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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]

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]

Agricultural policies are widening the scope to contribute to environmental objectives, such as the Green Deal, Paris Climate Agreement and sustainable development goals. This leads to new monitoring and data needs. To fulfil these data needs, it is crucial to explore the opportunities and limitations of new technologies. This paper analyses the information flows within the agricultural sector and its potential to contribute to future information needs. Farmers act within a network of commercial and governmental organisations. The information exchange with these organisations could increasingly occur through digital means, but in reality, there is still a lot of data transfer on paper or in PDF format. This implies information loss. Digital information flows provide a wealth of information for policy evaluation and monitoring and have the potential to reduce transaction costs. Combining data from different sources (open data like earth observation data, data from on-farm sensor networks, accountancy data like invoices and data from food chain platforms) concerning a single farm is an even bigger challenge than the transfer from paper to digital. Based on these observations a list of requirements for a future solution for information transfer is defined. Based on these requirements, this paper presents the design of a System for Information Transfer to Reduce Administrative burdens (SITRA) to combine data from different sources and give farmers control who can access these data. SITRA will address farmers' needs to reduce the growing administrative burden placed on them by governments and the food chain sustainability and food safety schemes, especially if data would be stored in a digital farm locker and a farmer could voluntary give his consent through an authorisation mechanism to share specific data with his business partners, paying agency, statistical organisation and Farm Accountancy Data Network (FADN, an EU monitoring system for the Common Agricultural Policy). A platform that provides such data lockers could also provide benchmark facilities and a tool for common (open source) maintenance of coding systems. The paper proposes some follow-up activities to test this design. As a pilot, organic farmers that participate in the Dutch Farm Accountancy Data Network (FADN) will be recruited to co-develop and test the design principles of the system. Given trust issues around data management, the design of the governance and business model of the system are key issues in the follow-up. This group is chosen as organic certification and FADN are the most dataintensive monitoring and evaluation tools in the CAP. The Farm to Fork communication proposes to enlarge the organic sector to 25% of the agricultural area. A new organic control regulation, based on a risk assessment approach is about to be implemented. The Farm to Fork communication also proposes to link the FADN much more with the Farm Advisory System and to extend it to a Farm Sustainability Data Network. [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]

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]

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]

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]

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]

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]

The growth of Walailak Journal of Science and Technology is chronicled through a scientometric analysis from 2010 to 2015. The number of submissions has vastly increased since the online submission system was introduced in 2011 and surpassed 400 manuscripts per year in 2014 - 2015. It follows that the acceptance ratio has been reduced to the minimum of 25 % in 2015. The 339 published papers, mostly full-length research articles, are categorized into Science (46.61 %), Agricultural technology (23.30 %), Engineering (17.70 %), Health sciences (6.78 %) and Information technology (4.72 %). The journal is benefited from demographic diversity of authors. Papers submitted from 27 different countries were published during this 6-year period. Furthermore, domestic reviewers and editorial board members have become the minority since 2012. The citation recorded in SCOPUS database has continued to improve with the reduction in self-citation ratio. The lastest Scimago Journal Rank (SJR) obtained in 2014 is 0.207 with 33 citations and average 0.33 citations per paper. [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]

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.)

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]

Agriculture plays a crucial role in fostering sustainable growth through the integration of various technological advancements such as image processing, artificial intelligence, deep learning, and the Internet of Things (IoT). The global population is increasing on a daily basis. The increasing demand within the agriculture industry has necessitated the collective enhancement of plant cultivation and field productivity. This paper emphasizes the significance of effectively managing the crop during its initial growth phase as well as during the harvesting era. Image processing and artificial neural networks are employed as distinct methodologies for detecting illnesses on leaves. When capturing images using drones, the resulting images undergo a process of segmentation and transformation, resulting in the identification of three distinct vectors that represent diseases. These vectors include colour, texture, and morphology. This paper reviews on various disease classification strategies that can be utilized for the detection of plant diseases. [ABSTRACT FROM AUTHOR]

