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Aiming to accurately identify apple targets and achieve segmentation and the extraction of branch and trunk areas of apple trees, providing visual guidance for a picking robot to actively adjust its posture to avoid branch trunks for obstacle avoidance fruit picking, the spindle-shaped fruit trees, which are widely planted in standard modern apple orchards, were focused on, and an algorithm for apple tree fruit detection and branch segmentation for picking robots was proposed based on an improved YOLOv8s model design. Firstly, image data of spindle-shaped fruit trees in modern apple orchards were collected, and annotations of object detection and pixel-level segmentation were conducted on the data. Training set data were then augmented to improve the generalization performance of the apple detection and branch segmentation algorithm. Secondly, the original YOLOv8s network architecture's design was improved by embedding the SE module visual attention mechanism after the C2f module of the YOLOv8s Backbone network architecture. Finally, the dynamic snake convolution module was embedded into the Neck structure of the YOLOv8s network architecture to better extract feature information of different apple targets and tree branches. The experimental results showed that the proposed improved algorithm can effectively recognize apple targets in images and segment tree branches and trunks. For apple recognition, the precision was 99.6%, the recall was 96.8%, and the mAP value was 98.3%. The mAP value for branch and trunk segmentation was 81.6%. The proposed improved YOLOv8s algorithm design was compared with the original YOLOv8s, YOLOv8n, and YOLOv5s algorithms for the recognition of apple targets and segmentation of tree branches and trunks on test set images. The experimental results showed that compared with the other three algorithms, the proposed algorithm increased the mAP for apple recognition by 1.5%, 2.3%, and 6%, respectively. The mAP for tree branch and trunk segmentation was increased by 3.7%, 15.4%, and 24.4%, respectively. The proposed detection and segmentation algorithm for apple tree fruits, branches, and trunks is of great significance for ensuring the success rate of robot harvesting, which can provide technical support for the development of an intelligent apple harvesting robot. [ABSTRACT FROM AUTHOR]

With the profound integration of digital technology and traditional agriculture, the whole industry chain of rural e-commerce, as an advanced system, is reshaping the production, sales, and management models of agriculture and is emerging as a new catalyst for the advancement of digital agriculture through significant innovation. This paper focuses on the digital empowerment attributes and strategic attributes of the whole industrial chain of rural e-commerce, and evolutionary game models under market mechanisms and regulations are constructed. It examines the influence of various elements, such as the digital technology empowerment coefficient, on collaborative innovation within the whole industry chain of rural e-commerce. Using case simulations, this paper discusses the role of government regulation and intervention when market mechanisms are inadequate. The study reveals that factors such as the digital technology empowerment coefficient, technology absorptive capacity, and the amount of technology and information stored in collaborative innovation positively influence the whole industry chain. Potential risk losses and free-rider gains have negative effects on the evolution of the system toward collaborative innovation. When market mechanisms are inadequate, a combination of operational cost subsidies and incentive measures can yield more effective policy synergy, with the incentive impact of cost subsidies being particularly notable. The strategic application of enterprise-liquidated damage and government penalties will aid in regulating and directing corporate behavior. The findings of this research not only offer a new microlevel explanation for understanding the decision-making mechanism behind collaborative innovation in the whole industrial chain of rural e-commerce but also serve as a reference for government policy formulation to ensure the stability of the whole rural e-commerce industry chain. [ABSTRACT FROM AUTHOR]

Sustainable and substantial crop production is essential globally, especially considering the increasing population. To achieve this, selecting appropriate crops and applying necessary fertilizers are pivotal for ensuring satisfactory crop growth and productivity. Farmers have relied heavily on intuition when choosing which crops to cultivate and suitable fertilizers to use in a given season. However, this traditional approach often needs to consider the significant impact of current environmental and soil conditions on crop growth and yield. Overlooking these factors can have far-reaching consequences, impacting not just individual farmers and their households but also the entire agricultural sector. The integration of machine learning offers a promising avenue for addressing these challenges and providing practical solutions. The core contribution of this research lies in proposing a unified framework termed Machine Learning-enabled Crop Selection and Fertilizer Recommendation (ML-CSFR). This framework's primary objective is to predict appropriate crops accurately and subsequently suggest corresponding fertilizers based on specific agricultural conditions. The initial phase involves the identification of proper crops for individual farmlands, considering local input variables. This phase employs artificial neural networks (ANN) to filter crops effectively using the available choices. The next phase utilizes soil and environmental parameters to anticipate the optimal fertilizer for the selected crops. This phase leverages the XGBoost (XGB) model to predict the most suitable fertilizers accurately. This two-phase approach ensures a comprehensive and effective recommendation system for enhancing agricultural outcomes. Experimental results demonstrate the effectiveness of this framework, achieving an accuracy score of 99.10% using ANN and 97.66% for XGB. The framework's capability to deliver tailored recommendations for individual farms and its potential to integrate real-time sensor data positions it as an effective tool for improving agricultural decision-making. [ABSTRACT FROM AUTHOR]

