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Research on Blockchain implementation in the Pharmaceutical Supply Chains (PSC) is lacking despite its strong potential to overcome conventional supply chain challenges. Thus, this study aims to provide critical insight into the nexus between Blockchain and PSC and further build a conceptual framework for implementation within the pharmaceutical industry. Following a systematic literature review and text mining approach, 65 interdisciplinary articles published between 2010 and 2021 were studied to capture the decade long developments. Descriptive and thematic analysis showcases nascent developments of Blockchain in PSC. The drivers and barriers to adoption, implementation stages, and applications identified through the thematic analysis guide in setting the agenda for future research, primarily focussing on the use of Blockchain for drug counterfeiting, recall issues, along with other sector-specific challenges such as patient privacy, regulations and clinical trials. Research on Blockchain for PSC has been slow compared to other sectors, but has accelerated since the Covid-19 pandemic. Identified influential factors, implementation process and apparent applications are expected to influence researchers and practitioners in developing a roadmap for adopting Blockchain in the pharmaceutical industry. The proposed conceptual framework is novel and provides valuable directions to producers, regulators and governments to implement Blockchain in the pharmaceutical industry. [ABSTRACT FROM AUTHOR]

Grading of oil palm fresh fruit bunches (FFB) plays a vital role in the postharvest operation as it directly influences the extraction rate of oil palm, thereby ensuring quality control in the estate and mill. Currently, manual grading based on visual assessment is employed, but it has limitations mainly due to subjective judgment-influenced factors such as visual resemblance, light intensities, and differences in colors across ripeness categories. Hence, an automated oil palm fruit grading system in postharvest technology is proposed to enhance the grading process and improve productivity while maintaining operational cost efficiency. This work involves developing an improved object detection model based on the You Only Look Once model to accurately identify four grades of oil palm FFB, namely, ripe, unripe, underripe, and overripe. The proposed model incorporated several improvements, including a hybrid feature extractor comprising mobile inverted bottleneck module and densely connected neural network. Additionally, it employs a spatial pyramid pooling structure to expand the receptive field and utilizes the complete intersection over union function for bounding box regression. The results indicate that the proposed model obtains a remarkable mAP of 94.37% and an F1-score of 0.89. Besides, the model performs real-time detection at a faster rate of 4.8 FPS on a limited-capacity embedded device, NVIDIA Jetson Nano. The comprehensive experimental results confirm the superiority of the proposed model over various detection models. [ABSTRACT FROM AUTHOR]

Modern agriculture relies heavily on the precise application of chemicals such as fertilisers, herbicides, and pesticides, which directly affect both crop yield and environmental footprint. Therefore, it is crucial to assess the accuracy of precision sprayers regarding the spatial location of spray deposits. However, there is currently no fully automated evaluation method for this. In this study, we collected a novel dataset from a precision spot spraying system to enable us to classify and detect spray deposits on target weeds and non-target crops. We employed multiple deep convolutional backbones for this task; subsequently, we have proposed a robustness testing methodology for evaluation purposes. We experimented with two novel data augmentation techniques: subtraction and thresholding which enhanced the classification accuracy and robustness of the developed models. On average, across nine different tests and four distinct convolutional neural networks, subtraction improves robustness by 50.83%, and thresholding increases by 42.26% from a baseline. Additionally, we have presented the results from a novel weakly supervised object detection task using our dataset, establishing a baseline Intersection over Union score of 42.78%. Our proposed pipeline includes an explainable artificial intelligence stage and provides insights not only into the spatial location of the spray deposits but also into the specific filtering methods within that spatial location utilised for classification. [ABSTRACT FROM AUTHOR]

High need for precision agriculture today has been associated with the completeness of the data available. The application of smart technology becomes the main alternative for fulfilling incomplete data and predicting future data. This paper presents the results of a research that aims to monitor and to predict water availability for determining conservation strategy on water availability using time series data from satellite rainfall, spanning from 2007 to 2022. We employed the innovative use of remote sensing technology, global rainfall measurement (GPM) and Climate Hazards Group InfraRed Rainfall with Station data (CHIRPS), to assess and correct satellite-derived rainfall estimates against ground-based observations. Conducted in the Serang watershed of Mount Merbabu’s volcanic landscape, the research employs a quantitative descriptive approach. We used coupled climate models MIROC6 and MRI-ESM2-0 in the CMIP-6 Framework for rainfall projections 2023–2030, then continued with the F.J. Mock model simulations for water availability projection. Our findings reveal a significant impact of climate change on water availability over the decade, with the most extreme conditions observed in 2029 and 2030, where the increasing of water availability reaching 10 m3 /s. The results showed that CHIRPS performed well in describing rainfall data. The novelty of this research highlighting the potential of: firstly, the use of satellite rainfall data for this specific region has not been extensively studied before; secondly, the discovered impacts of climate change to the water availability are particularly noteworthy, and thus contributing to the sustainability of agricultural practices in response to climate change. A limitation of this study is the short investigation period of just one decade, which does not fully capture the long-term impacts of climate change. Future research is recommended to utilize satellite data over extended periods to better represent extreme climate events and derive drought and wetness patterns over durations exceeding one decade. [ABSTRACT FROM AUTHOR]

Precision agriculture, a transformative farming approach, has gained prominence due to advancements in digital technologies. This paper explores the multifaceted landscape of precision agriculture, focusing on its tangible benefits, challenges, and future directions. Purpose: Amidst the growing interest in precision agriculture, this paper aims to provide a comprehensive analysis of its various aspects. Specifically, it seeks to elucidate the benefits of precision agriculture in optimizing resource utilization, enhancing crop health, and promoting sustainability. Moreover, it examines the challenges faced in its implementation and proposes future directions to overcome these obstacles. Findings: Through a review of existing literature and case studies, this paper presents a nuanced understanding of precision agriculture's impact on crop farming, livestock production, economic outcomes, and environmental sustainability. It identifies key challenges such as data security, implementation costs, and regulatory frameworks, while also highlighting innovative solutions and promising advancements in the field. Originality: To the best of our knowledge, this paper represents a rigorous attempt to comprehensively analyze the landscape of precision agriculture, with a focus on its original contributions to the field. By synthesizing existing research and offering insights into future directions, it adds to the emerging knowledge base surrounding precision agriculture and its potential to revolutionize modern farming practices. [ABSTRACT FROM AUTHOR]

ABSTRACT It has been observed that the agriculture sector has picked exponential growth with the help of the integration of advanced technology like wireless sensor networks (WSNs), the Internet of Things (IoT), and machine learning (ML). They have not only created new opportunities but also boosted the efficiency and productivity in the sector. Out of so many significant hurdles, accurately pinpointing the positions of the sensor nodes and agricultural assets like crops, livestock, and machinery within the field is one major challenge. Merging ML with a WSN‐assisted IoT (WIoT) network has offered a promising solution to overcome the localization challenge for smart agriculture. This integration will greatly benefit agricultural practices, such as precision agriculture, fast decision‐making, accurate positioning of sensor nodes, monitoring, and managing real‐time data effectively. This paper comprehensively discusses state‐of‐the‐art techniques and methodologies employed in localization within WIoT networks for smart agriculture. It explores various ML algorithms, which include reinforcement learning, supervised learning, and unsupervised learning, to perform the localization activity precisely. Moreover, it explores how fusion technologies like WSN, IoT, ML, and sensors will enhance localization accuracy and reliability in various agriculture activities. Additionally, the paper discusses the application of localization in the agriculture sector, such as crop monitoring, livestock management, precision agriculture, environmental conditions, and crop monitoring. It also discusses the challenges and obstacles in the path domain of various evaluation parameters like energy efficiency, scalability, and robustness, along with research and societal implications. Overall, the paper provides valuable guidance and outlines potential directions for future research in ML‐driven localization in WIoT for smart agriculture, offering a clear roadmap for researchers. [ABSTRACT FROM AUTHOR]