Genomic editing technologies are developing rapidly, promising significant developments for biomedicine, agriculture and other fields. In the present investigation, we analyzed and compared the process of innovation for six genomic technologies: viral vectors, RNAi, TALENs, meganucleases, ZFNs and CRISPR/Cas including the profile of the main research institutions and their funders, to understand how innovation evolved and what institutions influenced research trajectories. A Web of Science search of papers on viral vectors RNAi, CRISPR/Cas, TALENs, ZFNs and meganucleases was used to build a citation network of 16,746 papers. An analysis of network clustering combined with text mining was performed. For viral vectors, a long-term process of incremental innovation was identified, which was largely publicly funded in the United States and the European Union. The trajectory of RNAi research included clusters related to the study of RNAi as a biological phenomenon and its use in functional genomics, biomedicine and pest control. A British philanthropic organization and a US pharmaceutical company played a key role in the development of basic RNAi research and clinical application respectively, in addition to government and academic institutions. In the case of CRISPR/Cas research, basic science discoveries led to the technical improvements, and these two in turn provided the information required for the development of biomedical, agricultural, livestock and industrial applications. The trajectory of CRISPR/Cas research exhibits a geopolitical division of the investigation efforts between the US, as the main producer and funder of basic research and technical improvements, and Chinese research institutions increasingly leading applied research. Our results reflect a change in the model for financing science, with reduced public financing for basic science and applied research on publicly funded technological developments in the US, and the emergence of China as a scientific superpower, with implications for the development of applications of genomic technologies. [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]

In order to explore the impact of education and training of farmers' professional cooperatives on agricultural green production, this paper uses the Oprobit model to analyze the impact of the time, methods, and content of cooperative education and training on green production willingness based on 534 micro-survey data of cooperative members in Sichuan Province. And then the instrumental variable method (IV-Oprobit) is used for endogeneity treatment and robustness test. The results show that: (1) Cooperative education and training time can significantly promote the green production willingness of members. Specifically, for every additional hour of education and training, the probability of "very willing" to participate in green production increases by 1.7%; (2) the four types of education and training methods of cooperative project demonstration, on-site training, bulletin board publicity, and online information publicity are all positively affecting the willingness of members to green production, and on-site training is the most effective method; (3) the three education and training contents of cognition, emotion and skills all positively affect the green production intention of members, and cognition is the most effective content; (4) the methods and contents of cooperative education and training have an interactive effect on the influence of members' green production willingness. Based on this, this paper proposes policy suggestions to optimize the functions, training methods, and content design of green production education and training in cooperatives. [ABSTRACT FROM AUTHOR]

This paper firstly compares the structure of agricultural supply chain ecosystem from both internal members and external environment and analyzes the information transfer mechanism of information flow, capital flow, logistics and other energy and information in the supply chain. The importance of supply chain ecological coordination mechanism and its functional components are also discussed in depth. On this basis, the biosecurity strategy system of agricultural products supply chain management is constructed and the biosecurity strategy of supply chain management is proposed. Finally, the biosafety risk of agricultural products supply chain management in the e-commerce environment is assessed, and a multi-dimensional risk assessment index is proposed. Meanwhile, risk management theory is applied to provide a detailed analysis of the likelihood, loss, contagiousness, controllability and comprehensiveness of risks. The study showed that the risk indices were relatively high with the average values of 85% for transportation and storage technology risk, 76% for additive abuse risk, and 70% for agricultural input improper risk, respectively. The research in this paper provides new ideas and methods for biosafety management in the supply chain of agricultural products. [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]