This study focuses on the promising use of biospeckle technology to detect water stress in plants, a complex physiological mechanism. This involves monitoring the temporal activity of biospeckle pattern to study the occurrence of stress within the leaf. The effects of water stress in plants can involve physical and biochemical changes. Some of these changes may alter the optical scattering properties of leaves. The present study therefore proposes to test the potential of a biospeckle measurement to observe the temporal evolution in different varieties of sunflower plants under water stress. An experiment applying controlled water stress with osmotic shock using polyethylene glycol 6000 (PEG) was conducted on two sunflower varieties: one sensitive, and the other more tolerant to water stress. Temporal monitoring of biospeckle activity in these plants was performed using the average value of difference (AVD) indicator. Results indicate that AVD highlights the difference in biospeckle activity between day and night, with lower activity at night for both varieties. The addition of PEG entailed a gradual decrease in values throughout the experiment, particularly for the sensitive variety. The results obtained are consistent with the behaviour of the varieties submitted to water stress. Indeed, a few days after the introduction of PEG, a stronger decrease in AVD indicator values was observed for the sensitive variety than for the resistant variety. This study highlights the dynamics of biospeckle activity for different sunflower varieties undergoing water stress and can be considered as a promising phenotyping tool. [ABSTRACT FROM AUTHOR]

Summary: Agriculture has been affected by several global trends raising the concern for food security. Agri‐food demands are amplifying due to the ever‐escalating population numbers. Thus, the notion of extending the use of smart innovative technology in managing agricultural practices has emerged rapidly over the last decade. Technological innovations have contributed significantly to shape modern agriculture as smart agriculture. Smart agriculture unfolds various benefits such as increased production, real time data and production insights and remote monitoring. The rising advancements in Information and Communication Technologies (ICT) have paved the way for researchers to use these technologies in managing agricultural practices, leading to greater benefits for farmers and society. These innovations are the key to establishing agriculture as a research discipline. The purpose of this article is to conduct a scientometric analysis to study the structure and evolution of research activities in the field of smart agriculture. The scientometric analysis aims to empirically map the scientific knowledge and identify any possible challenges in the field. This study performs elementary analysis to study publication growth over the years, impact analysis to assess the leading journals, authors, and countries, and articles analysis for findings patterns among the citations over the years and among the keyword‐based clusters. There has been a considerable increase of more than 200% in the number of publications from 2011 to 2022. However, around 60% of authors have contributed with a single publication. The findings of the study reveal the research trends and hot topics for future research fronts. Deep learning, digital agriculture, object detection, blockchain, and semantic segmentation have been identified as trending topics in smart agriculture. Comprehensively, an intellectual view of the agriculture domain is presented as a scientific field in this article. [ABSTRACT FROM AUTHOR]

In the current study, an index system for digital agriculture growth was constructed. The index encompasses six key dimensions, namely production, operation, service, management, sustainability, and digital information infrastructure. Data from 30 Chinese provinces between 2011 and 2020 were collected and analyzed using the entropy method, Moran index, Dagum's Gini coefficient, and the kernel density estimate. An in-depth analysis of the development level and spatial patterns, dynamic evolution and intra- and inter-regional differences in China (i.e., eastern, western, and central regions) was conducted. From the result, an overall growing trend of digital agriculture in China was observed, with a relatively more advanced status in the eastern region. A positive spatial dependence, showing a "high-high" and "low-low" (HH, LL) trend, was obtained. However, the regional spatial dependence has generally weakened since 2019. The intra-regional differences were large in western and eastern areas, while the greatest inter-regional differences were unveiled between western and eastern regions. The country's overall differences mainly stemmed from inter-regional differences. The overall kernel density curves moved to the right over time, showing a trend of a gradual rise in digital agricultural growth, accompanied by a polarization pattern in the western region. [ABSTRACT FROM AUTHOR]

The digitalization of agriculture is rapidly changing the way farmers do business. With the integration of advanced technology, farmers are now able to increase efficiency, productivity, and precision in their operations. Digitalization allows for real-time monitoring and management of crops, leading to improved yields and reduced waste. This paper presents a review of some of the use cases that digitalization has made an impact in the automation of open-field and closedfield cultivations by means of collecting data about soils, crop growth, and microclimate, or by contributing to more accurate decisions about water usage and fertilizer application. The objective was to address some of the most recent technological advances that are leading to increased efficiency and sustainability of crop production, reduction in the use of inputs and environmental impacts, and releasing manual workforces from repetitive field tasks. The short discussions included at the end of each case study attempt to highlight the limitations and technological challenges toward successful implementations, as well as to introduce alternative solutions and methods that are rapidly evolving to offer a vast array of benefits for farmers by influencing cost-saving measures. This review concludes that despite the many benefits of digitalization, there are still a number of challenges that need to be overcome, including high costs, reliability, and scalability. Most of the available setups that are currently used for this purpose have been custom designed for specific tasks and are still too expensive to be implemented on commercial scales, while others are still in their early stages of development, making them not reliable or scalable for widespread acceptance and adoption by farmers. By providing a comprehensive understanding of the current state of digitalization in agriculture and its impact on sustainable crop production and food security, this review provides insights for policy-makers, industry stakeholders, and researchers working in this field. [ABSTRACT FROM AUTHOR]

