Search

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]

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]

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]

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]

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]

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]

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 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 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]

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]

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]

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]

This paper provides policy recommendations for accelerating the adoption of Fossil-Energy-Free Technologies and Strategies (FEFTS) in the EU agricultural sector. Faster adoption of these technologies and strategies is crucial to achieving the medium- and long-term sustainability targets laid out in EU policy. The prepared policy recommendations originate out of the key outputs and findings of the Horizon 2020 project "AgroFossilFree", including an assessment and evaluation of the current energy use status in EU agriculture, survey results on farmers' needs, ideas and interests on the adoption of FEFTS, FEFTS categories identified through an online inventory of FEFTS called the AgEnergy platform, and key innovative processes through national and transnational workshops that combine expertise from hundreds of keys stakeholders (researchers, innovation brokers, policymakers, farmers, and industry representatives). The policy recommendations are synthesized and presented in the form of 19 policy briefs split into three main categories: those that are related to energy issues in farming and can be applied to any farm and FEFTS type; those that are specific to certain agricultural production systems; and those that are necessary for FEFTS integration in agriculture in general. [ABSTRACT FROM AUTHOR]

The academic field has limited studies on implementing nature-based solutions (NbSs) in Chinese agriculture, especially regarding households' and farmers' attitudes toward the acceptance of agricultural NbS technologies. Based on this, this study aims to fill this knowledge gap by examining the factors behind farmers' agricultural practices of NbS adoption in Northeast China. Accordingly, this paper constructs an extended theoretical framework of planned behavior (TPB) and empirically tests it using partial least squares structural equation modeling (PLS-SEM) on data from 298 farmers in Inner Mongolia. It was found that incorporating institutional trust (IT) into TPB improves the prediction of Chinese farmers' intention to de-farm behavior. Unlike previous studies, this study identified two indirect pathways influencing farmers' intention to adopt agricultural NbS technologies: from institutional trust through attitude to intention, and from institutional trust through perceived behavioral control to intention. The findings broaden the research perspective in this area, provide empirical evidence for the application of NbSs in agricultural practices in China and other developing countries, and demonstrate in practice that NbS has been promoted as an essential tool to maximize nature's ability to provide ecosystem services and create direct economic benefits for farmers through increased yields and reduced costs. [ABSTRACT FROM AUTHOR]

Amidst escalating global populations and dwindling arable lands, enhancing agricultural productivity and sustainability is imperative. Addressing the inefficiencies of traditional agriculture, which struggles to meet the demands of large-scale production, this paper introduces a highly configurable smart agricultural robotic arm system (CARA), engineered using convolutional neural networks and multilayer perceptron. CARA integrates a highly configurable robotic arm, an image acquisition module, and a deep processing center, embodying the convergence of advanced robotics and artificial intelligence to facilitate precise and efficient agricultural tasks including harvesting, pesticide application, and crop inspection. Rigorous experimental validations confirm that the system significantly enhances operational efficiency, adapts seamlessly to diverse agricultural contexts, and bolsters the precision and sustainability of farming practices. This study not only underscores the vital role of intelligent automation in modern agriculture but also sets a precedent for future agricultural innovations. [ABSTRACT FROM AUTHOR]

The advancement of agricultural technology has increasingly positioned robotic detection and localization techniques at the forefront, ensuring critical support for agricultural development through their accuracy and reliability. This paper provides an in-depth analysis of various methods used in detection and localization, including UWB, deep learning, SLAM, and multi-sensor fusion. In the domain of detection, the application of deep algorithms in assessing crop maturity and pest analysis is discussed. For localization, the accuracy of different methods in target positioning is examined. Additionally, the integration of convolutional neural networks and multi-sensor fusion with deep algorithms in agriculture is reviewed. The current methodologies effectively mitigate environmental interference, significantly enhancing the accuracy and reliability of agricultural robots. This study offers directional insights into the development of robotic detection and localization in agriculture, clarifying the future trajectory of this field and promoting the advancement of related technologies. [ABSTRACT FROM AUTHOR]