The digital economy is integral in driving economic growth, particularly of high-quality standards. Against the backdrop of the "Double Carbon" project, exploring the impact of the digital economy on green agricultural development bears tremendous practical significance. Thus, this study aims to investigate how the digital economy affects agricultural green total factor productivity (GTFP). The entropy-TOPSIS method and SBM-GML index are used to measure core variables quantitatively. In addition, two-way fixed effects panel data models, Tobit, and intermediary effect models are implemented. Three research findings emerge. First, China's agricultural GTFP level generally experiences an upward trend, and the development of the digital economy has a significant positive effect on increasing agricultural GTFP. Second, the digital economy mainly promotes agricultural technology innovation that boosts agricultural GTFP. Third, Western China experiences a more significant positive effect of the digital economy on agricultural GTFP than Central and Eastern China. Finally, based on the findings, this paper proposes relevant policy recommendations to promote green and sustainable agriculture development. [ABSTRACT FROM AUTHOR]

The paper aims to identify the main aspects of digitalization in the EU and Romania at present, in general, and emphasizing on agriculture, because in this sector has a deep impact on infrastructure and work resources. The EU average for DESI index quantifying digital economy performance was compared with the index in the member states to show the differences in the performance of the digital economy. The aspects related to the impact of digitalization in agriculture from many points of view, but mainly on human resources have also been highlighted. Index scores differ from one country to another and reflect the performance result of the efforts that each member state has made to implement digital economy. The higher the index value, the higher the digital economy performance in that country. During the period 2017-2022, Finland, Denmark, the Netherlands and Sweden have maintained their top position in the EU, while Romania and Bulgaria are situated on the last places. Romania should be focused on the improvement of digital infrastructure, promotion of digital competences, innovation and entrepreneurship in order to increase its performance in digital economy. Major efforts should be done to improve connectivity, educational programmes in digital competences and to develop a favourable business environment for contributing to its digital transformation and integration into the EU digital unique market. [ABSTRACT FROM AUTHOR]

Digitization is becoming the key to achieving rural revitalization, and there is a complex inter-active relationship with farmer enrichment, and agriculture development, making it highly valued by governments around the world. Quantitatively evaluating the symbiotic relationship between rural digitization, farmer enrichment, and agricultural development, and reveal the driving mechanism behind them, which can provide evidence for the rural government, agricultural enterprises, and relevant stakeholders. This paper conducts an empirical study of 84 digital village national pilots in China based on coupling coordination model and geographically weighted regression, with focus on the spatial characteristics, symbiotic relationship and driving mechanisms of the pilots. The symbiotic relationship between rural digitization, farmer enrichment, and agriculture development has significant heterogeneity, with most of the pilots in coordination state. The driving mechanism of rural digitization, farmer enrichment, agriculture development, and symbiotic relationship were complicated, with significant heterogeneity and synergy in the driving forces of different factors, and special attention should be paid to spatial effects (the nature and intensity of regression coefficients in local regions) in the policy design and implementation. It is worth noting that population aging mainly plays a negative role, while industrialization, government support, resident capacity plays a positive role. The other factors play both positive and negative roles. Income gap between urban and rural residents, population aged 65 and above, financial self-sufficiency are the most critical factors. We suggest developing differentiated management policy, accurate management policy, and integrated management policy, which can provide a basis for digital village and smart village planning, construction and management in China. In view of the significant differences in rural digitalization and its driving mechanisms in different pilots as shown in the study, it is necessary to design differentiated spatial policies according to local conditions, to design accurate management policies based on the driving effects of key single factors, and to design integrated development policies by taking into account the interactive effects of multiple factors. [ABSTRACT FROM AUTHOR]

