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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]

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 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.)

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.

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]

Yancheng ranks the first in Jiangsu Province in terms of grain planting area and yield,and is strong in agricultural development. In order to further improve the level of agricultural modernization in Yancheng and to promote the transformation of agricultural digitization, we combed the current situation of agricultural digitalization transformation in Yancheng, and summarized the problems in the process of agricultural digitalization transformation in Yancheng from the aspects of infrastructure construction, industrial chain development, etc. On this basis, we proposed to strengthen the construction of digital agricultural infrastructure, adhered to the training and introduction of digital talents and the digital empowerment to activate the industrial chain, strengthened the ability of data acquisition, utilization and protection, adhered to the development of new formats and new models, and accelerated the construction of systems and standards and other future development suggestions. [ABSTRACT FROM AUTHOR]

Under the influence of the coronavirus disease and other factors, agricultural product prices show non-stationary and non-linear characteristics, making it increasingly difficult to forecast accurately. This paper proposes an innovative combinatorial model for Chinese hog price forecasting. First, the price is decomposed using the Seasonal and Trend decomposition using the Loess (STL) model. Next, the decomposed data are trained with the Long Short-term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA) models. Finally, the prepared data and the multivariate influence factors after Factor analysis are predicted using the gated recurrent neural network and attention mechanisms (AttGRU) to obtain the final prediction values. Compared with other models, the STL-FA-AttGRU model produced the lowest errors and achieved more accurate forecasts of hog prices. Therefore, the model proposed in this paper has the potential for other price forecasting, contributing to the development of precision and sustainable agriculture. [ABSTRACT FROM AUTHOR]

Addressing the urgent sustainability challenges in the wine industry, this study explores the efficacy of sustainability-oriented innovations (SOIs) and smart farming technologies (SFTs) across wine value chains in Cyprus and Italy. Utilising a mixed-methods approach that includes quantitative analysis through Key Performance Indicators (KPIs) and qualitative assessments to understand stakeholders' perspectives, this research delves into the environmental, economic, and social impacts of these technologies. In Cyprus, the integration of digital labelling and smart farming solutions led to a substantial reduction in pesticide usage by up to 75% and enhanced the perceived quality of wine by an average of 8%. A pilot study in Italy witnessed a 33.4% decrease in greenhouse gas emissions, with the additional benefit of a 5.3% improvement in intrinsic product quality. The pilot introduced a carbon credit system, potentially generating an average annual revenue of EUR 4140 per farm. These findings highlight the transformative potential of SOIs and SFTs in promoting sustainable practices within the wine industry, demonstrating significant advancements in reducing environmental impact, improving product quality, and enhancing economic viability. This study underscores the critical role of innovative technologies in achieving sustainability goals and provides a compelling case for their wider adoption within the agricultural sector. [ABSTRACT FROM AUTHOR]

Machine learning is a widespread technology that plays a crucial role in digitalisation and aims to explore rules and patterns in large datasets to autonomously solve non-linear problems, taking advantage of multiple source data. Due to its versatility, machine learning can be applied to agriculture. Better crop management, plant health assessment, and early disease detection are some of the main challenges facing the agricultural sector. Plant phenotyping can play a key role in addressing these challenges, especially when combined with machine learning techniques. Therefore, this study reviews available scientific literature on the applications of machine learning algorithms in plant phenotyping with a specific focus on sunflowers. The most common algorithms in the agricultural field are described to emphasise possible uses. Subsequently, the overview highlights machine learning application on phenotyping in three primaries areas: crop management (i.e., yield prediction, biomass estimation, and growth stage monitoring), plant health (i.e., nutritional status and water stress), and disease detection. Finally, we focus on the adoption of machine learning techniques in sunflower phenotyping. The role of machine learning in plant phenotyping has been thoroughly investigated. Artificial neural networks and stacked models seems to be the best way to analyse data. [ABSTRACT FROM AUTHOR]

During the past few decades, a range of digital strategies for Nitrogen (N) management using various types of input data and recommendation frameworks have been developed. Despite much research, the benefits accrued from such technology have been equivocal. In this work, thirteen methods for mid-season N recommendations in cereal production systems were evaluated simultaneously, ranging from simple mass balance through to non-mechanistic approaches based on machine learning. To achieve this, an extensive field research program was implemented, comprising twenty-one N strip trials implemented in wheat and barley fields across Australia over four cropping seasons. A moving window regression approach was used to generate crop response functions to applied N and calculate economically optimal N rates along the length of the strips. The N recommendations made using various methods were assessed based on the error against the optimal rate and expected profitability. The root mean squared error of the recommendations ranged from 15 to 57 kg/ha. The best performing method was a data-driven empirical strategy in which a multivariate input to characterise field and season conditions was abundantly available and used to predict optimal N rates using machine learning. This was the only approach with potential to substantially outperform the existing farmer management, reducing the recommendation error from 42 to 15 kg/ha and improving profitability by up to A$47/ha. Despite being reliant on extensive historical databases, such a framework shows a promising pathway to drive production systems closer towards season- and site-specific economically optimum recommendations. Automated on-farm experimentation is a key enabler for building the necessary crop response databases to run empirical data-driven decision tools. [ABSTRACT FROM AUTHOR]

Soil compaction is considered one of the main threats to structural soil degradation, and it promotes increased densification of soil particles, impairs ecosystem services, the plant development, and therefore affects agricultural profitability. In this sense, this study aimed to analyze the feasibility of using a Remotely Piloted Aircraft System (RPAS) by relating parameters derived from aerial images based on Vegetation Indices (VIs) and the Canopy Height Model (CHM) with soil compaction in a coffee plantation area. The study was conducted in a commercial coffee plantation with the cultivar Mundo Novo with 14 years of implantation. Two aerial surveys were carried out, the first to determine the CHM and define the sampling points and the second for radiometric calculations of VIs. In the sampling point were collected data plant height, soil characterization, soil penetration resistance and productivity. Images were processed by Pix4D software, and the data analysis at QGIS and RStudio. As at results, no statistically significant differences were detected between the different plant height zones in the soil chemical analysis; significant statistical differences between plant height zones were detected for penetration resistance, which is correlated to productivity data; and the radiometric data presented a correlation with the penetration resistance data, making it possible to determine VIs (NDRE and MTCI) with correlation to the compaction data allowing the estimation of such variable. In this way, the possibility of monitoring the height variations of the coffee crop using RPAS to demarcate compacted zones was evidenced. [ABSTRACT FROM AUTHOR]

In an era dominated by the intersection of advanced technology and traditional industries, the domain of agriculture is on the verge of a revolutionary transformation. This article introduces a solution for vineyard producers, harnessing satellite imagery, weather data, and deep learning (DL) to identify vineyard diseases robustly. This solution, designed for proactive plant health management, stands as a transformative tool towards digital viticulture. Such tools transition from luxuries to essentials as vineyards confront evolving challenges like climate change and new pathogens. Our research builds on the hypothesis that customising deep learning architectures for specific tasks is crucial in enhancing their effectiveness. We contribute by introducing a tailored convolutional neural network (CNN) architecture, developed specifically for the classification of plant diseases using vineyard imagery. The experimental results demonstrate that our custom CNN architecture exhibits performance on par with established state-of-the-art models like ResNet50 and MobileNetV2, underscoring the value of specialized solutions in addressing the unique challenges of viticulture. This paper introduces an overview of the solution's architecture, presents the implementation of DL modules with their corresponding results, and describes use case scenarios. [ABSTRACT FROM AUTHOR]

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.

ABSTRACT: 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.

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 closed-field 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.