Fish individual recognition technology is one of the key technologies to realize automated farming. Aiming at the deficiencies in the existing animal individual recognition technology, this paper proposes a method for individual recognition of underwater fish based on deep learning technology, which is divided into two parts: fish individual object detection and fish individual recognition. In the object detection part, the research has improved a new object detection for underwater fish based on the YOLOv4 algorithm, which changed the feature extraction network in YOLOv4 from CSP Darknet53 to Mobilenetv3 and changed the 3 × 3 convolution in the enhanced feature extraction network PANet to depthwise separable convolution. Compared with the original YOLOv4, the mean average precision is improved by 1.97%. For individual recognition, an algorithm called FIRN (Fish Individual Recognition Network) for individual recognition of underwater fish is proposed. The feature extraction network of the algorithm uses the improved ResNext50, and the loss function uses Arcface Loss. The CBAM attention module is introduced in the residual block of ResNext50, the max-pooling layer in the trunk is removed, and dilated convolution is introduced in the residual block, which increases the receptive field and improves the ability of feature extraction. Experiments show that the FIEN algorithm can enhance the compactness within a class while ensuring the separability between classes, and has a better recognition effect than other algorithms. [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]

As the global population is expected to reach 10 billion by 2050, the agricultural sector faces the challenge of achieving an increase of 60% in food production without using much more land. This paper explores the potential of Artificial Intelligence (AI) to bridge this "land gap" and mitigate the environmental implications of agricultural land use. Typically, the problem with using AI in such agricultural sectors is the need for more specific infrastructure to enable developers to design AI and ML engineers to deploy these AIs. It is, therefore, essential to develop dedicated infrastructures to apply AI models that optimize resource extraction in the agricultural sector. This article presents an infrastructure for the execution and development of AI-based models using open-source technology, and this infrastructure has been optimized and tuned for agricultural environments. By embracing the MLOps culture, the automation of AI model development processes is promoted, ensuring efficient workflows, fostering collaboration among multidisciplinary teams, and promoting the rapid deployment of AI-driven solutions adaptable to changing field conditions. The proposed architecture integrates state-of-the-art tools to cover the entire AI model lifecycle, enabling efficient workflows for data scientists and ML engineers. Considering the nature of the agricultural field, it also supports diverse IoT protocols, ensuring communication between sensors and AI models and running multiple AI models simultaneously, optimizing hardware resource utilization. Surveys specifically designed and conducted for this paper with professionals related to AI show promising results. These findings demonstrate that the proposed architecture helps close the gap between data scientists and ML engineers, easing the collaboration between them and simplifying their work through the whole AI model lifecycle. [ABSTRACT FROM AUTHOR]

In the context of global food security and the pursuit of sustainable agricultural development, fostering synergistic innovation in the seed industry is of strategic importance. However, the collaborative innovation process between seed companies, research institutions, and governments is fraught with challenges due to information asymmetry and bounded rationality within the research and development phase. This paper establishes a multi-agent evolutionary game framework, taking the breeding of salt-tolerant rice as a case study. This study, grounded in the theories of information asymmetry and bounded rationality, constructs a two-party evolutionary game model for the interaction between enterprises and research institutions under market mechanisms. It further extends this model to include government participation, forming a three-party evolutionary game model. The aim is to uncover the evolutionary trends in collaborative behavior under various policy interventions and to understand how governments can foster collaborative innovation in salt-tolerant rice breeding through policy measures. To integrate the impact of historical decisions on the evolution of collaborative innovation, this research employs a delay differential equation (DDE) algorithm that takes historical lags into account within the numerical simulation. The stability analysis and numerical simulation using the DDE algorithm reveal the risk of market failure within the collaborative innovation system for salt-tolerant rice breeding operating under market mechanisms. Government involvement can mitigate this risk by adjusting incentive and restraint mechanisms to promote the system's stability and efficiency. Simulation results further identify that the initial willingness to participate, the coefficient for the distribution of benefits, the coefficient for cost sharing, and the government's punitive and incentivizing intensities are crucial factors affecting the stability of collaborative innovation. Based on these findings, the study suggests a series of policy recommendations including enhancing the initial motivation for participation in collaborative innovation, refining mechanisms for benefit distribution and cost sharing, strengthening regulatory compliance systems, constructing incentive frameworks, and encouraging information sharing and technology exchange. These strategies aim to establish a healthy and effective ecosystem for collaborative innovation in salt-tolerant rice breeding. While this research uses salt-tolerant rice breeding as a case study, the proposed cooperative mechanisms and policy suggestions have universal applicability in various agricultural science and technology innovation scenarios, especially when research meets widespread social needs but lacks commercial profit drivers, underscoring the essential role of government incentives and support. Consequently, this research not only contributes a new perspective to the application of evolutionary game theory in agricultural science and technology innovation but also offers empirical backing for policymakers in advancing similar collaborative innovation endeavors. [ABSTRACT FROM AUTHOR]