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The symbiotic relationship between nitrogen-fixing cyanobacteria and plants offers a promising avenue for sustainable agricultural practices and environmental remediation. This review paper explores the molecular interactions between nitrogen-fixing cyanobacteria and nanoparticles, shedding light on their potential synergies in agricultural nanotechnology. Delving into the evolutionary history and specialized adaptations of cyanobacteria, this paper highlights their pivotal role in fixing atmospheric nitrogen, which is crucial for ecosystem productivity. The review discusses the unique characteristics of metal nanoparticles and their emerging applications in agriculture, including improved nutrient delivery, stress tolerance, and disease resistance. It delves into the complex mechanisms of nanoparticle entry into plant cells, intracellular transport, and localization, uncovering the impact on root-shoot translocation and systemic distribution. Furthermore, the paper elucidates cellular responses to nanoparticle exposure, emphasizing oxidative stress, signaling pathways, and enhanced nutrient uptake. The potential of metal nanoparticles as carriers of essential nutrients and their implications for nutrient-use efficiency and crop yield are also explored. Insights into the modulation of plant stress responses, disease resistance, and phytoremediation strategies demonstrate the multifaceted benefits of nanoparticles in agriculture. Current trends, prospects, and challenges in agricultural nanotechnology are discussed, underscoring the need for responsible and safe nanoparticle utilization. By harnessing the power of nitrogen-fixing cyanobacteria and leveraging the unique attributes of nanoparticles, this review paves the way for innovative, sustainable, and efficient agricultural practices. [ABSTRACT FROM AUTHOR]

This study aims to provide a scientific basis for the development of county-level agricultural industries through a comprehensive evaluation of the environmental and economic benefits of agricultural products. Focusing on Hancheng City in Shaanxi Province, this paper calculates and analyzes the carbon emission intensity per unit output value and the economic benefits of major agricultural products, assessing their comprehensive advantage indices. The research methods include data collection, data processing, and model construction, utilizing a bi-factor matrix analysis to explore the balance between environmental sustainability and economic profitability of different agricultural products. The results indicate that pepper and vegetables have the highest comprehensive advantages, demonstrating significant economic and environmental benefits, while soybeans show lower comprehensive advantages, requiring improvements in cultivation techniques and management practices. Based on the research findings, this paper proposes policy and management recommendations for different agricultural products, including focusing on the development of high-comprehensive-advantage products, improving cultivation techniques for low-comprehensive-advantage products, promoting green agricultural technologies, establishing a carbon footprint monitoring system for agricultural products, and strengthening agricultural infrastructure construction. The study's conclusions provide theoretical support and practical guidance for the agricultural development strategies of Hancheng City and similar regions, contributing to the achievement of sustainable agricultural development and carbon reduction goals. [ABSTRACT FROM AUTHOR]

The current geopolitical and socioeconomic landscape creates a difficult and uncertain operating environment for farming and agri-food businesses. Technological innovation has not been suggested to be a "silver bullet" but is one of the ways organizations can seek to reduce environmental impact, deliver net zero, address the rural skills and labor deficit and produce more output from fewer resources and as a result, make space for nature. But what barriers limit this promissory narrative from delivering in practice? The purpose of the paper is to firstly explore the reported social, technical and systemic barriers to agri-technology adoption in an increasingly uncertain world and then secondly identify potential research gaps that highlight areas for future research and inform key research questions. Socio-technical and infrastructural barriers have been identified within the context of the complex hollowing out and infilling of rural communities across the world. These barriers include seventeen factors that emerge, firstly those external to the farm (economic conditions, external conditions including bureaucracy, market conditions, weather uncertainty and the narratives about farmers), those internal to the farm business (farming conditions, employee relations, general finance, technology and time pressures) and then personal factors (living conditions, personal finances, physical health, role conflict, social isolation and social pressure). Adaptive resilience strategies at personal, organizational and community levels are essential to address these barriers and to navigate agri-technology adoption in an uncertain and dynamic world. [ABSTRACT FROM AUTHOR]