Although plant disease recognition has witnessed a significant improvement with deep learning in recent years, a common observation is that current deep learning methods with decent performance tend to suffer in real-world applications. We argue that this illusion essentially comes from the fact that current plant disease recognition datasets cater to deep learning methods and are far from real scenarios. Mitigating this illusion fundamentally requires an interdisciplinary perspective from both plant disease and deep learning, and a core question arises. What are the characteristics of a desired dataset? This paper aims to provide a perspective on this question. First, we present a taxonomy to describe potential plant disease datasets, which provides a bridge between the two research fields. We then give several directions for making future datasets, such as creating challenge-oriented datasets. We believe that our paper will contribute to creating datasets that can help achieve the ultimate objective of deploying deep learning in real-world plant disease recognition applications. To facilitate the community, our project is publicly available at https://github.com/xml94/PPDRD with the information of relevant public datasets. [ABSTRACT FROM AUTHOR]

Lameness in dairy cattle is a clinical sign of impaired locomotion, mainly caused by painful foot lesions, compromising the US dairy industry's economic, environmental, and social sustainability goals. Combining technology and on-farm data may be a more precise and less labor-intensive lameness detection tool, particularly for early detection. The objective of this observational study was to describe the association between average weekly autonomous camera-based (AUTO) locomotion scores and hoof trimming (HT) data. The AUTO data were collected from 3 farms from April 2022 to March 2023. Historical farm HT data were collected from March 2016 to March 2023 and used to determine cow lesion history and date of HT event. The HT events were categorized as a regular HT (TRIM; n = 2,290) or a HT with a lesion recorded (LESION; n = 670). Events with LESION were subcategorized based on lesion category: digital dermatitis (DD; n = 276), sole ulcer (SU; n = 79), white line disease (WLD; n = 141), and other (n = 174). The data also contained the leg of the LESION, classified as front left (FL; n = 54), front right (FR; n = 146), rear left (RL; n = 281), or rear right (RR; n = 183) leg with 6 events missing the leg. Cows' HT histories were classified as follows: cows with no previous recorded instance of any lesion were classified as TRIM0 (n = 1,554). The first instance of any hoof lesion was classified as LESION1 (n = 238). This classification was retained until a subsequent TRIM occurred—recorded as TRIM1 (n = 632). The next unique instance of any lesion following a TRIM1 was classified as LESION2 (n = 86). Any LESION events occurring after LESION1 or LESION2 without a subsequent TRIM were considered a hoof lesion recurrence and classified as LESIONRE1 (n = 164) and LESIONRE2 (n = 22), respectively. TRIM events after LESION2 or LESION2RE (n = 104) or LESION events after LESIONRE1 or LESIONRE2 were classified as LESION_OTHER (n = 160). The AUTO scores from −28 to −1 d prior to the HT event were summarized into weekly scores and included if cows had at least 1 observation per week in the 4 wk before the event. For all weeks, LESION cows had a higher median AUTO score than TRIM cows. Cows with TRIM0 had the lowest and most consistent median weekly score compared to LESION and other TRIM classifications. Before HT cows with TRIM0 and TRIM1, both had median score increases of 1 across the 4 wk, whereas the LESION categories had an increase of 4 to 8. Scores increased with each subsequent LESION event compared to the previous LESION event. Cows with SU lesions had the highest median score across the 4 wk, WLD had the largest score increase, and DD had the lowest median score and score increase. When grouping a LESION event by leg the hoof lesion was found on, the AUTO scores for 4 groups displayed comparable median values. Due to the difference between TRIM and LESION events, this technology shows potential for the early detection of hoof lesions. The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. [ABSTRACT FROM AUTHOR]

Agriculture faces significant challenges due to climate change and unpredictable environmental factors, which impact crop yields and threaten food security. This study proposes a novel approach to crop yield prediction and risk assessment using neutrosophic logic and least squares regression. By integrating these methods, we aim to improve accuracy in predicting crop losses under uncertain conditions. The model classifies crops based on profitability and environmental risks, utilizing the independence test to evaluate the relationships between crop attributes. Our approach leverages deep learning techniques, such as restricted Boltzmann machines (RBM), to enhance the analysis of crop data and provide farmers with actionable insights for decisionmaking. The results demonstrate that the proposed method effectively estimates crop yield damage with the ability to assess risk under varying conditions, such as extreme weather. This approach can help farmers optimize crop management strategies, minimize losses, and improve overall productivity. Future work will explore the application of this method to other crops and farming systems to expand its utility. [ABSTRACT FROM AUTHOR]

Background: The study focuses on enhancing the effectiveness of precision agriculture through the application of deep learning technologies. Precision agriculture, which aims to optimize farming practices by monitoring and adjusting various factors influencing crop growth, can greatly benefit from artificial intelligence (AI) methods like deep learning. The Agro Deep Learning Framework (ADLF) was developed to tackle critical issues in crop cultivation by processing vast datasets. These datasets include variables such as soil moisture, temperature, and humidity, all of which are essential to understanding and predicting crop behavior. By leveraging deep learning models, the framework seeks to improve decision-making processes, detect potential crop problems early, and boost agricultural productivity. Results: The study found that the Agro Deep Learning Framework (ADLF) achieved an accuracy of 85.41%, precision of 84.87%, recall of 84.24%, and an F1-Score of 88.91%, indicating strong predictive capabilities for improving crop management. The false negative rate was 91.17% and the false positive rate was 89.82%, highlighting the framework's ability to correctly detect issues while minimizing errors. These results suggest that ADLF can significantly enhance decision-making in precision agriculture, leading to improved crop yield and reduced agricultural losses. Conclusions: The ADLF can significantly improve precision agriculture by leveraging deep learning to process complex datasets and provide valuable insights into crop management. The framework allows farmers to detect issues early, optimize resource use, and improve yields. The study demonstrates that AI-driven agriculture has the potential to revolutionize farming, making it more efficient and sustainable. Future research could focus on further refining the model and exploring its applicability across different types of crops and farming environments. [ABSTRACT FROM AUTHOR]

Tall fescue (Festuca arundinacea) originated in Europe and North Africa and has evolved significantly, with a plethora of distinct varieties tailored to different environments. Despite widespread cultivation in the United States (US) since the 1970s, challenges remain, particularly the presence of the endophytic fungus, Epichloë coenophiala, leading to animal health concerns and a subsequent decline in cultivation. However, coordinated breeding programs aim to develop endophyte-free tall fescue varieties, propelling the field forward. This comprehensive review offers valuable insights into the multifaceted world of tall fescue cultivation, from its historical roots to cutting-edge genetic engineering efforts, fostering a deeper understanding and appreciation for this critical forage grass species in the US. Progress in improving the dry matter digestibility of tall fescue, increasing the genetic diversity of turf-type tall fescue, and using precision farming methods to maximize grassland productivity are highlighted in this review. Additionally, genetic engineering technologies for breeding tall fescue are reviewed, with an emphasis on creating varieties resistant to environmental stresses such as heat and drought. The difficulties and innovations in this field are also discussed. [ABSTRACT FROM AUTHOR]

There are many difficulties in growing tomatoes, such as erratic weather patterns, variable yields, and short shelf lives. Tomatoes are a staple food that is grown all throughout the world, particularly in climate-friendly areas such as Morocco. These areas are not free from the difficulties that present major obstacles for farmers, though. Modern agricultural techniques are increasingly using cutting-edge technologies, such as Internet of Things (IoT)-enabled smart greenhouses, to address these problems. In order to improve tomato output, this study presents an enhanced smart greenhouse model that incorporates the zoning principle. The method provides optimal plant growth and resource efficiency by separating the greenhouse into various zones, each tailored for specific growth stages and environmental circumstances. Precision farming and sustainable practices are made possible by the suggested systems real-time monitoring and control, which integrates sensors, actuators, and data analytics. The automatic irrigation system of the intelligent greenhouse prototype is additionally set up to offer the best possible support for the growth of tomato plants. The functionality and efficacy of the prototype are investigated through extensive laboratory testing and experimentation, providing insight into its potential influence on agricultural practices and practical practicality. The greenhouse that has been created with the help of IoT capabilities and the zoning concept has proven to be an effective instrument in assisting farmers in their attempts to grow tomatoes in an efficient and sustainable manner. Our comprehensive testing yielded data that demonstrate the zoning principle-integrated IoT-enabled smart greenhouse greatly improves tomato quality and production. This system demonstrates the benefits and practical applicability of sophisticated data-driven automation in agriculture by providing a scalable and sustainable solution to the problems encountered by tomato producers. [ABSTRACT FROM AUTHOR]