Nudging the adoption of agricultural green production technologies (AGPTs) by cotton farmers is a practical need to implement the national "green development" strategy. Based on the micro-survey data of 502 cotton farmers, this paper empirically analyzed the influence and driving mechanism of cotton farmers' participation in cooperatives on their adoption of green production technology from the perspective of their inner cognition and external regulation by using the propensity score matching (PSM) model and the intermediary effect model. The study found that (1) the importance of agricultural green production technology to cotton farmers was in the order of soil testing and formula fertilization technology, green prevention and control technology, deep tillage technology, water-saving irrigation technology, new variety technology, and straw-returning technology. (2) Participation in cooperatives can significantly improve the adoption of agricultural green production technologies by cotton farmers, with an increase of about 27.16%, and the improvement effect on technology-intensive production links is pronounced. (3) By improving the inner cognition and external regulation of cotton farmers, cooperatives can enhance the green endogenous power of cotton farmers, improve environmental external constraints, and become an intermediary path to guide cotton farmers to adopt agricultural green production technology. [ABSTRACT FROM AUTHOR]

In the last decade, agricultural cooperatives have become increasingly popular in the food industry. This paper aims to shed light on the extensive literature on agricultural cooperatives. Design/Methodology/Approach: In conducting this review, we applied the bibliometric review method. Initially, we retrieved 1249 bibliometric data from the Scopus database, which were reduced to 364 documents after applying the PRISMA guidelines. The data were filtered using the following keywords: "agricultural marketing cooperatives", "agricultural marketing societies", "performance", "value chains", and "supply chains". Findings: According to our findings, the present research is primarily focused on smallholders, sustainability, and supply chain management topics. We found that current research lacks an understanding of why agricultural cooperatives fail in terms of finances, investments, and implementation of strategies. We conclude that agricultural marketing cooperatives in their current state and legal form might not be flexible enough to compete in markets due to global and sustainability concerns. Finally, we provide a practical roadmap for researchers, investors, policymakers, and non-profits. Limitations: The main limitation of our review is that it contains only studies found in Scopus and examines only the English language literature. Originality/Value: Bibliometric analyses focused on agriculture cooperatives are scarce, and this paper provides a broader perspective of the existing literature. Moreover, it identifies research gaps and current trends in the field while providing a detailed description of how agricultural cooperative research has evolved over time. [ABSTRACT FROM AUTHOR]

This review paper discusses the development trends of agricultural autonomous all-terrain vehicles (AATVs) from four cornerstones, such as (1) control strategy and algorithms, (2) sensors, (3) data communication tools and systems, and (4) controllers and actuators, based on 221 papers published in peer-reviewed journals for 1960–2023. The paper highlights a comparative analysis of commonly employed control methods and algorithms by highlighting their advantages and disadvantages. It gives comparative analyses of sensors, data communication tools, actuators, and hardware-embedded controllers. In recent years, many novel developments in AATVs have been made due to advancements in wireless and remote communication, high-speed data processors, sensors, computer vision, and broader applications of AI tools. Technical advancements in fully autonomous control of AATVs remain limited, requiring research into accurate estimation of terrain mechanics, identifying uncertainties, and making fast and accurate decisions, as well as utilizing wireless communication and edge cloud computing. Furthermore, most of the developments are at the research level and have many practical limitations due to terrain and weather conditions. [ABSTRACT FROM AUTHOR]

This paper explores the transformative potential of Big Data and Artificial Intelligence (AI) in propelling the dairy industry toward net zero emissions, a critical objective in the global fight against climate change. Employing the Canadian dairy sector as a case study, the study extrapolates its findings to demonstrate the global applicability of these technologies in enhancing environmental sustainability across the agricultural spectrum. We begin by delineating the environmental challenges confronting the dairy industry worldwide, with an emphasis on greenhouse gas (GHG) emissions, including methane from enteric fermentation and nitrous oxide from manure management. The pressing need for innovative approaches in light of the accelerating climate crisis forms the crux of our argument. Our analysis delves into the role of Big Data and AI in revolutionizing emission management in dairy farming. This includes applications in optimizing feed efficiency, refining manure management, and improving energy utilization. Technological solutions such as predictive analytics for feed optimization, AI in herd health management, and sensor networks for real-time monitoring are thoroughly examined. Crucially, the paper addresses the wider implications of integrating these technologies in dairy farming. We discuss the development of benchmarking standards for emissions, the importance of data privacy, and the essential role of policy in promoting sustainable practices. These aspects are vital in supporting the adoption of technology, ensuring ethical use, and aligning with international climate commitments. Concluding, our comprehensive study not only suggests a pathway for the dairy industry towards environmental sustainability but also provides insights into the role of digital technologies in broader agricultural practices, aligning with global environmental sustainability efforts. [ABSTRACT FROM AUTHOR]

