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• Reviewed "precision agriculture", "smart agriculture" and "digital agriculture" concepts. • Suggested "smart agriculture" and "digital agriculture" be phases of "precision agriculture". • Proposed a few research focuses on smart and digital technologies for agriculture. [ABSTRACT FROM AUTHOR]

Pesticides are largely used at worldwide level to improve food production, fulfilling the needs of the global population which is increasing year by year. Although pesticides are beneficial for crop production, their extensive use has serious consequences for the pollution of the produced food as well as for soil and groundwaters. Indeed, it is reported that 50% of sprayed pesticides reach different destinations other than their target species, including soil, surface waters, and groundwaters. For this reason, we developed a flower-like origami paper-based device for pesticides detection in aerosol phase for precision agriculture. In detail, the paper-based electrochemical platform detects paraoxon, 2,4-dichlorophenoxyacetic acid, and glyphosate at ppb levels by measuring their inhibitory activity towards three different enzymes namely butyrylcholinesterase, alkaline phosphatase, and peroxidase enzyme, respectively. This integrated electrochemical device is composed of three office paper-based screen-printed electrodes and filter paper-based pads loaded with enzymes and enzymatic substrates. The pesticide detection is carried out by measuring through chronoamperometric technique the initial and residual enzymatic activity by using a smartphone-assisted potentiostat and evaluating the percentage of inhibition, proportional to the amount of aerosolized pesticides. This paper-based device was able to detect the three classes of pesticides in aerosol phase with limits of detection equal to 30 ppb, 10 ppb, and 2 ppb, respectively for 2,4-D, glyphosate, and paraoxon. [Display omitted] • Paper-based flower like biosensor for pesticide detection. • Pesticide multiclass analysis using origami paper-based devices. • Smartphone paper-based devices for boosting precision agriculture. [ABSTRACT FROM AUTHOR]

• Energy and environmental performances of strawberry supply chain. • Field measurement and interviews of professional stakeholders. • Non-plastic packaging showed significantly better environmental performance. • Most impactful processes are packaging production and refrigerated transport. Berries are highly perishable fruits and require both low storage temperature and suitable packaging throughout the supply chain to preserve their organoleptic qualities. However, the energy consumption of refrigerated equipment and the use of packaging materials, plastic in particular, might generate important environmental impacts. Besides, there is a strong commitment to reduce the use of plastic in the food industry. The aims of the current work are first to assess the energy consumption of refrigerated equipment and second to analyze the environmental performance of the strawberry supply chain. Various stages of the supply chain from transport from growers to retail storage were modeled using data from field measurement and interviews with professional stakeholders. Life Cycle Assessment (LCA) was performed for the strawberry supply chain. Different packaging materials, plastic (PET, RPET) and alternatives (molded pulp, recycled paper, cardboard), were used. The processes that generated the most important environmental burden were the packaging production and the long-distance refrigerated transport. To limit the impact related to packaging production, it is necessary to consider not only the type of packaging material but also the processes and energy consumption used in their manufacturing. [ABSTRACT FROM AUTHOR]