Accurate oestrous detection is critical to optimise sows' reproductive performance. The conventional method of oestrous detection relies on the laborious back-pressure test. This study presents an automated oestrous detection method for sows housed in individual stalls using a robotic imaging system and neural networks. A robotic imaging system consisting of a LiDAR camera was used to monitor a group of stall-housed sows at a 10-min interval to capture their postures and vulva volume. Imagery data were analysed using a previously developed pipeline. Results showed that significant changes were observed in daily standing index, sternal lying index, lateral lying index, posture change frequency, and vulva volume before the onset of oestrous. A 1-D convolutional neural network model architecture for oestrous detection was developed using Days from Weaning (DFW), behaviour features, and vulva volume features as inputs. The oestrous detection models were evaluated using 10-fold cross validation. The training and testing accuracies of the oestrous detection model were 96.1 ± 2.0% and 92.3 ± 10.1% when using the DFW and behaviour features as input. The model's training and testing accuracies increased to 98.1 ± 2.4% and 98.0 ± 4.2% when vulva volume features were added to the input. While it is difficult to trace the behaviour of sows housed in group conditions, combining vulva volume features with DFW could be a suitable method to detect the onset of oestrous in these sows. The training and testing accuracies of this method of oestrous detection were 97.9 ± 1.4% and 95.2 ± 4.8%. However, further validation under real group house conditions is needed. • An automated estrus detection method for stall- and group-housed sows. • Combining vulva volume and behaviour features can improve detection accuracy. • Method showing potential for automated early pregnancy diagnosis for sows. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Disciplines Economics & Administrative Scienves Studies is the property of International Journal of Disciplines in Economics & Administrative Sciences Studies 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.)

This paper explores the potential of smart crop management based on the incorporation of tools like digital agriculture, which considers current technological tools applied in agriculture, such as the Internet of Things (IoT), remote sensing, and artificial intelligence (AI), to improve crop production efficiency and sustainability. This is essential in the context of varying climatic conditions that affect the availability of resources for agriculture. The integration of tools such as IoT and sensor networks can allow farmers to obtain real-time data on their crops, assessing key health factors, such as soil conditions, plant water status, presence of pests, and environmental factors, among others, which can finally result in data-based decision-making to optimize irrigation, fertilization, and pest control. Also, this can be enhanced by incorporating tools such as drones and unmanned aerial vehicles (UAVs), which can increase monitoring capabilities through comprehensive field surveys and high-precision crop growth tracking. On the other hand, big data analytics and AI are crucial in analyzing extensive datasets to uncover patterns and trends and provide valuable insights for improving agricultural practices. This paper highlights the key technological advancements and applications in smart crop management, addressing challenges and barriers to the global adoption of these current and new types of technologies and emphasizing the need for ongoing research and collaboration to achieve sustainable and efficient crop production. [ABSTRACT FROM AUTHOR]

In unstructured agricultural fields where autonomous navigation is challenging and demands additional safety, the operator’s experience and knowledge are essential for supervising operations and making decisions beyond the robot’s autonomous capabilities. Local networks with long-range wireless communication combined with digital twin concepts are promising solutions that can be used for robot teleoperation. The purpose of this study was to demonstrate the feasibility of supervising a mobile robot inside berry orchards using a digital shadow from a long-range distance (between 300 and 3000 m), with the primary objective of assisting the robot in navigating in complex situations such as row-end turning. This involved creating a virtual representation of the robot that mirrors its state and actions, allowing the remote operator to monitor and guide the robot effectively. The system comprised a GPS-based navigation controller with collision avoidance sensors, two sets of LoRa transmitters and repeaters, a simulation environment with a digital shadow of the robot, and a graphical user interface for the remote operator. Information about the digital shadow’s state, including location, orientation, and distances to obstacles, was received as a message by the LoRa gateway and was used to update the path for the actual robot that interfaced with the Robot Operating System (ROS). The main research hypothesis aimed to test the quality of the LoRa communication link between the robot and the operator, as well as the robustness of the robot’s control system, with an emphasis on the architecture, communication link, and situation awareness creation. Preliminary results showed that depending on the environment, the average packet loss was 12% at distances of approximately 2300 m. Our results highlight some of the core technical challenges that need to be addressed for an effective teleoperation system, including latency, stability, and the limited range of wireless communication. Future works involves evaluating the performance and reliability of the proposed method under different field conditions and scenarios, as well as considering the use of the 5G network for a significant improvement in data transmission speed, navigation efficiency, and visual feedback. Upon successful implementation, this study has the potential to enhance the efficiency and safety of robot navigation, providing a practical solution for remote supervision in challenging environments.Article highlights: Development of a digital shadow mobile robot: Designed and implemented a simulated mobile robot with that can follow a user-defined reference trajectory and generate a path. Enhanced Teleoperation: LoRa-based Internet of Robotic Things for efficient teleoperation of an agricultural mobile robot in complex environments for remote agricultural supervision. Evaluation of LoRa Connectivity: The study reveals issues like latency, stability, and limited range in LoRa communication that need addressing for effective teleoperation systems. [ABSTRACT FROM AUTHOR]