In the ever-evolving landscape of modern agriculture, the integration of advanced technologies has become indispensable for optimizing crop management and ensuring sustainable food production. This paper presents the development and implementation of a real-time AI-assisted push-broom hyperspectral system for plant identification. The push-broom hyperspectral technique, coupled with artificial intelligence, offers unprecedented detail and accuracy in crop monitoring. This paper details the design and construction of the spectrometer, including optical assembly and system integration. The real-time acquisition and classification system, utilizing an embedded computing solution, is also described. The calibration and resolution analysis demonstrates the accuracy of the system in capturing spectral data. As a test, the system was applied to the classification of plant leaves. The AI algorithm based on neural networks allows for the continuous analysis of hyperspectral data relative up to 720 ground positions at 50 fps. [ABSTRACT FROM AUTHOR]

This review article conducts an in-depth analysis of the role of next-generation technologies in soilless vegetable production, highlighting their groundbreaking potential to revolutionize yield, efficiency, and sustainability. These technologies, such as AI-driven monitoring systems and precision farming methods, offer unparalleled accuracy in monitoring critical variables such as nutrient concentrations and pH levels. However, the paper also addresses the multifaceted challenges that hinder the widespread adoption of these technologies. The high initial investment costs pose a significant barrier, particularly for small- and medium-scale farmers, thereby risking the creation of a technological divide in the industry. Additionally, the technical complexity of these systems demands specialized expertise, potentially exacerbating knowledge gaps among farmers. Other considerations are scrutinized, including data privacy concerns and potential job displacement due to automation. Regulatory challenges, such as international trade regulations and policy frameworks, are discussed, as they may need revision to accommodate these new technologies. The paper concludes by emphasizing that while these sustainable technologies offer transformative benefits, their potential for broad adoption is constrained by a complex interplay of financial, technical, regulatory, and social factors. [ABSTRACT FROM AUTHOR]

The coupling coordination of agricultural technology innovation with food security is of great significance for high-quality agricultural development. By identifying the coupling coordination relationship between the two systems and the influencing factors, this paper aims to promote the virtuous cycle of coordinated development between regional agriculture and technology, as well as accelerate the realisation of high-quality development of Chinese agriculture. Therefore, this paper explores the spatial and temporal coupling characteristics of the two using the entropy value method, coupling coordination degree model, and exploratory spatial data analysis, and it screens for important influencing factors using the grey correlation model. The main results show that ① the coupling coordination relationship between agricultural technology innovation and food security in China is at a dissonant stage, but the value of the coupling coordination degree increases from 0.2076 to 0.3437 during the period of study, and the level of coordination gradually improves. ② The degree of coupling coordination in the provincial space exhibits a distribution pattern of "high in the east and low in the west". The areas of high value are primarily situated in the provinces of Shandong, Jiangsu, and other provinces along the southeastern coast of China, while the areas of low value are mainly located in the provinces of Qinghai, Ningxia, and other provinces in inland northwest China. ③ The Moran's index of provincial coupling coordination is greater than 0, showing a certain positive correlation, and there is a significant pattern of spatial aggregation. ④ The correlation coefficients between the influencing factors and the degree of coupling coordination are all greater than 0.35, indicating a moderate or high correlation, but the significance of technological support capacity and food distribution security increased over time. [ABSTRACT FROM AUTHOR]