Peanut production plays a crucial role in global food security, particularly in developing countries, where it provides essential nutrition and income. This paper examines the optimization of peanut production through genetic advancements, climate adaptation strategies, and sustainable practices. The primary objective is to increase yields by addressing challenges related to climate change, pests, and resource constraints. Globally, peanut production is hindered by rising temperatures, irregular rainfall, and declining soil quality, impacting both yield and quality. Developing countries, especially in Africa and Asia, face additional challenges, such as limited access to advanced agricultural technologies, inadequate infrastructure, and insufficient support for smallholder farmers. The vital issues include genetic vulnerabilities to pests, climate stress, and inefficient water use. Recent genetic research has provided insights into breeding more resilient, drought-resistant varieties, offering hope for improving yields, despite environmental challenges. The adoption of climate adaptation strategies, precision farming, and integrated pest management is essential for boosting productivity. These, along with optimized irrigation and nutrient management, have significantly impacted peanut production in resource-limited settings. Additionally, drought-resistant varieties have proven crucial, enabling farmers to increase resilience and yields in areas facing climate stress. In conclusion, optimizing peanut production requires continued investment in genetic advancements, infrastructure, and sustainable practices. Future efforts should focus on improving climate adaptation and sustainable farming techniques for long-term success. [ABSTRACT FROM AUTHOR]

Introduction: Monitoring crop spike growth using low-altitude remote sensing images is essential for precision agriculture, as it enables accurate crop health assessment and yield estimation. Despite the advancements in deep learningbased visual recognition, existing crop spike detection methods struggle to balance computational efficiency with accuracy in complex multi-scale environments, particularly on resource-constrained low-altitude remote sensing platforms. Methods: To address this gap, we propose FDRMNet, a novel feature diffusion reconstruction mechanism network designed to accurately detect crop spikes in challenging scenarios. The core innovation of FDRMNet lies in its multi-scale feature focus reconstruction and lightweight parameter-sharing detection head, which can effectively improve the computational efficiency of the model while enhancing the model's ability to perceive spike shape and texture.FDRMNet introduces a Multi-Scale Feature Focus Reconstruction module that integrates feature information across different scales and employs various convolutional kernels to capture global context effectively. Additionally, an Attention-Enhanced Feature Fusion Module is developed to improve the interaction between different feature map positions, leveraging adaptive average pooling and convolution operations to enhance the model's focus on critical features. To ensure suitability for low-altitude platforms with limited computational resources, we incorporate a Lightweight Parameter Sharing Detection Head, which reduces the model's parameter count by sharing weights across convolutional layers. Results: According to the evaluation experiments on the global wheat head detection dataset and diverse rice panicle detection dataset, FDRMNet outperforms other state-of-the-art methods with mAP@.5 of 94.23%, 75.13% and R2 value of 0.969, 0.963 between predicted values and ground truth values. In addition, the model's frames per second and parameters in the two datasets are 227.27,288 and 6.8M, respectively, which maintains the top three position among all the compared algorithms. Discussion: Extensive qualitative and quantitative experiments demonstrate that FDRMNet significantly outperforms existing methods in spike detection and counting tasks, achieving higher detection accuracy with lower computational complexity. The results underscore the model's superior practicality and generalization capability in real-world applications. This research contributes a highly efficient and computationally effective solution for crop spike detection, offering substantial benefits to precision agriculture practices. [ABSTRACT FROM AUTHOR]

Purpose: Organizational resilience is a prerequisite to ensure preparedness and quick recovery from disruptions for an organization. However, there is scant literature that examines antecedents that are associated with resilience. This study aims to evaluate antecedents of organizational resilience considering three factors: sustainable supply chain management (SSCM), business model adaptation (BMA) and digitalization. Design/methodology/approach: This study considers the dynamic capability view as a theoretical lens to formulate the hypotheses between the mentioned constructs. The authors test the hypotheses based on the data collected from 259 manufacturing companies. The authors consider structural equation modeling and mediation analysis to analyze the proposed hypotheses. Findings: The results of structural equation modeling suggest that there is a positive association between SSCM and organizational resilience. Furthermore, the results suggest that digitalizing the business processes and deploying an adaptable business model can help organizations enhance their resilience levels. Originality/value: This study contributes to the literature by identifying dynamic capabilities that can influence organizational resilience and extending the relevance of BMA and digitalization in the context of sustainability and resilience. Furthermore, this research informs managers on effective pathways for managing future disruptions. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to comprehensively assess how digital maturity degree (DMD) impacts sustainable supply chain management (SSCM) performance through the mediating role of SSCM practices in businesses in Vietnam. Design/methodology/approach: The hypotheses were performed using partial least square-structural equation modeling (PLS-SEM) with data collected from a survey of over 234 managers having responsibility in the supply chain field in Vietnam. Qualitative data were collected through semistructured interviews with 6 experts to deepen understanding of the relationship between DMD and SSCM. Findings: The results show the mix-results in the relationship between SSCM practices and SSCM performance dimensions while DMD strongly impacts SSCM practices. Also, this study finds the mediating role of SSCM practices on the relationship between DMD and SSCM performance. Originality/value: This is the first study to investigate the role of DMD on SSCM practices and SSCM performance, using empirical evidence. Moreover, the authors integrate both qualitative and quantitative for understanding complex SSCM phenomena. The present study also helps businesses improve their SSCM performance by leveraging SSCM practices and developing their digital technologies in the long-term view. [ABSTRACT FROM AUTHOR]

Precision farming data enhance agricultural productivity by informing site-specific resource management. These benefits may be capitalized into the underlying value of the farmland, raising rental rates. This article uses a stated preference choice experiment to estimate farmers' willingness to pay for farm data in farmland rental markets. Farmers are willing to pay a small premium to acquire data accrued by previous operators, depending on the field type and quality information provided by the landowner and farmers' use of precision agriculture technology. We find evidence that farm data confer both a "management value" and a "signaling value" to prospective tenants. [ABSTRACT FROM AUTHOR]

Path planning is one of the key elements for achieving rapid and stable flight when unmanned aerial vehicles (UAVs) are conducting monitoring and inspection tasks at ultra-low altitudes or in orchard environments. It involves finding the optimal and safe route between a given starting point and a target point. Achieving rapid and stable flight in complex environments is paramount. In environments characterized by high-density obstacles, the stability of UAVs remains a focal point in the research of path planning algorithms. This study, utilizing a feature attention mechanism, systematically identifies distinctive points on the obstacles, leading to the development of the RFA-Star (R5DOS Feature Attention A-star) path planning algorithm. In MATLAB, random maps were generated to assess the performance of the RFA-Star algorithm. The analysis focused on evaluating the effectiveness of the RFA-Star algorithm under varying obstacle density conditions and different map sizes. Additionally, comparative analyses juxtaposed the performance of the RFA-Star algorithm against three other algorithms. Experimental results indicate that the RFA-Star algorithm demonstrates the shortest computation time, approximately 84%-94% faster than the RJA-Star algorithm and 51%-96% faster than the Improved A-Star. The flight distance is comparable to the RJA-Star algorithm, with slightly more searched nodes. Considering these factors collectively, the RFA-Star algorithm exhibits a relatively superior balance between computational efficiency and path quality. It consistently demonstrates efficient and stable performance across diverse complex environments. However, for comprehensive performance enhancement, further optimization is necessary. [ABSTRACT FROM AUTHOR]