Abstract This research focuses on the importance of management strategies, green innovation, and sustainable practices in the agricultural sector. These factors are crucial for job creation, food security, and environmental conservation. Particularly in water-scarce regions, effective management is necessary to overcome natural resource constraints and encourage a shift towards digital agriculture (AGRI). The study aims to identify and analyze the challenges and issues related to agricultural research and technology in Asian countries. The collected data were analyzed using exploratory factor analysis in the SPSS software. The analysis revealed a range of issues and challenges for agricultural development, including those related to the structure and policy framework, the availability and quality of resources and infrastructures, and the provision of effective support services, all of which encompassed factors such as research and technology investment, research management, productivity, research culture, networking, and the integration of higher education and agricultural research. To estimate the efficiency of technology development, agricultural development, and support services for AGRI, an artificial neural network (ANN) was utilized. The ANN was trained by incorporating changes in management strategies, green innovation, and sustainability across a broader range of experimental scenarios. The evaluation of the ANN's predictions showed that improvements in management strategies and the adoption of green innovation and sustainability significantly impacted the productivity of technology development, agricultural development, and support services for AGRI. The accuracy of the ANN's predictions was further assessed using linear regression. The results indicated an acceptable level of error when compared to the target results obtained from experimental tests. Overall, this study emphasizes the importance of effective management, green innovation, and sustainable practices in driving advancements in technology and agricultural development.

This study reviewed the advancements of Agriculture 4.0 in mechanized sugarcane harvesting, analyzing the efficiency of the mechanized harvesting system and evaluating the impact of the harvesting, loading, and transportation process costs. The mechanized sugarcane harvesting system in the 2020/21 harvest reached 91.6% or approximately 588 Mg in Brazil. As this activity demands a high amount of capital - assets, labor, inputs - the cost of the harvesting, loading, and transporting process represents 40% of the sugarcane production cost. The continuous delivery of raw material to the industrial processing unit is essential to ensure stability and maximum yield for obtaining sugar and ethanol, especially in an environment with geographical dispersion of harvesting fronts and agronomic factors linked to crop management. Therefore, tools that enable real-time equipment tracking benefit process management. Digital agriculture, defined as the application of technologies that seek to add intelligence and process automation, allows for improved agricultural management of farms, making it possible to reach new productivity levels in the field. The data generated through digital agriculture allows for the creation of models to construct scenarios that improve decision-making, both in terms of operational strategies - distribution of resources by harvesting front - and long-term strategies - the adoption of new technologies in the process. [ABSTRACT FROM AUTHOR]

The implementation of advanced digital, intelligent production technologies and robotic systems contributes to the achievement of the goal set for the agro-industrial complex for transition to a highly productive, environmentally friendly agricultural economy. Taking this into account, as well as the global trends in the development of unmanned mobile vehicles, three conceptual models for the development of unmanned mobile power tools are proposed: the creation of universal unmanned mobile vehicles of various traction classes and power based on commercially available tractors (conceptual model A), the creation of universal unmanned low-power mobile devices working in groups (conceptual model B) and the creation of energy modules (conceptual model C). In order to determine further prospects for their use, theoretical studies have been carried out on the issue of evaluating the productivity of agricultural aggregates in combination with unmanned mobile vehicles of the proposed conceptual models. The research was carried out on the basis of existing well-known methods and formulas for determining productivity. The analysis of factors affecting the productivity of an agricultural unit during field operations shows that when using unmanned mobile vehicles, an increase in the productivity of the unit can be ensured by increasing the utilization factor of the width of the grip and the time of main work by eliminating the time spent on rest and personal needs of the operator, reducing the time spent when turning the unit at the end of the rut. The developed methodological approaches to the issue of calculating the productivity of agricultural aggregates in combination with unmanned mobile agricultural vehicles made it possible to assess the increase in the productivity of aggregates using unmanned mobile means of the proposed conceptual models. The use of unmanned mobile vehicles of the considered conceptual models can increase the replaceable productivity of units for continuous cultivation compared with traditional manned tractors by a level from 3 to 24 %.

Making plant breeding programs less expensive, fast, practical, and accurate, especially for soybeans, promotes the selection of new soybean genotypes and contributes to the emergence of new varieties that are more efficient in absorbing and metabolizing nutrients. Using spectral information from soybean genotypes combined with nutritional information on secondary macronutrients can help genetic improvement programs select populations that are efficient in absorbing and metabolizing these nutrients. In addition, using machine learning algorithms to process this information makes the acquisition of superior genotypes more accurate. Therefore, the objective of the work was to verify the classification performance of soybean genotypes regarding secondary macronutrients by ML algorithms and different inputs. The experiment was conducted in the experimental area of the Federal University of Mato Grosso do Sul, municipality of Chapadão do Sul, Brazil. Soybean was sown in the 2019/20 crop season, with the planting of 103 F2 soybean populations. The experimental design used was randomized blocks, with two replications. At 60 days after crop emergence (DAE), spectral images were collected with a Sensifly eBee RTK fixed-wing remotely piloted aircraft (RPA), with autonomous takeoff control, flight plan, and landing. At the reproductive stage (R1), three leaves were collected per plant to determine the macronutrients calcium (Ca), magnesium (Mg), and sulfur (S) levels. The data obtained from the spectral information and the nutritional values of the genotypes in relation to Ca, Mg, and S were subjected to a Pearson correlation analysis; a PC analysis was carried out with a k-means algorithm to divide the genotypes into clusters. The clusters were taken as output variables, while the spectral data were used as input variables for the classification models in the machine learning analyses. The configurations tested in the models were spectral bands (SBs), vegetation indices (VIs), and a combination of both. The combination of machine learning algorithms with spectral data can provide important biological information about soybean plants. The classification of soybean genotypes according to calcium, magnesium, and sulfur content can maximize time, effort, and labor in field evaluations in genetic improvement programs. Therefore, the use of spectral bands as input data in random forest algorithms makes the process of classifying soybean genotypes in terms of secondary macronutrients efficient and important for researchers in the field.