Agricultural practices such as tilling, sowing, cropping, It's duty but arvesting, and land-use patterns in any agrarian economy depend on climate. Therefore, any adverse climatic conditions can seriously affect the production or yield of crops. Increased temperature enhances the susceptibility of crops to pests and various plant diseases. Weeds are also known to multiply rapidly and decrease the nutritive value of soil, negatively affecting crop production. Our present study is designed to address similar problems faced by the farming community in the South-24 Parganas district of West Bengal, India, and suggest several probable technological solutions. Importantly, West Bengal is included in one of the six agro-climatic zones. Major crops from this study site are rice, wheat, maize, jute, green gram, black gram, pigeon pea, lentils, sugarcane, pulses, rapeseed, mustard, sesame, linseed, and vegetables. Significantly, cultivable land area has decreased in comparison to the overall crop area in this region. Reduced interest in agriculture, irrigation problems, increased profit in the non-agricultural economy, and rapid conversion of agricultural land for commercial purposes (construction of plots, hatcheries for fishing practices), along with uncertainties associated with rainfall patterns and frequent cyclones, are matters of grave concern in this study area. Agricultural scientists, researchers, environmentalists, local bodies, and government organizations are suggesting alternatives to benefit farmers. Thus, precision agriculture or crop management is required to recognize site-specific variables within agricultural lands and formulate strategies for improving decision-making regarding crop sowing, appropriate use of herbicides, weedicides, and precision irrigation, along with innovative harvesting technologies. Thus, the present paper provides a vision for the farming community in our study area to overcome their traditional practices and adopt different techniques of precision agriculture to increase flexibility, performance, accuracy, and cost-effectiveness. Soil temperature, humidity, and moisture monitoring sensors could be beneficial. Precision soil management, precision irrigation, crop disease management, weed management, and harvesting technologies are the different modules considered for discussion in this paper. Machine learning algorithms, such as decision tree, K-nearest neighbor (KNN), Gaussian naïve Bayes (GNB), K-means clustering, artificial neural network (ANN), fuzzy logic system (FLS), and support vector machine (SVM), could prove helpful for progressive farmers. The use of AI-powered weeding machines, drones, and UAVs for rapid weed removal and the localized application of herbicides and pesticides could also improve the accuracy and efficiency of agriculture. Utilizing drones fitted with high-resolution cameras could help gather precision field images, proving to be quite helpful in crop monitoring and crop health assessment. Unmanned driverless tractors and harvesting machines using robotics integrated with data from GPS/GIS sensors or radars could also be considered an effective and time-saving option. Thus, machine learning, along with innovative agricultural technologies, could contribute to improving the livelihoods of the farming fraternity. [ABSTRACT FROM AUTHOR]

The occurrence of late spring frosts due to climate change causes great damage to plantation production worldwide. The main objective of the paper is to provide a comprehensive overview of the problem and to outline effective protective measures against late spring frosts. The nature of frost depends on regional, altitudinal, and geographic differences, but they all share a common problem: they remove heat, resulting in the freezing of new plant growth and flowers. Tissue freezing is affected by critical temperatures and the frost type, intensity, and duration. Protection against late spring frosts can be broadly divided into three categories: active, passive, and chemical measures. In the field of agricultural engineering, various techniques have been thoroughly researched, and their effectiveness has been confirmed by research. These include various sprinkler systems, different heating devices, and large-diameter fans. Conclusive findings are being made on the performance of these systems in sub-zero temperatures and their cost-effectiveness. Climate change increases the importance of protecting permanent crops from late spring frosts and requires advances in agricultural technology to meet changing production demands and challenges. [ABSTRACT FROM AUTHOR]

The development of smart farming comes with a lot of data problems. Studies have shown this is due to insufficient cognition of the structural relationship between data and events. Shili Theory is an attractive concept. To embed intelligent agricultural technology in events and the natural environment, especially to unify and standardize agricultural production data, firstly, this paper has defined the concept of Shili Theory which researches the natural regularity of the event by Shili Mirrored Structure. Secondly, this paper has proposed a Shili Mirrored Structure based on the technology development path (from the human brain memory mechanism to the information storage mechanism to intelligent technology). Finally, the structure has been applied to develop an intelligent system of agricultural production data management. In rice production of Jilin Province, it forms the event chain of the whole plant 5T (seed, seeding, paddy shoot, grain, product period operation) and grain period 5T (harvesting, field stacking, drying, warehousing, storing). The system application shows that this management structure can reduce data flow, improve data utilization, and enhance the correlation between data and events. It can realize the quality improvement of the agricultural production process, especially revealing the 8.83% significant latent loss in rice harvest. [ABSTRACT FROM AUTHOR]