Imitation learning (IL), a burgeoning frontier in machine learning, holds immense promise across diverse domains. In recent years, its integration into robotics has sparked significant interest, offering substantial advancements in autonomous control processes. This paper presents an exhaustive insight focusing on the implementation of imitation learning techniques in agricultural robotics. The survey rigorously examines varied research endeavors utilizing imitation learning to address pivotal agricultural challenges. Methodologically, this survey comprehensively investigates multifaceted aspects of imitation learning applications in agricultural robotics. The survey encompasses the identification of agricultural tasks that can potentially be addressed through imitation learning, detailed analysis of specific models and frameworks, and a thorough assessment of performance metrics employed in the surveyed studies. Additionally, it includes a comparative analysis between imitation learning techniques and conventional control methodologies in the realm of robotics. The findings derived from this survey unveil profound insights into the applications of imitation learning in agricultural robotics. These methods are highlighted for their potential to significantly improve task execution in dynamic and high-dimensional action spaces prevalent in agricultural settings, such as precision farming. Despite promising advancements, the survey discusses considerable challenges in data quality, environmental variability, and computational constraints that IL must overcome. The survey also addresses the ethical and social implications of implementing such technologies, emphasizing the need for robust policy frameworks to manage the societal impacts of automation. These findings hold substantial implications, showcasing the potential of imitation learning to revolutionize processes in agricultural robotics. This research significantly contributes to envisioning innovative applications and tools within the agricultural robotics domain, promising heightened productivity and efficiency in robotic agricultural systems. It underscores the potential for remarkable enhancements in various agricultural processes, signaling a transformative trajectory for the sector, particularly in the realm of robotics and autonomous systems. [ABSTRACT FROM AUTHOR]

The sorghum agricultural sector in Al-Qadarif State, Sudan, plays a pivotal role in ensuring regional food security. However, its productivity is hindered by significant constraints. Substantial improvements in sorghum yield are impeded by the intricate complexities that characterize this sector's financial resources, and agricultural practices. This study in Al-Qadarif State, Sudan, explores the relationship between financial resources and sorghum agrarian productivity. Data were collected from 200 sorghum farmers using standardized questionnaires during the 2022 season, and regression analysis was employed to investigate the association between financial factors and sorghum productivity. The findings indicate that financial accessibility considerations significantly influence sorghum productivity. This emphasizes the importance of improved financial resources for farmers to adopt innovative farming technologies and practices, leading to higher sorghum productivity levels. The study recommends the integration of digital financial services and precision agriculture technologies as a creative solution to enhance financial accessibility and sorghum productivity. By leveraging mobile money platforms and digital credit services, farmers can conveniently access financial resources, enabling them to invest in modern agricultural inputs and technologies. Additionally, the adoption of precision agriculture techniques, such as soil moisture sensors, drone-based crop monitoring, and variable rate application of inputs, can optimize resource utilization, increase yield, and reduce production costs. The combination of these innovative approaches has the potential to bridge the financial and knowledge gaps, promoting the widespread adoption of sustainable agricultural practices. This synergistic approach could significantly improve sorghum productivity, enhance farmer livelihoods, and contribute to regional food security. [ABSTRACT FROM AUTHOR]

The detection of apple leaf diseases plays a crucial role in ensuring crop health and yield. However, due to variations in lighting and shadow, as well as the complex relationships between perceptual fields and target scales, current detection methods face significant challenges. To address these issues, we propose a new model called YOLO-Leaf. Specifically, YOLO-Leaf utilizes Dynamic Snake Convolution (DSConv) for robust feature extraction, employs BiFormer to enhance the attention mechanism, and introduces IF-CIoU to improve bounding box regression for increased detection accuracy and generalization ability. Experimental results on the FGVC7 and FGVC8 datasets show that YOLO-Leaf significantly outperforms existing models in terms of detection accuracy, achieving mAP50 scores of 93.88% and 95.69%, respectively. This advancement not only validates the effectiveness of our approach but also highlights its practical application potential in agricultural disease detection. [ABSTRACT FROM AUTHOR]

The measurement of the macronutrient values of an oil palm plantation is a complex and tedious task, particularly when dealing with large plantation areas. This situation complicates the process of the conventional measurement of nutrients by taking samples of oil palm leaves in the area being observed, causing delays in fertilizer recommendation and a lack of spatial diversity observation. Precision agriculture (PA) principles and approaches, which focus on assessing temporal and spatial variability, can be used to improve conventional measurement methods in terms of both accuracy and speed. This research aims to determine macronutrients, specifically nitrogen (N), phosphorus (P), and potassium (K) contents in oil palm leaves based on PA principles using the integration of remote sensing technology and machine learning to quickly obtain the macronutrient status from oil palm plantation areas. The Sentinel‐1A and Sentinel‐2A imagery data were analyzed and extracted to produce selected features, which are most influencing in the correlation between the imagery data and the leaf macronutrient values obtained from laboratory analysis. The random forest regression (RFR) model is used to produce correlation functions to compute macronutrient values. The use of the two satellites is to cope with cloud and smoke interference. The prototype system developed, named PreciPalm (Precision Agriculture Platform for Oil Palm), has been validated and implemented based on 2000 leaf sampling units representing several oil palm plantation areas in Indonesia, including Java, Sumatra, and Kalimantan. The observed system performance resulted in the measurement accuracy of 95.02%, 93.50%, and 82.52% for the nutrients N, P, and K, respectively. The novelty of PreciPalm is that it provides an ecosystem to transparently measure and observe the macronutrient status of oil palm in a timely, visual, spatial, and location‐specific manner, thereby improving oil palm nutritional management with more certainty and precision. [ABSTRACT FROM AUTHOR]

Image processing has shown to be a well-organized tool for analysis in a variety of domains and applications. Agricultural Image Processing is one of for the most part extensively used applications of this technique and it is the fastest-growing study topic, with application in syndrome finding, classification, and recognition. It is used in agricultural fields to identify crops, plant life, vegetation, flowers, fruits, and other objects, as well as to recognize the disease. The plant's image is used as the input image. Digital image processing is a procedure for visualizing the excellence of a picture. CNN is utilized for picture segmentation in the abundant leaf detection method. Precision agriculture is regarded as a way to enlarge the agriculture product's habitual recognition. In comparison to previous methods, the parameter analysis has proven to be accurate and time-saving. The factors that necessitate plant infectivity are discussed, as well as the type of infection that affects vegetation. In this research, a small number of dissimilar traditions of studying the cause and impact of plant infection. [ABSTRACT FROM AUTHOR]

Accurate coffee plant counting is a crucial metric for yield estimation and a key component of precision agriculture. While multispectral UAV technology provides more accurate crop growth data, the varying spectral characteristics of coffee plants across different phenological stages complicate automatic plant counting. This study compared the performance of mainstream YOLO models for coffee detection and segmentation, identifying YOLOv9 as the best-performing model, with it achieving high precision in both detection (P = 89.3%, mAP50 = 94.6%) and segmentation performance (P = 88.9%, mAP50 = 94.8%). Furthermore, we studied various spectral combinations from UAV data and found that RGB was most effective during the flowering stage, while RGN (Red, Green, Near-infrared) was more suitable for non-flowering periods. Based on these findings, we proposed an innovative dual-channel non-maximum suppression method (dual-channel NMS), which merges YOLOv9 detection results from both RGB and RGN data, leveraging the strengths of each spectral combination to enhance detection accuracy and achieving a final counting accuracy of 98.4%. This study highlights the importance of integrating UAV multispectral technology with deep learning for coffee detection and offers new insights for the implementation of precision agriculture. [ABSTRACT FROM AUTHOR]

This study presents an innovative hexagonal deployment model designed specifically for wireless sensor networks (WSNs) with a primary application in precision agriculture. The proposed protocol integrates advanced features, notably an adaptive frequency-hopping spread spectrum (AFHSS) mechanism and a decentralized real-time adaptation strategy to optimize data transmission in dynamic agricultural environments. The simulation study, conducted in diverse terrains with realistic sensor node distributions, meticulously evaluates the protocol's performance using comprehensive Quality of Service (QoS) metrics. The hexagonal deployment model operates by strategically positioning sensor nodes in a hexagonal grid pattern, ensuring uniform coverage of the agricultural field. The AFHSS mechanism dynamically adjusts frequency channels, mitigating interference and fortifying the network's robustness against external disruptions. Complementing this, the decentralized real-time adaptation empowers individual nodes to autonomously respond to the ever-changing environmental conditions, optimizing data transmission efficiency. Quantitative results from the simulations exhibit outstanding performance metrics. The protocol achieves an average latency of 50 milliseconds, a packet loss rate below 2%, a success rate exceeding 95%, and highly efficient obstacle management, with adjusted nodes accounting for less than 5%. These compelling outcomes underscore the protocol's exceptional ability to deliver responsive and reliable data transmission, positioning it as a promising solution for enhancing environmental monitoring in precision agriculture. This study provides quantitative evidence of the protocol's prowess and delves into the nuanced working mechanisms, offering a deeper understanding of its potential impact. The findings contribute significant insights to the field, serving as a robust foundation for researchers and practitioners engaged in designing and implementing resilient WSNs tailored for precision agriculture applications. [ABSTRACT FROM AUTHOR]