Introduction. Wireless sensor networks are a part of information and communication technologies and the basis of the Internet of Things technologies. Data are collected, transmitted and processed in real time with the wireless sensor networks. The typical WSN consists of the large number wireless sensor nodes and the coordinator. The wireless network is based on wireless communication standards. Today, WSNs are used in the variety of industries such as medicine, military and digital agriculture. The purpose is to provide a comprehensive analysis of a wireless sensor node for use in digital agriculture. Results. The author analysed wireless communication standards for use in digital agriculture. The typical wireless node architecture is analysed. The parameters of the wireless node are identified. The types of Energy Harvesting Systems (EHS) and their functions are reviewed and analysed. Based on the requires of the wireless node for digital agriculture, the list of photovoltaic energy converters is proposed. In addition, the list of power management integrated circuits is suggested for the Energy Harvesting System. A Classification of Power Supplies for Wireless Sensor Nodes and Networks are developed. The types of sensors for use in digital agriculture are analysed. Conclusions. The main wireless communication standards for digital agriculture are Wi-Fi, IEEE 802.15.4, ZigBee, Bluetooth 4.0, GPRS, 4G, LoRaWAN and NB-IoT. The parameters of the wireless node are identified for further modelling. The list of photovoltaic energy converters is given. The types of power management integrated circuits are proposed. The classification enable to simplify the selection of WSN power supplies for network development. Based on the analysis of sensor parameters for digital agriculture, it is concluded that practically there are no wireless sensors on the world market for the express estimation of plants state.

Citrus pests pose a major threat to both citrus yield and fruit quality. The early prevention of pests is essential for sustainable citrus cultivation, cost savings, and the reduction of environmental pollution. Despite the increasing application of deep learning techniques in agriculture, the performance of existing models for small target detection of citrus pests is limited, mainly in terms of information bottlenecks that occur during the transfer of information. This hinders its effectiveness in fully automating the detection of citrus pests. In this study, a new approach was introduced to overcome these limitations. Firstly, a comprehensive large-scale dataset named IP-CitrusPests13 was developed, encompassing 13 distinct citrus pest categories. This dataset was amalgamated from IP102 and web crawlers, serving as a fundamental resource for precision-oriented pest detection tasks in citrus farming. Web crawlers can supplement information on various forms of pests and changes in pest size. Using this comprehensive dataset, we employed the SPD Module in the backbone network to preserve fine-grained information and prevent the model from losing important information as the depth increased. In addition, we introduced the AFFD Head detection module into the YOLOv8 architecture, which has two important functions that effectively integrate shallow and deep information to improve the learning ability of the model. Optimizing the bounding box loss function to WIoU v3 (Wise-IoU v3), which focuses on medium-quality anchor frames, sped up the convergence of the network. Experimental evaluation on a test set showed that the proposed SAW-YOLO (SPD Module, AFFD, WIoU v3) model achieved an average accuracy of 90.3%, which is 3.3% higher than the benchmark YOLOv8n model. Without any significant enlargement in the model size, state-of-the-art (SOTA) performance can be achieved in small target detection. To validate the robustness of the model against pests of various sizes, the SAW-YOLO model showed improved detection performance on all three scales of pests, significantly reducing the rate of missed detections. Our experimental results show that the SAW-YOLO model performs well in the detection of multiple pest classes in citrus orchards, helping to advance smart planting practices in the citrus industry. [ABSTRACT FROM AUTHOR]

Copyright of Egyptian Journal of Agricultural Sciences is the property of Egyptian National Agricultural Library (ENAL) 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.)

Copyright of Journal of Agricultural Economics & Social Sciences is the property of Egyptian National Agricultural Library (ENAL) 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.)