With the continuous development of digital agriculture and intelligent forestry, the demand for three-dimensional modeling of trees or plants using agricultural robots is also increasing. Laser radar technology has gradually become an important technical means for agricultural robots to obtain three-dimensional information about trees. When using laser radar to scan trees, incomplete point cloud data are often obtained due to leaf occlusion, visual angle limitation, or operation error, which leads to quality degradation of the subsequent 3D modeling and quantitative analysis of trees. At present, a lot of research work has been carried out in the direction of point cloud completion, in which the deep learning model is the mainstream solution. However, the existing deep learning models have mainly been applied to urban scene completion or the point cloud completion of indoor regularized objects, and the research objects generally have obvious continuity and symmetry characteristics. There has been no relevant research on the point cloud completion method for objects with obvious individual morphological differences, such as trees. Therefore, this paper proposes a single-tree point cloud completion method based on feature fusion. This method uses PointNet, based on point structure, to extract the global features of trees, and EdgeConv, based on graph structure, to extract the local features of trees. After integrating global and local features, FoldingNet is used to realize the generation of a complete point cloud. Compared to other deep learning methods on the open source data set, the CD index using this method increased by 21.772% on average, and the EMD index increased by 15.672% on average, which proves the effectiveness of the method in this paper and provides a new solution for agricultural robots to obtain three-dimensional information about trees. [ABSTRACT FROM AUTHOR]

Given the prevalence of part-time farming behaviors in rural households, studying the impact of part-time farming behaviors on grain output is of great practical significance. Using a panel dataset of 5629 Chinese national rural fixed observation point data from 2009 to 2015, this paper used the Propensity Score Matching-Difference in Differences method (PSM-DID) to examine the impact and dynamic effect of rural household's part-time farming behavior on grain output. In addition, the paper also aims to explore the mechanism of how part-time farming affects grain output by running an OLS in an intermediary effect model. We analyzed the data from two aspects: the effect of rural households' part-time farming decisions on grain output and the relationship between the income from rural households' part-time farming and grain output. After accounting for the control variables, the results showed a significant positive correlation between rural households' part-time farming behavior and grain output, with an influence coefficient of 0.304 tons increase in total grain output. While rural households' part-time farming behavior inhibited grain output over the short term, it ultimately promoted grain output in the long run. The promotion effect increased with the duration of the rural households' part-time farming. Moreover, the degree of part-time farming and its impact on grain output are mediated by agricultural labor inputs and agricultural technology inputs. Hence, differences in the employment times of rural households' part-time farming can lead to varying results in grain output. To improve the efficiency of grain output, this study recommends that the government implement policies that promote orderly rural households' part-time farming behavior. [ABSTRACT FROM AUTHOR]

From these studies, it is clear that the research focus with relation to harvesting machinery is on equipment intelligence, in addition to design improvements for crop-specific harvesting components. The main function of harvesting is the cutting, picking, or digging of mature crop seeds, fruits, stalks, leaves, root parts, or the whole plant. Therefore, efficient harvesting machinery is a comprehensive research area that integrates the fields of mechanical and biological material, information, and computers. [Extracted from the article]

Agricultural machinery services are an important guaranteed way to promote Conservation Agriculture. It is of great significance to study how to encourage farmers to choose agricultural machinery services to promote the standard implementation of Conservation Agriculture technology. In order to promote the implementation of Conservation Agriculture and improve the supply of agricultural machinery services, this paper identifies the stakeholders of normative Conservation Agriculture technology adoption behavior and the relationship between agricultural machinery service organizations, farmers and agriculture-related governments. An evolutionary game model was established to evaluate the decision-making characteristics of tripartite behavior and simulate the evolution trend of stakeholder behavior. The results show that agriculture-related governments, agricultural machinery service organizations and farmers can achieve evolutionarily stable strategies. The punishments and subsidies of agriculture-related governments and the supervision cost of all links of agricultural machinery social service organizations can significantly affect the behavior strategies of the three parties. The government set up reasonable subsidy and punishment mechanisms, and the agricultural machinery service organization controls the supervision cost of all links to ensure the stability of the three-party behavior strategy. This study provides theoretical guidance for scientific decision making and active cooperative development of the government, farmers and agricultural machinery service organizations and lays a foundation for countermeasures and suggestions to further promote farmers' implementation of Conservation Agriculture technology. [ABSTRACT FROM AUTHOR]