Sustainable agriculture involves the use of organic farming technologies, which excludes the use of growth stimulants, hormones and chemical additives in animal feeding as well as fertilisers with pesticides and other hazardous chemicals. Sustainable land management practices are aimed at preserving soils, water resources and biodiversity. They mitigate the effects of climate change by reducing greenhouse gas emissions and preserving natural environments. Organic agriculture produces environmentally friendly food that contains fewer harmful chemicals, potentially reducing the incidence of diet-related disorders such as cardiovascular diseases, diabetes and cancer. This study conducts a comprehensive bibliometric analysis (via VOSviewer 1.6.16, Bibliometrix / Biblioshiny App) to explore the sustainable agriculture impact on public health and sustainable development of nations. Applying the Scopus database, 427 relevant papers were reviewed to identify trends, influential works, and key research themes. The analysis reveals that sustainable agricultural practices, which aim to reduce environmental impact, conserve resources and enhance productivity, have gained increasing attention since the mid-1990s. Noteworthy, contributions include fundamental works, influential studies and various UN and WHO publications. The research highlights keywords such as agriculture, public health, nutrition, and food security. Leading countries in this research domain include the USA, China, Canada, India, and the UK, demonstrating extensive international collaboration. The findings underscore the critical role of sustainable agriculture in addressing global challenges, promoting environmental stewardship, and supporting socio-economic progress, which aligns with the UN Sustainable Development Goals. The study provides valuable insights into the development of sustainable practices and policies essential for future research and implementation. [ABSTRACT FROM AUTHOR]

Due to the global population increase and the recovery of agricultural demand after the COVID-19 pandemic, the importance of agricultural automation and autonomous agricultural vehicles is growing. Fallen person detection is critical to preventing fatal accidents during autonomous agricultural vehicle operations. However, there is a challenge due to the relatively limited dataset for fallen persons in off-road environments compared to on-road pedestrian datasets. To enhance the generalization performance of fallen person detection off-road using object detection technology, data augmentation is necessary. This paper proposes a data augmentation technique called Automated Region of Interest Copy-Paste (ARCP) to address the issue of data scarcity. The technique involves copying real fallen person objects obtained from public source datasets and then pasting the objects onto a background off-road dataset. Segmentation annotations for these objects are generated using YOLOv8x-seg and Grounded-Segment-Anything, respectively. The proposed algorithm is then applied to automatically produce augmented data based on the generated segmentation annotations. The technique encompasses segmentation annotation generation, Intersection over Union-based segment setting, and Region of Interest configuration. When the ARCP technique is applied, significant improvements in detection accuracy are observed for two state-of-the-art object detectors: anchor-based YOLOv7x and anchor-free YOLOv8x, showing an increase of 17.8% (from 77.8% to 95.6%) and 12.4% (from 83.8% to 96.2%), respectively. This suggests high applicability for addressing the challenges of limited datasets in off-road environments and is expected to have a significant impact on the advancement of object detection technology in the agricultural industry. [ABSTRACT FROM AUTHOR]

Despite its status as one of the most ancient sectors worldwide, agriculture continues to be a fundamental cornerstone of the global economy. Nevertheless, it faces obstacles such as a lack of trust, difficulties in tracking, and inefficiencies in managing the supply chain. This article examines the potential of blockchain technology (BCT) to alter the agricultural industry by providing a decentralized, transparent, and unchangeable solution to meet the difficulties it faces. The initial discussion provides an overview of the challenges encountered by the agricultural industry, followed by a thorough analysis of BCT, highlighting its potential advantages. Following that, the article explores other agricultural uses for blockchain technology, such as managing supply chains, verifying products, and processing payments. In addition, this paper examines the constraints and challenges related to the use of blockchain technology in agriculture, including issues such as scalability, legal frameworks, and interoperability. This paper highlights the potential of BCT to transform the agricultural industry by offering a transparent and secure platform for managing the supply chain. Nevertheless, it emphasizes the need for involving stakeholders, having clear legislation, and possessing technical skills in order to achieve effective implementation. This work utilizes a systematic literature review using the PRISMA technique and applies meta-analysis as the research methodology, enabling a thorough investigation of the present information available. The results emphasize the significant and positive effect of BCT on agriculture, emphasizing the need for cooperative endeavors among governments, industry pioneers, and technology specialists to encourage its extensive implementation and contribute to the advancement of a sustainable and resilient food system. [ABSTRACT FROM AUTHOR]