Making plant breeding programs less expensive, fast, practical, and accurate, especially for soybeans, promotes the selection of new soybean genotypes and contributes to the emergence of new varieties that are more efficient in absorbing and metabolizing nutrients. Using spectral information from soybean genotypes combined with nutritional information on secondary macronutrients can help genetic improvement programs select populations that are efficient in absorbing and metabolizing these nutrients. In addition, using machine learning algorithms to process this information makes the acquisition of superior genotypes more accurate. Therefore, the objective of the work was to verify the classification performance of soybean genotypes regarding secondary macronutrients by ML algorithms and different inputs. The experiment was conducted in the experimental area of the Federal University of Mato Grosso do Sul, municipality of Chapadão do Sul, Brazil. Soybean was sown in the 2019/20 crop season, with the planting of 103 F2 soybean populations. The experimental design used was randomized blocks, with two replications. At 60 days after crop emergence (DAE), spectral images were collected with a Sensifly eBee RTK fixed-wing remotely piloted aircraft (RPA), with autonomous takeoff control, flight plan, and landing. At the reproductive stage (R1), three leaves were collected per plant to determine the macronutrients calcium (Ca), magnesium (Mg), and sulfur (S) levels. The data obtained from the spectral information and the nutritional values of the genotypes in relation to Ca, Mg, and S were subjected to a Pearson correlation analysis; a PC analysis was carried out with a k-means algorithm to divide the genotypes into clusters. The clusters were taken as output variables, while the spectral data were used as input variables for the classification models in the machine learning analyses. The configurations tested in the models were spectral bands (SBs), vegetation indices (VIs), and a combination of both. The combination of machine learning algorithms with spectral data can provide important biological information about soybean plants. The classification of soybean genotypes according to calcium, magnesium, and sulfur content can maximize time, effort, and labor in field evaluations in genetic improvement programs. Therefore, the use of spectral bands as input data in random forest algorithms makes the process of classifying soybean genotypes in terms of secondary macronutrients efficient and important for researchers in the field. [ABSTRACT FROM AUTHOR]

With the emergence of social distancing, the shutdown of schools and restaurants, and increased anxieties about in- person farmers markets in early 2020, local farmers and food distributors quickly pivoted to digital tools to manage their farms and connect with buyers. In this paper, we explore the role of a seemingly simple digital tool in shaping alternative agrarian relations during the pandemic- induced local food boom: the spreadsheet. Through interviews with farmers and food distributors across urban, periurban, and rural landscapes in the United States (U.S.), we show how spreadsheets and other digital tools have helped farmers manage demand during the COVID- 19 pandemic while also presupposing costs, benefits, and efficiencies for these alternative agricultural food spaces. Ultimately, many local farmers found themselves pursuing goals of simplicity, labor efficiency, and expansion- oriented growth as they made sense of their farm data. Reflecting on these values, and the spreadsheet data underlying them, became a point of tension for farmers who place a high importance on diversification, stability, and interpersonal interactions. By attending to how these data are tracked, we gain deeper insights into how farmers and other stakeholders think about agrarian futures: who does the work, what gets planted, who's buying, and who benefits. [ABSTRACT FROM AUTHOR]

Genomic breeding programs have been paramount in improving the rates of genetic progress of productive efficiency traits in livestock. Such improvement has been accompanied by the intensification of production systems, use of a wider range of precision technologies in routine management practices, and high-throughput phenotyping. Simultaneously, a greater public awareness of animal welfare has influenced livestock producers to place more emphasis on welfare relative to production traits. Therefore, management practices and breeding technologies in livestock have been developed in recent years to enhance animal welfare. In particular, genomic selection can be used to improve livestock social behavior, resilience to disease and other stress factors, and ease habituation to production system changes. The main requirements for including novel behavioral and welfare traits in genomic breeding schemes are: (1) to identify traits that represent the biological mechanisms of the industry breeding goals; (2) the availability of individual phenotypic records measured on a large number of animals (ideally with genomic information); (3) the derived traits are heritable, biologically meaningful, repeatable, and (ideally) not highly correlated with other traits already included in the selection indexes; and (4) genomic information is available for a large number of individuals (or genetically close individuals) with phenotypic records. In this review, we (1) describe a potential route for development of novel welfare indicator traits (using ideal phenotypes) for both genetic and genomic selection schemes; (2) summarize key indicator variables of livestock behavior and welfare, including a detailed assessment of thermal stress in livestock; (3) describe the primary statistical and bioinformatic methods available for large-scale data analyses of animal welfare; and (4) identify major advancements, challenges, and opportunities to generate high-throughput and large-scale datasets to enable genetic and genomic selection for improved welfare in livestock. A wide variety of novel welfare indicator traits can be derived from information captured by modern technology such as sensors, automatic feeding systems, milking robots, activity monitors, video cameras, and indirect biomarkers at the cellular and physiological levels. The development of novel traits coupled with genomic selection schemes for improved welfare in livestock can be feasible and optimized based on recently developed (or developing) technologies. Efficient implementation of genetic and genomic selection for improved animal welfare also requires the integration of a multitude of scientific fields such as cell and molecular biology, neuroscience, immunology, stress physiology, computer science, engineering, quantitative genomics, and bioinformatics. [ABSTRACT FROM AUTHOR]

Digitalization of agriculture is one of the priorities of the EU's rural development strategy "From Field to Table", which promotes the creation of more added value and climate change mitigation in agriculture. A growing body of the literature argues that digitalization enables better information management, reduces production costs, and increases the potential for farm income growth, but only a few papers provide empirical studies on how digitalization improves the performance of small farms. To fill this gap in the literature, this paper presents a case study as empirical evidence of the impact of digital innovation on smallholder performance through a sustainable development lens. This paper reports research based on a pilot digitalization project implemented on a small organic farm. It examines the identification of logical links between the digitalization processes introduced and the impact of digitalization on the economic, social, and environmental performance of the small farm. The case study data were collected through semi-structured interviews and based on the results of a pilot project. The findings of this study provide evidence that the introduction of digital technologies has improved the economic performance of the farm, including a reduction in labor costs, improved customer relations, improvements in farmers' investment planning, and process redesign. Based on this study, recommendations are made to policymakers on how to promote the uptake of digital technologies in smallholder farming. [ABSTRACT FROM AUTHOR]