Although plant disease recognition has witnessed a significant improvement with deep learning in recent years, a common observation is that current deep learning methods with decent performance tend to suffer in real-world applications. We argue that this illusion essentially comes from the fact that current plant disease recognition datasets cater to deep learning methods and are far from real scenarios. Mitigating this illusion fundamentally requires an interdisciplinary perspective from both plant disease and deep learning, and a core question arises. What are the characteristics of a desired dataset? This paper aims to provide a perspective on this question. First, we present a taxonomy to describe potential plant disease datasets, which provides a bridge between the two research fields. We then give several directions for making future datasets, such as creating challenge-oriented datasets. We believe that our paper will contribute to creating datasets that can help achieve the ultimate objective of deploying deep learning in real-world plant disease recognition applications. To facilitate the community, our project is publicly available at https://github.com/xml94/PPDRD with the information of relevant public datasets. [ABSTRACT FROM AUTHOR]

This paper addresses the critical and urgent need to reduce food losses and waste (FLW) resulting from stringent marketing standards. It proposes a comprehensive and actionable framework grounded in the three pillars of sustainability—environmental, economic, and social—to effectively evaluate FLW across the entire food supply chain. The paper involves a thorough review of existing marketing standards, including research on FLW due to marketing standards, and proposes the implementation of targeted key actions within four key food sectors: fruits, vegetables, dairy, and cereals. The study provides a detailed analysis of the significant impact marketing standards have on FLW at various stages of the supply chain, including primary production, processing, retail, and consumption. By focusing on these critical points, the research underscores the necessity of addressing marketing standards to achieve meaningful reductions in FLW. The proposed framework aims to foster improved business practices and drive the development of innovative, sector-specific solutions that balance sustainability goals with economic viability. The holistic approach followed for this research lays the foundation for ensuring that the proposed framework is adaptable and practical, leading to measurable improvements in reducing FLW and promoting sustainability across the food industry. [ABSTRACT FROM AUTHOR]

Context: In recent years, the focus on sustainable meat production in largescale pig farming has increased. An essential element is acquiring and evaluating digital data on ecological, outdoor pig housing environments and individuals. Objective: Some of the precision farming Internet of Things (IoT) tools that can be used in pasture-based, extensive pig production include those that have been proven in pasture-based ruminants and have been used in confined pig production for many years. These include Radio Frequency IDentification (RFID) technology, in which passive RFID tags attached to pigs' ears capture signals from a reader equipped with an antenna. The reader can also be used to collect weather parameters. Methods: In our on-farm research, which lasted 7 months and covered three seasons, we used RFID technology to investigate the effects of ambient temperature, humidity, air pressure, and hour of day on the appearance of breeding sows kept on pasture at the wallowing site. Results and conclusion: We found that all three weather parameters and hourof- day had a significant effect (p < 0.001) on the appearance of sows at the wallow. We calculated the THI (Temperature Humidity Index) and found days in summer and early autumn when there was a risk of heat stress. This is essential information for the pig farmer because heat stress has a strong negative effect on sow fertility. Our on-farm study found that RFID technology is suitable for detecting the presence of pigs in the pasture. However, the limitations of the technology, both in terms of the housing environment and the species specificity of the pigs, need to be taken into account. Significance: In pasture-based pig production systems, the meat production process is performed under extensive conditions of natural animalenvironment interaction. The negative impact on the natural environment is much lower, but animal welfare is higher than in intensive pig farming. It is also a profit-oriented activity, where the production of traceable and reliable animal products is a social and economic requirement. Collecting information from trusted sources using precision livestock farming (PLF) techniques is also feasible in this type of farming, providing significant added value for pig farmers and consumers. [ABSTRACT FROM AUTHOR]

This study presents an improved weed detection model, ADL-YOLOv8, designed to enhance detection accuracy for small targets while achieving model lightweighting. It addresses the challenge of attaining both high accuracy and low memory usage in current intelligent weeding equipment. By overcoming this issue, the research not only reduces the hardware costs of automated impurity removal equipment but also enhances software recognition accuracy, contributing to reduced pesticide use and the promotion of sustainable agriculture. The ADL-YOLOv8 model incorporates a lighter AKConv network for better processing of specific features, an ultra-lightweight DySample upsampling module to improve accuracy and efficiency, and the LSKA-Attention mechanism for enhanced detection, particularly of small targets. On the same dataset, ADL-YOLOv8 demonstrated a 2.2% increase in precision, a 2.45% rise in recall, a 3.07% boost in mAP@0.5, and a 1.9% enhancement in mAP@0.95. The model's size was cut by 15.77%, and its computational complexity was reduced by 10.98%. These findings indicate that ADL-YOLOv8 not only exceeds the original YOLOv8n model but also surpasses the newer YOLOv9t and YOLOv10n in overall performance. The improved algorithm model makes the hardware cost required for embedded terminals lower. [ABSTRACT FROM AUTHOR]

On-the-go soil sensors have emerged as promising tools for real-time, high-resolution soil nutrient monitoring in precision agriculture. This review provides a comprehensive overview of the current state-of-the-art in on-the-go soil sensor technology, discussing the potential benefits, limitations, and applications of various sensor types, including optical sensors (Vis-NIR, MIR, ATR spectroscopy) and electrochemical sensors (ISEs, ISFETs). The integration of these sensors with positioning systems (GPS) enables the generation of detailed soil nutrient maps, which can guide site-specific management practices and optimize fertilizer application rates. However, factors such as soil moisture, texture, and heterogeneity can affect sensor performance, necessitating robust calibration models and standardized protocols. Future perspectives highlight the need for multi-sensor systems, incorporation into IoT networks for smart farming, and enhancing affordability and adoptability of on-the-go sensor technologies to promote widespread adoption in precision agriculture. [ABSTRACT FROM AUTHOR]