As a powerful statistical method, meta-analysis has been applied increasingly in agricultural science with remarkable progress. However, meta-analysis research reports in the agricultural discipline still need to be systematically combed. Scientometrics is often used to quantitatively analyze research on certain themes. In this study, the literature from a 30-year period (1992–2021) was retrieved based on the Web of Science database, and a quantitative analysis was performed using the VOSviewer and CiteSpace visual analysis software packages. The objective of this study was to investigate the current application of meta-analysis in agricultural sciences, the latest research hotspots, and trends, and to identify influential authors, research institutions, countries, articles, and journal sources. Over the past 30 years, the volume of the meta-analysis literature in agriculture has increased rapidly. We identified the top three authors (Sauvant D, Kebreab E, and Huhtanen P), the top three contributing organizations (Chinese Academy of Sciences, National Institute for Agricultural Research, and Northwest A&F University), and top three productive countries (the USA, China, and France). Keyword cluster analysis shows that the meta-analysis research in agricultural sciences falls into four categories: climate change, crop yield, soil, and animal husbandry. Jeffrey (2011) is the most influential and cited research paper, with the highest utilization rate for the Journal of Dairy Science. This paper objectively evaluates the development of meta-analysis in the agricultural sciences using bibliometrics analysis, grasps the development frontier of agricultural research, and provides insights into the future of related research in the agricultural sciences. [ABSTRACT FROM AUTHOR]

The long-term and stable development of agriculture is the key to China's economic development and social stability. Agricultural total factor productivity and the digital economy have become new kinetic energy and new engines driving agricultural high-quality development. It is of great significance to verify whether there are significant spatial and threshold effects in the process of high-quality development of agriculture and to explore the intrinsic relationship between high-quality development of agriculture and agricultural total factor productivity and digital economy. This paper takes 31 provinces in China from 2011 to 2020 as the research object. The coefficient of variation method is used to estimate the comprehensive evaluation index of agricultural high-quality development and digital economy. And Dea-Malmquist index method is used to estimate agricultural total factor productivity. On this basis, the spatial Durbin model and threshold regression model are constructed to explore the spatial and threshold effects of agricultural total factor productivity, digital economy and other factors and high-quality agricultural development. The conclusion is as follows: the high-quality development of agriculture has significant spatial autocorrelation. Agricultural total factor productivity and digital economy have significant direct effect and indirect spillover effect on the high-quality development of agriculture. Agricultural total factor productivity has stage differences in each range of digital economy level, but its influence on agricultural high-quality development shows a positive state. Based on this, the paper puts forward some countermeasures to promote the high-quality development of agriculture. [ABSTRACT FROM AUTHOR]

This paper studied the performance audit regarding the management of the agricultural sector subsidy funds in order to determine to what extent the actions undertaken boosted the agricultural development process in the Republic of Moldova. The analysis revealed that the activity of subsidizing agriculture is primarily aimed at increasing competitiveness and the sustainable development of the agri-food sector. This situation leads to the modernization of the agri-food chain, facilitating access to capital markets for agricultural producers, adapting to climate changes and mitigating their effects on agricultural production, increasing rural employment opportunities, developing agricultural businesses, etc. The data on the subsidy activity were taken from the database of the Agency for Intervention and Payments for Agriculture from the Republic of Moldova, which is responsible for managing the subsidy fund. The results of the study highlight a fairly efficient management of financial means for subsidizing the national agricultural sector. [ABSTRACT FROM AUTHOR]