With the rapid development of digital information technology, digital agriculture has become the engine to promote the development of high-quality agriculture. Digital agriculture can not only promote the sustainable development of precision agriculture, but also improve the ability of agricultural management and decision-making. However, the popularization and application of digital technology in agriculture in our country lags behind, and the investment of digital agriculture technology is small, the talents are short, and the popularization degree of digital technology is low, the development of digital agriculture is restricted to some extent by such factors as agricultural decentralized management. Therefore, in order to promote the development of high quality agriculture, we must overcome the resistance that restricts the development of digital agriculture and activate the motive force of the development of digital agriculture. Based on the analysis of the current situation and restrictive factors of digital agriculture, this paper puts forward the theoretical logic and practical path for digital agriculture to promote the development of high-quality agriculture. [ABSTRACT FROM AUTHOR]

Accurate and timely estrus detection is one of the critical factors for improving the conception rate of sows in swine reproduction. Estrus detection in the current industry relies on experienced breeding technicians using the back pressure test method, which is time-consuming, labor-intensive, and subjective. This study aimed to develop an image-processing pipeline that could predict the onset of estrus by monitoring the variation of the vulva volume of sows. A robotic imaging system consisting of a LiDAR camera was used to collect depth and infrared (IR) images of sows multiple times per hour. The developed image processing pipeline consisted of a deep learning model (MobileNet) to exclude images with defective postures (e.g., excreting posture, vulva region not showing) for vulva detection. The selected images were further processed to (1) segment sow vulva regions (3-dimensional surface) using a U-Net model, (2) exclude incorrectly segmented 3D vulva surfaces using a MobileNet model, and (3) calculate the vulva volume using the segmented 3D vulva surface. Results show that all three models achieved high test accuracy of 94.8% (MobileNet 1), 96.7% (U-Net), and 100% (MobileNet 2). The vulva volume was calculated using the developed pipeline for all the test sows. Daily average vulva volume showed a noticeable increase in all sows at 0-1 day prior to the onset of estrus. This study concluded that the developed image processing pipeline could automatically detect vulva swelling, which may be used as a biological sign indicating the onset of post-weaning estrus in sows. [ABSTRACT FROM AUTHOR]

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This survey aimed to acquire and generate significant information on coffee cultivation in high and low elevations to support agriculture during climate change. This survey dataset helps understand coffee cultivation in highland and lowland areas with diverse climates and environmental conditions for coffee researchers to use this data to improvecultivation and production techniques. In the business scope, this dataset provides a critical vision on the value proposition of the coffee business to maintain conservation and wealth creation of the coffee chain. Similarly, coffee chains can use this data as an example to assess sustainability and carbon literacy. The structured interviews and field trips were conducted at coffee plantations in southern and northern Thailand. The transcript results were manually coded for thematic analysis. This dataset offers insights into anthropogenic plant migration and plant distribution for researchers and academics to use as a valuable resource and good reference in agricultural and biodiversity research. Today, agriculture faces many challenges, such as climate change, water shortage, and improper land management. This information on coffee cultivation at high and low altitudes may help others grow crops in ever-changing climates.

Virtual fencing (VF) technology is gaining interest due to its potential to facilitate sustainable grazing management. It allows farmers to contain grazing livestock without physical fences, thereby reducing the time and labour associated with the implementation of conventional fences. From a conservation perspective, some sensitive areas within uplands should not be grazed during certain periods of the year, and VF provides an invisible and moveable fence line that can exclude livestock from these areas. However, there are also concerns associated with its use, including animal welfare impacts, cost-effectiveness, and public perception. The extent to which VF can contribute to make livestock systems more sustainable remains to be investigated. To address this gap, this study investigates the potential of VF to promote sustainable grazing management using the Efficiency, Substitution, and Redesign framework, which has been used for the first time in this context. The framework is particularly relevant in taking an active and normative approach to identify key aspects to focus on to help achieve sustainability. We consulted stakeholders including farmers, wildlife inspectors, veterinarians, policy officers, researchers, NGOs, farm advisors or certification managers, through focus groups (N = 4) and in-depth, semi-structured interviews (N = 5). Stakeholders have highlighted the potential of VF to provide new opportunities to increase the efficiency and sustainability of livestock grazing systems, enabling their redesign, and contributing to improved environmental and animal welfare outcomes, as well as higher financial and social performance. However, there are important aspects that remain to be addressed to achieve such redesign, including issues of reliability due to poor network signal, animals’ ability to learn, biosecurity and safety issues related to the absence of physical fences, farm suitability and farmers’ ability to use the systems effectively. This study highlights the need to ensure that the development and uptake of VF are mutually beneficial to farmers, animals, and the wider farming industry. This includes a highlight on the importance of participative approaches to involve key stakeholders to address concerns and maximise the potential of the technology.