Simple Summary: Milk removal, more commonly known as weaning, is considered a major stressor in the early life of a calf. If improperly managed, it can negatively impact both the calf's performance and welfare. One of the most delicate aspects to address during a calf's growth is nutrition, both liquid and solid, so that the calf can develop the rumen compartment correctly without precluding natural behavior and, at the same time, preserving business costs. The weaning calf, in addition to milk, generally receives calf starters, a very palatable solid feed rich in concentrates, but no source of roughage, although several studies emphasize the importance of fibrous feeds even at this stage of the animal's life. The aim of this study was to evaluate the impact on the behavior and welfare of weaning calves resulting from the provision of Total Mixed Ration (TMR) as a forage source in addition to calf starter administered ad libitum or in restricted quantities. TMR is a diet widely used in dairy cow breeding, obtained by chopping and mixing all types of feeds, i.e., forages, concentrates and other ingredients, in order to achieve a homogeneous ration composition. The results obtained from this study indicate that the administration of TMR without restrictions of starter allows the young calf to better express its natural behavior, suggesting a better state of well-being. Currently, in dairy farming, there is growing concern for the welfare of calves during the critical period between the separation from their dams and weaning. During weaning, rationed feeding is a practice used to improve feed efficiency and control the calves' growth, but it could also have negative consequences associated with hunger and feed restriction behavior. One such consequence could be the performance by calves of stereotyped behaviors indicative of poor welfare, such as non-nutritive oral behaviors. We hypothesized that making a Total Mixed Ration (TMR) available to calves, in addition to the standard pelleted starter diet, thanks to its structural and nutritional characteristics, could help to focus the oral behavior of the subjects towards nutritional activities and therefore limit the development of stereotyped behaviors, even when the amount of starter is restricted. To test this hypothesis, 30 female Holstein calves (equipped with an accelerometer based on an ear tag), were randomly assigned to one of three treatments: a control diet (CTR) with an ad libitum calf starter but without TMR; Treatment 1 (TRT1) with both ad libitum calf starter and ad libitum TMR; Treatment 2 (TRT2) with ad libitum TMR and a restricted amount of a calf starter (50% of the intake starter of the control group day by day). All animals were kept in individual cages equipped with a slow-flow teat bucket apparatus for milk feeding and with access to separated buckets (one for drinking water, one for the starter, and one for the TMR) placed on the outside of the front gate of each cage. Sucking behavior, as well as resting, ruminating and activity behavior, was recorded individually from 7 days of life to weaning (63 d of age) by an automated monitoring system based on ear-tag accelerometers (SCR eSense, Allflex, Irving, TX, USA). The results showed that in the CTR group, there was a greater sucking activity compared to the TRT1 group (26.25 min/head/day vs. 16.83 min/head/day, p = 0.0181), while the TRT2 group showed intermediate values (20.41 min/head/day). We hypothesized that this increased sucking activity may be explained by the oral activity directed at sucking the Milk Bar tube observed only in CTR calves after complete consumption of available milk and could indicate a higher stress level than in the TRT1 group. The time spent resting was significantly lower for the CTR group compared to the TRT1 group (9.20 h/day vs. 11.15 h/day, p = 0.0049) while the TRT2 group was in an intermediate situation (10.65 h/day). Furthermore, the increase in time dedicated to rest observed in TRT1, in light of the good vitality of the calves shown by the daily patterns of resting behavior and daily activity, also seems to suggest an improvement in welfare conditions compared to calves receiving pelleted starter alone. Based on these results, we can deduce that providing TMR ad libitum in the diet of weaning calves could be an effective strategy to improve calf welfare due to the reduction in stereotyped oral activity. However, further studies are needed to establish threshold values useful for distinguishing physiological from pathological resting times in weaning calves raised in individual cages. [ABSTRACT FROM AUTHOR]

In order to express the energy flow, motion flow, and control flow in wireless rechargeable sensor networks accurately and intuitively, and to maximize the charging benefit of MVs (mobile vehicles), a type of MTS-HACO (Mobile Transition Sequence Hybrid Ant Colony Optimization) is proposed. Firstly, node places are grouped according to the firing time of node's energy consumption transition to ensure that in each time slot, MV places only enable charging transitions for the node places with lower remaining lifetimes. Then, the FSOMCT (Firing Sequence Optimization of Mobile Charging Transition) problem is formulated under the constraints of MV places capacity, travelling arc weight, charging arc weight, and so on. The elite strategy and the Max–Min Ant Colony system are further introduced to improve the ant colony algorithm, while the improved FWA (fireworks algorithm) optimizes the path constructed by each ant. Finally, the optimal mobile charging transition firing sequence and charging times are obtained, ensuring that MVs have sufficient energy to return to the base station. Simulation results indicate that, compared with the periodic algorithm and the PE-FWA algorithm, the proposed method can improve charging benefit by approximately 48.7% and 26.3%, respectively. [ABSTRACT FROM AUTHOR]

This paper comprehensively explores the development of a standalone and compact microwave sensing system tailored for automated radio frequency (RF) scattered parameter acquisitions. Coupled with an emitting RF device (antenna, resonator, open waveguide), the system could be used for non-invasive monitoring of external matter or latent environmental variables. Central to this design is the integration of a NanoVNA and a Raspberry Pi Zero W platform, allowing easy recording of S-parameters (scattering parameters) in the range of the 50 kHz– 4.4 GHz frequency band. Noteworthy features include dual recording modes, manual for on-demand acquisitions and automatic for scheduled data collection, powered seamlessly by a single battery source. Thanks to the flexibility of the system's architecture, which embeds a Linux operating system, we can easily embed machine learning (ML) algorithms and predictive models for information detection. As a case study, the potential application of the integrated sensor system with an RF patch antenna is explored in the context of greenwood hydration detection within the field of smart agriculture. This innovative system enables non-invasive monitoring of wood hydration levels by analyzing scattering parameters (S-parameters). These S-parameters are then processed using ML techniques to automate the monitoring process, enabling real-time and predictive analysis of moisture levels. [ABSTRACT FROM AUTHOR]

In this paper, the advantages of the joint use of MHz- and GHz-frequency band impulses when employing contactless ground penetration radar (GPR) for the remote sensing of biomass, the height of the wheat canopy, and underlying soil moisture were experimentally investigated. A MHz-frequency band nanosecond impulse with a duration of 1.2 ns (average frequency of 750 MHz and spectrum bandwidth of 580 MHz, at a level of –6 dB) was emitted and received by a GPR OKO-3 equipped with an AB-900 M3 antenna unit. A GHz-frequency band sub-nanosecond impulse with a duration of 0.5 ns (average frequency of 3.2 GHz and spectral bandwidth of 1.36 GHz, at a level of −6 dB) was generated using a horn antenna and a Keysight FieldFox N9917B 18 GHz vector network analyzer. It has been shown that changes in the relative amplitudes and time delays of nanosecond impulses, reflected from a soil surface covered with wheat at a height from 0 to 87 cm and fresh above-ground biomass (AGB) from 0 to 1.5 kg/m2, do not exceed 6% and 0.09 ns, respectively. GPR nanosecond impulses reflected/scattered by the wheat canopy have not been detected. In this research, sub-nanosecond impulses reflected/scattered by the wheat canopy have been confidently identified and make it possible to measure the wheat height (fresh AGB up to 2.3 kg/m2 and height up to 104 cm) with a determination coefficient (R2) of ~0.99 and a bias of ~−7 cm, as well as fresh AGB where R2 = 0.97, with a bias = −0.09 kg/m2, and a root-mean-square error of 0.1 kg/m2. The joint use of impulses in two different MHz- and GHz-frequency bands will, in the future, make it possible to create UAV-based reflectometers for simultaneously mapping the soil moisture, height, and biomass of vegetation for precision farming systems. [ABSTRACT FROM AUTHOR]

The success of Variable Rate Application (VRA) techniques is closely linked to the algorithm used to calculate the different fertilizer rates. In this study, we proposed an algorithm based on the integration between some estimated agronomic inputs and crop radiometric data acquired by using a multispectral sensor. Generally, VRA algorithms are evaluated by comparing the yields, but they can often be affected by factors acting in the final phase of the crop cycle and not dependent on the fertilization treatments. Therefore, we decided to compare our algorithm (ALG) versus the traditional application of fertilizer (TRD) by evaluating the crop growth 1.5 months after the fertilization time. The algorithm was tested on a sorghum crop under organic farming, managed with or without manure. The saving of N obtained with ALG was equal to 14 and 5 kg ha− 1 (-14 and − 10% for the non-manure and fertilized treatments, respectively). The NDVI values acquired after fertilization showed a remarkable reduction of relative standard deviation for ALG system (from 22 to 9% and from 34 to 14% for manured and not manured, respectively), which was not found for TRD system (from 16 to 17% and from 29 to 18% for manured and not manured, respectively). The above ground biomass produced was statistically equivalent for the two systems in the manured plots and significant higher for ALG in not-manured plots (+ 0.74 t ha− 1 of dm, equal to + 23%). Finally, the indices calculated to evaluate the Nitrogen Use Efficiency (NUE) were consistently better in the ALG theses. [ABSTRACT FROM AUTHOR]