Different capital structures and their impact on firm performance are one of the most studied topics in the finance literature. This study is an attempt to assess the impact of financial structure on profitability of agricultural enterprises in the Republic of Serbia, with the aim of evaluating the importance and nature of the contribution of the total, short-term and long-term aspects of debt to the success of the company. The financial performance of the selected sample of 38 medium and large agricultural enterprises in the Republic of Serbia was analysed in the period from 2018 to 2021. The findings of this paper indicate that total debt and short-term debt significantly determine the return on equity, while the effect of long-term debt is insignificant. The nature of the impact of all three aspects of financial structure is negative. The inclusion of size and growth of sales in the regression models showed that only annual sales growth significantly positively determines profitability. [ABSTRACT FROM AUTHOR]

Agricultural technologies (agri-techs) have focused on developing the AI perspective of human-AI interaction rather than human perceptions and responses. A lack of understanding of their employees' behavioral responses when interacting with advanced technologies can lead to unexpected problems in the future. Drawing on the theoretical perspective of advanced user engagement, this paper examines the impact of five different technostressors on user engagement and, consequently, user experience. For data collection, 464 participants from the U.S. and Asian (Singaporean) agri-tech sectors were interviewed via an electronic survey. The U.S. study showed that techno-overload, techno-complexity, and techno-uncertainty were positively related to user engagement (t = 2.609; t = 6.998, and t = 6.013, respectively), whereas techno-invasion and techno-uncertainty were negatively correlated with user engagement (t = –2.167 and t = –3.119, respectively). The Singapore study showed that techno-overload, techno-complexity, and techno-invasion were negatively related to user engagement (t = –2.185, t = –2.765; t = –5.062, respectively), while techno-insecurity and techno-uncertainty surprisingly showed nonlinear correlations with user engagement. In both studies, user engagement is positively related to user experience (t = 2.009 for the U.S. study and t = 2.887 for the Singapore study). First, this paper provides agri-techs with a modern framework to better predict the behavioral responses of their employees when managing AI. Second, this paper expands the equation of change in the discipline of change management by introducing the dimension of readiness. [ABSTRACT FROM AUTHOR]

The institutional environment in Bulgarian agricultural sector is a complex system of relationships, dynamically changing under the influence of national and sectoral legislation. Over the last 16 years, was characterized as a difficult period for agriculture and land relations in Bulgaria -- because of insufficient legislation the Ownership and Use of Agricultural Land Act about 34 changes have been made to the Act, which are directed in the interest of agricultural users. The changes made concern the concluded contracts for the use and lease of the agricultural land. The dynamics and future changes in land relations are inextricably linked to changes in European and national policy. Receiving European subsidies and financial support to the income of Bulgarian farmers deforms the economic environment in our country. The European subsidies are a key motive and priority for farmers. The purpose of this paper is to describe the state in Bulgarian agricultural sector which is characterized by intensity and dynamics, more complexity and instability. The value of the gross agricultural output had declined during the last years. This research work is based on the following methodological approaches: complex, structural and comparative. The theoretical framework of this article provides a summary overview of the main institutional changes in the agricultural policy and the structure of land relations in Bulgaria. For the purposes of the research different information sources have been used - scientific journals, as well as the author's own research. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to present the procedure of managing public procurement contracts for sustainable agricultural products from the public entities perspective (with reference to setting criteria for selecting the suppliers, contract management, etc), but also from the perspective of the selling companies (from drafting the tender to products being delivered). The research is based on data processed by Eurostat (2012-2021), but also on information taken from the Romanian Electronic Public Procurement System (2018-2022). The analyses in the paper concern indicators such as: the procurement of agricultural products from Romania, and at European level the organic production of animal products, the number of processors of sustainable agricultural products, etc. In conclusion, since the public institutions buy large quantities of agricultural products, but the market for sustainable agricultural products is restricted, a global strategy for rethinking the public procurement in this area is clearly required. The work is relevant both for the contracting authorities with respect to the management of public procurement for agricultural products, but also for the companies with respect to the business management of public contracts. [ABSTRACT FROM AUTHOR]