The growing disparity between food supply and demand requires innovative Digital Agriculture (DA) systems to increase farm sustainability and profitability. However, current systems suffer from problems of complexity stemming from the challenge of integrating diverse, often non-interoperable hardware and software components. In order to tackle these complexities to increase farm efficiency and understand the tradeoffs of these new DA innovations we developed Realtime Optimization and Management System (ROAM), which is a decision-support system developed to find a Pareto optimal architectural design to build DA systems. To find the Pareto optimal solution, we employed the Rhodium Multi-Objective Evolutionary Algorithm (MOEA), which systematically evaluates the trade-offs in DA system designs. Based on data from five live deployments at Cornell University, each DA design can be analyzed based on user defined objectives and evaluated under uncertain farming environments with ROAM. Paired with this, we develop a web interface that allows users to define personalized decision spaces and visualize decision tradeoffs. To help validate ROAM, it was deployed to a commercial farm where the user was recommended a DA architecture design method to increase farm efficiency. ROAM allows users to quickly make key decisions in designing their DA systems to increase farm profitability.

The transition from traditional agriculture to Digital Agriculture involves various elements that make it challenging. Given the different contexts and particularities, this paper aims to identify and analyze the primary factors and barriers to adopting Digital Agriculture, to promote an understanding of this transition. By conducting a bibliometric assessment and analyzing case studies, the results show that the economic condition, availability of technological infrastructure, technical knowledge, age of farmers, type of organization, reliability of technology, and concerns about security and privacy are important elements in adopting Digital Agriculture. The study also reveals a limitation linked to the data sources used in their preparation by analyzing the case studies. This finding reveals the existence of a gap in the literature concerning the scarcity of indicators capable of measuring the adoption of digital agriculture while at the same time providing a perspective devoid of producer bias. Furthermore, by considering the insights provided by the identification and analysis of those factors and barriers, policymakers can tailor policies to address specific challenges and promote the widespread adoption of digital technologies in agriculture.

The purpose of this paper is to further strengthen the belief within the academic, professional and policy making communities about the need to speed up the adoption of Agriculture 4.0 technologies in the Romanian farms as the grassroot constituents of the national agriculture in order to secure their future competitiveness in the context of new European agricultural policies and regulations. A review of the most influential papers and the bibliometric mapping of articles on change management as well as ways to accelerate change processes through change agents is discussed. Agrinnovator is the think tank of young digital agriculture practitioners in the Romanian farms. Their public policy proposal to create The Farm Technology Officers Program in order to accelerate digitalization through relevant change agents is also discussed. Their interviews highlighted the need for government support in creating scale and impetus for the digitalization of agriculture. It is deemed to be a structural countrywide change that will happen faster if the adequate change agents are used.

Digitalisation in agriculture is transforming the way farming is practised worldwide, and its potential benefits for smallholder farmers in sub-Saharan Africa (SSA) are particularly promising. Yet, scientific evidence on the digital technologies utilised by smallholder food producers in SSA and the associated challenges still needs improvement. This review attempts to provide a thorough overview of the technologies currently being employed by smallholder farmers in SSA while also exploring the associated challenges and opportunities. Through a systematic literature search, 27 relevant studies were analysed to identify the region-wise current technologies, challenges, and opportunities. Results show that various digital technologies are employed, including digital extension services and digital marketing of agricultural products. These technologies improve access to information and markets and enhance productivity. However, challenges hinder widespread adoption. Limited internet connectivity, low digital literacy, inadequate infrastructure, and affordability issues impede progress. Gender disparities further limit the equitable distribution of digitalisation benefits. Despite these challenges, significant opportunities arise from adopting digital technologies. The potential advantages are market access, better decision-making capabilities, and increased income and livelihoods. Digitalisation offers transformative possibilities for smallholder farmers in SSA. Overcoming barriers such as limited connectivity and low digital literacy is crucial. By harnessing the opportunities digital technologies can offer, the livelihoods of smallholder farmers can be uplifted, contributing to the growth of agriculture in SSA.

AbstractThe study examined how the use of digital agriculture solutions affected farm worker pay and on-farm employment. Data gathered, was from 1,199 randomly sampled Small-scale farmers in the Bono East Region of Ghana. To account for potential selection bias resulting from unobservable factors that can influence the adoption of digital agriculture solutions, an endogenous switching regression model was employed. The results show that farmer demographic characteristics (age), farm characteristics (farm size and contract with employees), and institutional support services (group membership, access to training, access to credit and access to agricultural extension services) influence the adoption of digital agricultural solutions by Small-scale farmers in the Bono East region. We provide evidence that a high household income, a large farm size, access to information and participation in FBOs encourage farmers to employ more farm workers and pay higher wages. The study reveals that digital agricultural solutions have the potential to reduce the number of farm workers employed by 52% as well as reduce the amount of wages paid farm workers by 2%. The findings suggest the need for government and development partners to devise strategies that would increase the financial endowment of households and Farmers by making credit more available, expanding the availability of farmers productive resources like land, and increasing household income through livelihood diversification strategies. This move can enhance the use of digital farming technologies and empower farmers to increase on-farm employment and wages.