various industries. The world's population is steadily growing, and there is increasing pressure on agriculture to meet the rising global food demand. In this context, the use of machine learning approaches to predict crop yields has gained significant importance. This paper aim is to study the significance of crop yield prediction through machine learning, its methods, applications, and its potential to revolutionize the agricultural sector. [ABSTRACT FROM AUTHOR]

Introduction: Digital agricultural technology service has the characteristics of high efficiency, convenience and flexibility, and plays an important role in the process of Chinese agricultural digitalization, which is of great significance for realizing the modernization and intelligent development of agricultural production. Methods: Based on micro-survey data of small-scale farmers in Sichuan Province, this study utilizes the OLS method and the mediation effect model to investigate the impact of digital agricultural technology services on farmers' willingness to adopt digital production technologies and its underlying mechanisms. Results: The results indicate that digital agricultural technology services effectively enhance farmers' willingness to adopt digital production technologies, especially among those engaged in cooperative. Further analysis of mechanisms suggests that digital agricultural technology services influence farmers' choices of household production and management methods by expanding their information channels, enhancing their cognition of digital production technologies, and facilitating access to technology. Moreover, heterogeneous analysis reveals significant differences in the willingness of farmers with varying levels of digital literacy to adopt digital production technologies. Digital agricultural technology services have a more pronounced impact on the willingness of highly digitally literate farmers. Discussion: The findings of this paper deepen our comprehension of the operational mechanisms of digital agricultural technology services in the context of agricultural development, and advocate for policy recommendations such as enhancing the supply of digital agricultural technology services, strengthening the role of new agricultural entities, improving farmers' digital literacy training, and enhancing policy support systems. [ABSTRACT FROM AUTHOR]

To explore whether digital finance can reduce agricultural carbon emissions, promote regional convergence, and foster inclusivity in rural revitalization and shared prosperity, this paper uses the provincial-level index of digital financial inclusion to analyze the impact of digital financial inclusion on the intensity of agricultural carbon emissions and the Degum Gini coefficient (D-Gini coefficient) of regional carbon emission intensity in 30 sample provinces from 2010 to 2020. It examines the mechanism of the impact of digital financial inclusion on both variables to understand the underlying factors better. The main conclusions are as follows: (1) Digital financial inclusion significantly reduces the intensity of agricultural carbon emissions and narrows the gap in carbon emission intensity between regions. (2) The unconditional quantile regression coefficients show that the negative coefficients of the digital financial inclusion index and the three-dimensional indices decrease with increasing quantiles. However, the significant effects vary significantly at different quantiles. (3) Technological progress and the government's ability to allocate financial resources play a significant mediating role, and the income gap between urban and rural areas can be further narrowed, as well as the carbon emission intensity gap between provinces. The empirical results are robust and proven by replacing the econometric analysis method, changing the core variables, and other methods. [ABSTRACT FROM AUTHOR]

The integration of the Internet of Things (IoT) has brought about transformative changes across diverse industries, and its application in agriculture, particularly in soil management, has become a focal point of research and innovation. This paper explores the paradigm shift enabled by IoT in detecting organic carbon in soil, with the overarching goal of advancing agricultural productivity and sustainability. Emphasizing the pivotal role of organic carbon in soil health, the research addresses the existing challenges associated with its detection and underscores the potential advantages offered by IoT-based solutions for precise and real-time monitoring. The study meticulously examines various IoT technologies, including sensors, data analytics, and wireless communication protocols, within the specific context of organic carbon detection. A thorough review of contemporary techniques, methodologies, and devices utilized in IoT-based organic carbon detection in soil is presented. The paper not only sheds light on the current state-of-the-art but also explores the advantages, limitations, and prospects inherent in the implementation of IoT-enabled systems in agriculture. The envisioned outcomes encompass enhanced soil management practices, heightened crop yields, and the simultaneous preservation of the environment. In essence, this research contributes to the expanding body of knowledge surrounding IoT applications in agriculture, specifically emphasizing the transformative potential of IoT-enabled organic carbon detection for revolutionizing farming practices and promoting sustainable agricultural development. [ABSTRACT FROM AUTHOR]