Introduction: Context Data-driven models (DDMs) are increasingly used for crop yield prediction due to their ability to capture complex patterns and relationships. DDMs rely heavily on data inputs to provide predictions. Despite their effectiveness, DDMs can be complemented by inputs derived from mechanistic models (MMs). Methods: This study investigated enhancing the predictive quality of DDMs by using as features a combination of MMs outputs, specifically biomass and soil moisture, with conventional data sources like satellite imagery, weather, and soil information. Four experiments were performed with different datasets being used for prediction: Experiment 1 combined MM outputs with conventional data; Experiment 2 excluded MM outputs; Experiment 3 was the same as Experiment 1 but all conventional temporal data were omitted; Experiment 4 utilised solely MM outputs. The research encompassed ten field-years of wheat and chickpea yield data, applying the eXtreme Gradient Boosting (XGBOOST) algorithm for model fitting. Performance was evaluated using root mean square error (RMSE) and the concordance correlation coefficient (CCC). Results and conclusions: The validation results showed that the XGBOOST model had similar predictive power for both crops in Experiments 1, 2, and 3. For chickpeas, the CCC ranged from 0.89 to 0.91 and the RMSE from 0.23 to 0.25 t ha−1. For wheat, the CCC ranged from 0.87 to 0.92 and the RMSE from 0.29 to 0.35 t ha−1. However, Experiment 4 significantly reduced the model's accuracy, with CCCs dropping to 0.47 for chickpeas and 0.36 for wheat, and RMSEs increasing to 0.46 and 0.65 t ha−1, respectively. Ultimately, Experiments 1, 2, and 3 demonstrated comparable effectiveness, but Experiment 3 is recommended for achieving similar predictive quality with a simpler, more interpretable model using biomass and soil moisture alongside non-temporal conventional features. [ABSTRACT FROM AUTHOR]

Purpose: This study examines the willingness of Spanish and Lithuanian grain farmers to adopt a combined approach of preventive site-specific spraying (PSSS) and selective harvesting (SH), two precision agricultural technologies (below referred to as PSSS-SH) aimed at mitigating the risk of mycotoxin contamination in barley and wheat. Methods: Data were collected from 190 commercial grain farmers using a choice experimental survey. The empirical analysis relied on the estimation of mixed logit and integrated latent class models. Results: The surveyed farmers were heterogeneous in their preference for the PSSS-SH technology, with a majority (81%) reporting that they were willing to adopt and pay for the PSSS-SH technology. Furthermore, the farmers' willingness to adopt PSSS-SH technology was influenced by the trade-offs between the potential production, economic and environmental changes. Conclusion: Profit maximization is not the only motivation for a farmer's decision to adopt PSSS-SH, there are also important non-financial benefits that align with the observed choices. Furthermore, the perceived usefulness of the technology, the willingness and readiness to use the technology, and the farmer characteristics (e.g. cooperative membership, employment status, share of household income from grain production and past experience with precision farming technology) were positively associated with uptake of the PSSS-SH technology. Therefore, extension programmes should have a special focus on the perceived usefulness of the technology, the willingness and readiness of farmers to use it, and its unique characteristics. [ABSTRACT FROM AUTHOR]

Automated technologies in precision agriculture enable unmanned systems to precisely target areas with chemicals through controlled nozzle movements. Quantitative assessment of these sprayers can enhance spraying strategies, catering to different canopy sizes, row spacing and coverage objectives. This research assessed an unmanned sprayer equipped with pan-tilt nozzles for targeted area control and spray coverage adjustment. The spray cloud path on the canopy, as the nozzles moved vertically and the sprayer advanced, was simulated mathematically. A model was developed to determine the swing angle based on orchard/vineyard geometrical parameters. This model was then applied in field tests in a vineyard and an apple orchard. Various nozzle-heading angles, driving speeds, and flow rates were experimented with, using average coverage and droplet density as the evaluation criterion. The findings showed that the developed model offered an effective method for determining the swing angles. Lowering driving speeds and increasing flow rates were found to notably enhance coverage. A 45º nozzle-heading angle proved more effective in vineyards, whereas a 90º angle yielded better results in apple orchards, reflecting the variations in canopy size and row spacing. The unmanned sprayer demonstrated great potential for autonomous spraying in vineyards and orchards. [ABSTRACT FROM AUTHOR]

Background: The body weight (BW) of animals at various growth stages is an important piece of information for the decision-making process. In the current "livestock 4.0" or precision livestock farming it becomes necessary to know if body measurements obtained from the dorsal view of an animal are related to its BW. Objective: To evaluate the relationship between BW and dorsal area (DA) of water buffaloes (Bubalus bubalis) reared in southeastern Mexico. Methods: The BW (340 ± 161.68 kg), hip width (HW), thorax width (TW), and body length (BL) were measured in 215 female Murrah buffaloes aged between 3 months and 5 years. The DA (m2) was calculated using the mathematical formulae for the area of a trapezoid, considering HW, TW, and BL in the calculation. The relationship between BW and DA was assessed with correlation and regression models. Results: The correlation coefficient between BW and AD was 0.96 (p<0.001). The linear equation had the highest determination coefficient (R2 = 0.94) along with the lowest mean square error (MSE = 1716.86), root MSE (RMSE = 41.43), Akaike Information Criterion (AIC = 1603.36), and Bayesian Information Criterion (BIC = 1610.10). Conversely, the allometric equation exhibited the highest values of MSE, RMSE, AIC, and BIC. Based on the quality of fit by the k-folds technique, the three proposed equations showed consistent adjustments, with more than 90% accuracy (R2 = 0.92). The quadratic equation exhibited the lowest mean squared prediction error and mean absolute error. Conclusion: The DA can be used as a good predictor of BW in buffaloes, especially when incorporated into first and second-degree linear equations. [ABSTRACT FROM AUTHOR]

Ion‐selective transistor‐based sensors play a pivotal role in quantifying ion concentrations in aqueous media. Existing solutions rely on direct coupling between ion‐selective membrane and channel, requiring bulky electrolyte reservoirs or complex technological approaches and material interfaces. This work introduces a transformative paradigm with ion‐selective floating‐gate organic electrochemical transistors (ISFG‐OECTs) and their integration in sensing circuits. ISFG‐OECTs feature spatial separation between ion‐selective gating and ionic‐electronic current modulation. Leveraging volumetric capacitance and solid‐state ionic liquid, efficient ionic coupling with the channel is obtained. These distinctive features make them an ideal solution for streamlined materials integration, eliminating the need for liquid reservoirs. Theoretical foundations and design guidelines for efficient ISFG‐OECT implementation are elucidated. Experimental results demonstrate the effectiveness of ISFG‐OECTs in both transistor‐sensors and current‐driven circuit configurations, revealing highly selective detection of K+ ions with a limit of detection as low as 11 × 10−6 m, even in the presence of interfering Na+ ions at concentrations two orders of magnitude higher. The proposed approach is simple, reliable, and scalable, offering opportunities for a broad range of fields, such as medical diagnostics, precision agriculture, and environmental monitoring. [ABSTRACT FROM AUTHOR]

Agriculture plays an important part in the overall growth and development of a nation. Concerns about agriculture continue to be a recalcitrant obstacle in the path of upward movement. Although agricultural yields are increased, however, low‐ and middle‐income countries still have difficulty in producing all of the required foods with the current state of agricultural technology. Smart agriculture is becoming increasingly important to the farmers as a means of ensuring optimal field growth and higher crop yield. This systematic study analysed and briefly explained the effects of using smart agriculture techniques (SATs) from a variety of countries, including China, the United States of America, Australia, India, the Philippine Islands, South Africa, Pakistan and Iran, among others. Increased climatic change resulting in abiotic stress and other harmful effects on plants have resulted in decreased productivity under traditional agricultural practices. Stats from the literature have shown that the launching of SATs has resulted in a significant increase in cotton–wheat and rice–wheat crop yields, resultantly increased incomes of the farmers. Application of SATs, including satellite remote sensing, drones, machine learning and image processing, monitoring, wireless sensor networks, IoT–based robotics, precision agriculture and agroforestry could be extremely useful in developing intelligent agricultural systems in underdeveloped and developing countries, with improved plant growth, high crop yield and ensuring food security. These technologies could help farmers by storing additional water, spraying pesticides with drones, practicing precision agriculture and employing sensors for assessing different environmental parameters. By making efficient use of these technologies, countries could be able to increase the yield of their crops, which, in turn, will contribute to the reduction of poverty and the elimination of food insecurity. [ABSTRACT FROM AUTHOR]