It is worthwhile to calculate the execution cost of a manipulator for selecting a planning algorithm to generate trajectories, especially for an agricultural robot. Although there are various off-the-shelf trajectory planning methods, such as pursuing the shortest stroke or the smallest time cost, they often do not consider factors synthetically. This paper uses the state-of-the-art Python version of the Robotics Toolbox for manipulator trajectory planning instead of the traditional D–H method. We propose a cost function with mass, iteration, and residual to assess the effort of a manipulator. We realized three inverse kinematics methods (NR, GN, and LM with variants) and verified our cost function's feasibility and effectiveness. Furthermore, we compared it with state-of-the-art methods such as Double A* and MoveIt. Results show that our method is valid and stable. Moreover, we applied LM (Chan λ = 0.1) in mobile operation on our agricultural robot platform. [ABSTRACT FROM AUTHOR]

Digital twin technology is expected to transform agriculture. By creating the virtual representation of a physical entity, it assists food producers in monitoring, predicting, and optimizing the production process remotely and even autonomously. However, the progress in this area is relatively slower than in industries like manufacturing. A systematic investigation of agricultural digital twins' current status and progress is imperative. With seventy published papers, this work elaborated on the studies targeting agricultural digital twins from overall trends, focused areas (including domains, processes, and topics), reference architectures, and open questions, which could help scholars examine their research agenda and support the further development of digital twins in agriculture. [ABSTRACT FROM AUTHOR]

In an era of connectivity, the Internet of Things introduces smart solutions for smart and sustainable agriculture, bringing alternatives to overcome the food crisis. Among these solutions, smart greenhouses support crop and vegetable agriculture regardless of season and cultivated area by carefully controlling and managing parameters like temperature, air and soil humidity, and light. Smart technologies have proven to be successful tools for increasing agricultural production at both the macro and micro levels, which is an important step in streamlining small-scale agriculture. This paper presents an experimental Internet of Things-based small-scale greenhouse prototype as a proof of concept for the benefits of merging smart sensing, connectivity, IoT, and mobile-based applications, for growing cultures. Our proposed solution is cost-friendly and includes a photovoltaic panel and a buffer battery for reducing energy consumption costs, while also assuring functionality during night and cloudy weather and a mobile application for easy data visualization and monitoring of the greenhouse. [ABSTRACT FROM AUTHOR]

The integration of Internet of Things (IoT) technology into agriculture has revolutionized farming practices by using connected devices and sensors to optimize processes and facilitate sustainable execution. Because most IoT devices have limited resources, the vital requirement to efficiently manage data traffic while ensuring data security in agricultural IoT solutions creates several challenges. Therefore, it is important to study the data amount that IoT protocols generate for resource-constrained devices, as it has a direct impact on the device performance and overall usability of the IoT solution. In this paper, we present a comprehensive study that focuses on optimizing data transmission in agricultural IoT solutions with the use of compression algorithms and secure technologies. Through experimentation and analysis, we evaluate different approaches to minimize data traffic while protecting sensitive agricultural data. Our results highlight the effectiveness of compression algorithms, especially Huffman coding, in reducing data size and optimizing resource usage. In addition, the integration of encryption techniques, such as AES, provides the security of the transmitted data without incurring significant overhead. By assessing different communication scenarios, we identify the most efficient approach, a combination of Huffman encoding and AES encryption, to strike a balance between data security and transmission efficiency. [ABSTRACT FROM AUTHOR]

Visual simultaneous localization and mapping (VSLAM) is a foundational technology that enables robots to achieve fully autonomous locomotion, exploration, inspection, and more within complex environments. Its applicability also extends significantly to agricultural settings. While numerous impressive VSLAM systems have emerged, a majority of them rely on static world assumptions. This reliance constrains their use in real dynamic scenarios and leads to increased instability when applied to agricultural contexts. To address the problem of detecting and eliminating slow dynamic objects in outdoor forest and tea garden agricultural scenarios, this paper presents a dynamic VSLAM innovation called MOLO-SLAM (mask ORB label optimization SLAM). MOLO-SLAM merges the ORBSLAM2 framework with the Mask-RCNN instance segmentation network, utilizing masks and bounding boxes to enhance the accuracy and cleanliness of 3D point clouds. Additionally, we used the BundleFusion reconstruction algorithm for 3D mesh model reconstruction. By comparing our algorithm with various dynamic VSLAM algorithms on the TUM and KITTI datasets, the results demonstrate significant improvements, with enhancements of up to 97.72%, 98.51%, and 28.07% relative to the original ORBSLAM2 on the three datasets. This showcases the outstanding advantages of our algorithm. [ABSTRACT FROM AUTHOR]