Effects of Bacillus thuringiensis (Bt) cotton are at the forefront of an intense debate on the benefits of genetically modified (GM) crops among smallholder farmers in developing countries. Existing studies fail to control for confounders, selection bias, or cultivation bias from preferential treatment in the initial adoption phase. Addressing these concerns in this paper, I examine the impact of Bt cotton employing an unbalanced panel fixed-effects model of a crop yield and profit function on newly collected plot-level data in the most recent decade. Results show that Bt cotton yields have stagnated, have a null effect on profits, and have become more sensitive to pest pressure in the most recent decade. Though many studies have demonstrated higher crop yield and profit gains in the first decade of Bt cotton adoption that raised the average returns to the technology, the second decade shows convergence in benefits, which raises obvious questions about the prospect of GM technology. Since Bt cotton is the only GM crop technology widely adopted by smallholder farmers, the findings of this paper contribute to the broader public debate on the future of agricultural biotechnology. [ABSTRACT FROM AUTHOR]

With object detection technology, real-time detection of dense scenes has become an important application requirement in various industries, which is of great significance for improving production efficiency and ensuring public safety. However, the current mainstream target detection algorithms have problems such as insufficient accuracy or inability to achieve real-time detection when detecting dense scenes, and to address this problem this paper improves the YOLOv7 model using attention mechanisms that can enhance critical information. Based on the original YOLOv7 network model, part of the traditional convolutional layers are replaced with the standard convolution combined with the attention mechanism. After comparing the optimization results of three different attention mechanisms, CBAM, CA, and SimAM, the YOLOv7B-CBAM model is proposed, which effectively improves the accuracy of object detection in dense scenes. The results on VOC datasets show that the YOLOv7B-CBAM model has the highest accuracy, reaching 87.8%, 1.5% higher than that of the original model, and outperforms the original model as well as other models with improved attention mechanisms in the subsequent results of two other different dense scene practical application scenarios. This model can be applied to public safety detection, agricultural detection, and other fields, saving labor costs, improving public health, reducing the spread and loss of plant diseases, and realizing high-precision, real-time target detection. [ABSTRACT FROM AUTHOR]

The technological revolution and the evolution of technology have significantly facilitated the applicability of the IoT in various domains, such as healthcare, transportation, agriculture, retail, education, and, especially, higher education, which encompasses countless areas. Petri nets can be a useful tool to model the behavior of an IoT system. The main objective of this paper was to propose, model, and analyze a complex IoT system for higher education. The system involves the integration of IoT devices for monitoring data. An educational cloud was used as a support tool through which tracking, and control actions were implemented both internally, between the cloud and entities, and externally, between the cloud and the IoT. The system was modeled using Petri nets, which are systems with discrete events, and for simulation, we used the Visual Object Net++ package. Using this application, information was obtained in real time, and it was possible to intervene with changes even in the design phase. The diagrams were easy to read and interpret, which is an advantage for the decision-making system. The general structure of the application was based on n entities, where each entity represented a higher education field. In this paper, we discuss at least three fields: economics, computational linguistics, and engineering. [ABSTRACT FROM AUTHOR]

Vrchota et al. [[21]] evaluated precision agriculture technologies' practical use in agricultural enterprises in the Czech Republic, which is a reference for the development and implementation of precision agriculture technology and equipment in each country. The global agriculture faces critical pressures, including an aging population, rising production costs, and labor shortages. 10.3390/agriculture12071051 14 Liu L., Yuan J., Gong L., Wang X., Liu X. Dynamic Fresh Weight Prediction of Substrate-Cultivated Lettuce Grown in a Solar Greenhouse Based on Phenotypic and Environmental Data. For intelligent agricultural machines, Bai et al. [[1]] designed a monitoring system for the sowing quality of cotton precision planters, to realize the real-time monitoring of the cotton precision seeding operation processes and improve the intelligence level of cotton precision planters. [Extracted from the article]