Livestock feeding behaviour is an influential research area in animal husbandry and agriculture. In recent years, there has been a growing interest in automated systems for monitoring the behaviour of ruminants. Current automated monitoring systems mainly use motion, acoustic, pressure and image sensors to collect and analyse patterns related to ingestive behaviour, foraging activities and daily intake. The performance evaluation of existing methods is a complex task and direct comparison s between studies is difficult. Several factors prevent a direct comparison, starting from the diversity of data and performance metrics used in the experiments. This review on the analysis of the feeding behaviour of ruminants emphasise the relationship between sensing methodologies, signal processing, and computational intelligence methods. It assesses the main sensing methodologies and the main techniques to analyse the signals associated with feeding behaviour, evaluating their use in different settings and situations. It also highlights the potential of the valuable information provided by automated monitoring systems to expand knowledge in the field, positively impacting production systems and research. The paper closes by discussing future engineering challenges and opportunities in livestock feeding behaviour monitoring. • The ruminant feeding process and its sensing variables are described. • Monitoring systems based on movement, sound, images and pressure are analysed. • Features of the acquisition, management, and availability of the data are discussed. • Signal processing and machine learning methods used in the algorithms are analysed. • Trends in sensing technology and computational methods are presented. [ABSTRACT FROM AUTHOR]

This paper provides an overview of the current state‐of‐the‐art in selective harvesting robots (SHRs) and their potential for addressing the challenges of global food production. SHRs have the potential to increase productivity, reduce labor costs, and minimize wastage by selectively harvesting only ripe fruits and vegetables. The paper discusses the main components of SHRs, including perception, grasping, cutting, motion planning, and control. It also highlights the challenges in developing SHR technologies, particularly in the areas of robot design, motion planning, and control. The paper also discusses the potential benefits of integrating artificial intelligence and soft robots and data‐driven methods to enhance the performance and robustness of SHR systems. Finally, the paper identifies several open research questions in the field and highlights the need for further research and development efforts to advance SHR technologies to meet the challenges of global food production. Overall, this paper provides a starting point for researchers and practitioners interested in developing SHRs and highlights the need for more research in this field. [ABSTRACT FROM AUTHOR]

Challenges in strawberry picking made selective harvesting robotic technology very demanding. However, the selective harvesting of strawberries is a complicated robotic task forming a few scientific research questions. Most available solutions only deal with a specific picking scenario, for example, picking only a single variety of fruit in isolation. Nonetheless, most economically viable (e.g., high‐yielding and/or disease‐resistant) varieties of strawberry are grown in dense clusters. The current perception technology in such use cases is inefficient. In this work, we developed a novel system capable of harvesting strawberries with several unique features. These features allow the system to deal with very complex picking scenarios, for example, dense clusters. Our concept of a modular system makes our system reconfigurable to adapt to different picking scenarios. We designed, manufactured, and tested a patented picking head with 2.5‐degrees of freedom (two independent mechanisms and one dependent cutting system) capable of removing possible occlusions and harvesting the targeted strawberry without any contact with the fruit flesh to avoid damage and bruising. In addition, we developed a novel perception system to localize strawberries and detect their key points, picking points, and determine their ripeness. For this purpose, we introduced two new data sets. Finally, we tested the system in a commercial strawberry growing field and our research farm with three different strawberry varieties. The results show the effectiveness and reliability of the proposed system. The designed picking head was able to remove occlusions and harvest strawberries effectively. The perception system was able to detect and determine the ripeness of strawberries with 95% accuracy. In total, the system was able to harvest 87% of all detected strawberries with a success rate of 83% for all pluckable fruits. We also discuss a series of open research questions in the discussion section. [ABSTRACT FROM AUTHOR]

In this work, motivated by the precision agriculture (PA) paradigm, we address the problem of managing hazelnut suckering plants on a per‐plant basis in a large‐scale orchard. Suckering plants, or shortly, suckers, are basal shoots that grow at the base of a tree and compete with the tree itself for nutrients and water. Generally, in large‐scale orchards, suckers are treated with the application of herbicide through spraying tractors that continuously spray the crops while navigating the whole orchard. This approach however does not consider the individual needs of each plant and it is definitely not environmentally‐friendly since a lot of unnecessary solution is being drained in the soil. For this reason, we propose a novel fully autonomous sucker management architecture that is able to detect the presence of suckers for each plant, by relying on a You Only Look Once (YOLO)‐based recognition system, reconstruct them in three‐dimension and estimate the amount of herbicide solution needed for the specific plant, based on a data‐driven approach. The herbicide solution is applied using a ground robot equipped with an RGB‐D camera and a spraying system. This approach allows to significantly reduce pollution and waste. Experimental results both for individual components and for the entire architecture in a real‐world (1:1 scale) hazelnut orchard located in Caprarola, Italy, are provided to corroborate the proposed architecture. [ABSTRACT FROM AUTHOR]

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This systematic review critically examines the diverse applications of Blockchain technology in the food supply chain and identifies areas where its potential remains underutilized. By analysing 60 Blockchain-based frameworks, the study highlights the most frequently employed drivers such as transparency, traceability, and security within food supply chains. Additionally, underexplored applications such as food donation and redistribution, supply chain financing, animal welfare, food waste management, and data analysis are identified, revealing opportunities for further innovation. The research employed NVivo 14 to analyze the extent of Blockchain's implementation in various food supply chain drivers, and the findings informed the development of a more diverse framework for Blockchain integration. Key insights demonstrate Blockchain's transformative potential, particularly in enhancing data integrity, trust, and operational efficiency through its immutable ledger and smart contracts, which streamline transactions, cut administrative costs, and reduce fraud. In terms of sustainability and safety, Blockchain improves traceability, accelerates safety responses, promotes environmental sustainability by tracking resource usage, and enhances humanitarian efforts with transparent, efficient resource distribution. Additionally, Blockchain facilitates food waste reduction by optimizing inventory and distribution, while ensuring surplus food reaches those in need. The study concludes by offering a roadmap for future research, pointing toward untapped dimensions of Blockchain's application in food traceability, sustainable supply chain management, and environmental & social impact. While the review provides a comprehensive understanding of Blockchain's current usage in food supply chains, the scope is limited by the systematic review process and specific inclusion criteria. This study serves as a foundation for exploring Blockchain's broader potential in shaping the future of food supply chains. [ABSTRACT FROM AUTHOR]

Climate change poses significant challenges to agricultural management, particularly in adapting to extreme weather conditions that impact agricultural production. Existing works with traditional Reinforcement Learning (RL) methods often falter under such extreme conditions. To address this challenge, our study introduces a novel approach by integrating Continual Learning (CL) with RL to form Continual Reinforcement Learning (CRL), enhancing the adaptability of agricultural management strategies. Leveraging the Gym-DSSAT simulation environment, our research enables RL agents to learn optimal fertilization strategies based on variable weather conditions. By incorporating CL algorithms, such as Elastic Weight Consolidation (EWC), with established RL techniques like Deep Q-Networks (DQN), we developed a framework in which agents can learn and retain knowledge across diverse weather scenarios. The CRL approach was tested under climate variability to assess the robustness and adaptability of the induced policies, particularly under extreme weather events like severe droughts. Our results showed that continually learned policies exhibited superior adaptability and performance compared to optimal policies learned through the conventional RL methods, especially in challenging conditions of reduced rainfall and increased temperatures. This pioneering work, which combines CL with RL to generate adaptive policies for agricultural management, is expected to make significant advancements in precision agriculture in the era of climate change. [ABSTRACT FROM AUTHOR]

The study purpose was to examine the design and analysis of a solar-powered multifunctional agricultural robot for cotton plants. It was designed primarily for seed sowing operations, which are labour-intensive due to the need of precise and optimum spacing between each plant for better growth and yield, as well as to determine soil moisture content, soil quality, and plant health. Multiple minor sensors were included in the design to determine parameters important for farming such as the light intensity, carbon dioxide composition, nitrogen components in the air, atmospheric temperature and humidity, and were able to achieve 80% accuracy in collecting the data. Therefore, this agricultural robot can be very beneficial in solving labour issues and increase farm productivity by 30% while also being cost-effective. The design and analysis of the robot was done using Solid Works and Ansys software, respectively. [ABSTRACT FROM AUTHOR]