This document, titled "Smart Strategies and Technologies for Sustainability and Biodiversity in Herbaceous and Horticultural Crops," discusses the current trends in modern farming systems that focus on improving the sustainability and biodiversity of agroecosystems. The document highlights various innovative agronomical strategies and new technologies that can help farmers reduce chemical inputs, preserve soil and water quality, decrease emissions, prevent pollution, and lower energy demand. The special issue presented in the document includes 12 papers, with 5 focusing on tree crops/orchards and 7 on herbaceous crops. The papers cover topics such as innovative techniques for tree crops, weed control methods, conservation agriculture, organic farming, cover crops, intercropping, and precision farming technologies. The document concludes that these strategies and technologies can effectively enhance sustainability and biodiversity in herbaceous and horticultural crops in different farming scenarios. [Extracted from the article]

The article discusses the application of vision technology and artificial intelligence in smart farming. It highlights the use of image recognition algorithms, machine learning techniques, and remote sensing technology to monitor the growth and environments of plants and animals. The article also emphasizes the importance of accurate model recognition for disease detection and the challenges of implementing smart farming technology, such as cost and connectivity issues. Overall, the research papers in this special issue demonstrate the feasibility and potential benefits of using artificial intelligence and visual technologies in smart farming. [Extracted from the article]

LoRaWAN is a communication protocol designed especially for Internet of Things (IoT) applications that offers benefits such as long-distance connection and low power consumption. Due to the characteristics of LoRaWAN, this technology has gained great popularity in various IoT applications, such as environmental monitoring, smart agriculture, and applications in the areas of health and mobility, among others. Given this situation, the objective of this work is to provide an in-depth overview of LoRaWAN technology in terms of its applications, as well as the devices that have been used for the development of such applications. Additionally, this work reviews what other areas of LoRaWAN have been covered in different scientific articles, i.e., performance improvement and security. Among the main results of this study though analyzing previous works, we can say that most of them have been developed in the area of environmental monitoring and have used low-cost devices such as Arduinos, Raspberry Pis, and relatively low-cost commercial products such as those of the Semtech and STMicroelectronics brands. The analysis of the present work shows objectively and formally that LoRaWAN technology can be applied in various applications and that there are many studies that try to optimize its performance and security. This paper seeks to identify and describe the most relevant applications of LoRaWAN in different sectors, such as agriculture, health, and environmental monitoring, among others, and the challenges and solutions found in each area. This literature review will provide a valuable reference to understand the potential and opportunities offered by LoRaWAN technology. [ABSTRACT FROM AUTHOR]

The digital economy is a key driver of greener agriculture and sustainable development. This paper focuses on the impact of the digital economy on green agricultural development and the mediating role of green technology innovation. Using provincial panel data from China from 2011 to 2020, we test hypotheses using fixed effects models. The results indicate that: (a) the digital economy boosts agricultural total factor productivity (AGTFP); (b) green technology innovation positively moderates the relationship between the digital economy and AGTFP; and (c) the positive impact of the digital economy on green agriculture varies across regions, favoring eastern areas. [ABSTRACT FROM AUTHOR]

Rural industrial integration is the key to promoting the development of rural industrial restructuring and modernization, and plays a vital role in improving agricultural economic resilience. Based on the evaluation index system of agricultural economic resilience and the level of rural industrial integration development, respectively, this paper uses panel data from 30 provincial administrative regions in China from 2000 to 2020 to empirically test the impact of rural industrial integration development on agricultural economic resilience by using an individual fixed-effects model and a mediating-effects model. The results show that rural industrial integration significantly contributes to enhancing agricultural economic resilience, and the conclusion still holds after a series of robustness tests. There is dimensional and regional heterogeneity in the impact of rural industrial integration on agricultural economic resilience, and the strength of the promotion effect by dimension is in the order of adaptive adjustment capacity, transformation and innovation capacity, and resilience to recovery capacity. In the eastern and central regions, the promotion effect of rural industrial integration on agricultural economic resilience is more significant. In the main food-producing areas, the promotion effect of rural industrial integration on resilience to recovery is significantly slighter than that of non-food-producing regions. Mechanism analysis indicates that regional industrial structure optimization is an essential channel for rural industrial integration to enhance the resilience of the agricultural economy. On this basis, in order to strengthen agricultural economic resilience, provinces should actively explore differentiated industrial integration policies to enhance industrial structure optimization and upgrading, stimulate agricultural economic vitality, and foster the development of China's agricultural modernization. [ABSTRACT FROM AUTHOR]