Plant health monitoring is devised as a new concept to elucidate in situ physiological processes. The need for increased food production to nourish the growing global population is inconsistent with the dramatic impact of climate change, which hinders crop health and exacerbates plant stress. In this context, wearable sensors play a crucial role in assessing plant stress. Herein, we present a low-cost 3D-printed hollow microneedle array (HMA) patch as a sampling device coupled with biosensors based on screen-printing technology, leading to affordable analysis of biomarkers in the plant fluid of a leaf. First, a refinement of the 3D-printing method showed a tip diameter of 25.9 ± 3.7 μm with a side hole diameter on the microneedle of 228.2 ± 18.6 μm using an affordable 3D printer (<500 EUR). Notably, the HMA patch withstanded the forces exerted by thumb pressing (i.e. 20-40 N). Subsequently, the holes of the HMA enabled the fluid extraction tested in vitro and in vivo in plant leaves (i.e. 13.5 ± 1.1 μL). A paper-based sampling strategy adapted to the HMA allowed the collection of plant fluid. Finally, integrating the sampling device onto biosensors facilitated the in situ electrochemical analysis of plant health biomarkers (i.e. H 2 O 2 , glucose, and pH) and the electrochemical profiling of plants in five plant species. Overall, this electrochemical platform advances precise and versatile sensors for plant health monitoring. The wearable device can potentially improve precision farming practices, addressing the critical need for sustainable and resilient agriculture in changing environmental conditions. [Display omitted] • An affordable high resolution 3D-printed hollow microneedle array (<30 μm tip) is developed. • Full characterization of the fluid uptake including in vitro and in vivo tests (plant leaves) is provided. • A paper-based sampling strategy is used to interface the microneedle array and the sensor. • The electrochemical detection of plant biomarkers has been demonstrated on plant leaves. • The cost-effectiveness and scalability of the platform contribute to a faster transfer from lab to fab. [ABSTRACT FROM AUTHOR]

Precision dairy tools (PDT) can provide timely information on individual cow's physiological and behavioral parameters, which can lead to more efficient management of the dairy farm. Although the economic rationale behind the adoption of PDT has been extensively discussed in the literature, the socio-psychological aspects related to the adoption of these technologies have received far less attention. Therefore, this paper proposes a socio-psychological model that builds upon the theory of planned behavior and develops hypotheses regarding cognitive constructs, their interaction with the farmers' perceived risks and social networks, and their overall influence on adoption. These hypotheses are tested using a generalized structural equation model for (a) the adoption of automatic milking systems (AMS) on the farms and (b) the PDT that are usually adopted with the AMS. Results show that adoption of these technologies is affected directly by intention, and the effects of subjective norms, perceived control, and attitudes on adoption are mediated through intention. A unit increase in perceived control score is associated with an increase in marginal probability of adoption of AMS and PDT by 0.05 and 0.19, respectively. Subjective norms are associated with an increase in marginal probability of adoption of AMS and PDT by 0.009 and 0.05, respectively. These results suggest that perceived control exerts a stronger influence on adoption of AMS and PDT, particularly compared with their subjective norms. Technology-related social networks are associated with an increase in marginal probability of adoption of AMS and PDT by 0.026 and 0.10, respectively. Perceived risks related to AMS and PDT negatively affect probability of adoption by 0.042 and 0.16, respectively, by having negative effects on attitudes, perceived self-confidence, and intentions. These results imply that integrating farmers within knowledge-sharing networks, minimizing perceived risks associated with these technologies, and enhancing farmers' confidence in their ability to use these technologies can significantly enhance uptake. [ABSTRACT FROM AUTHOR]

• Cybernetics systems have been developed to monitor animal production. • Machine vision methods are key for precision poultry welfare detection. • Robotics is an emerging method for poultry farm management. • Sustainable poultry farming requires continuous innovation in cybernetics. As global demands on the poultry production and welfare both intensify, the precision poultry farming technologies such as computer vision-based cybernetics system is becoming important in addressing the current issues related to animal welfare and production efficiencies. The integration of computer vision technology has become a catalyst for transformative change in precision farming, particularly concerning productivity and welfare. This review paper delineates the central role of computer vision in precision poultry farming, focusing on its applications in non-contact monitoring methods that employ advanced sensors and cameras to enhance farm biosecurity and bird observation without disturbance. We delved into the multifaceted advancements such as the utilization of convolutional neural networks (CNNs) for behavior analysis and health monitoring, evidenced by the high accuracy sorting of eggs and identification of health concerns within target-dense farm environments. The review paper underscores advancements in precision agriculture, including accurate egg weight estimation and egg classification within cage-free systems, paralleling the poultry sector's evolution towards more ethical farming practices. Moreover, it addresses the progress in poultry growth monitoring and examines case studies of commercial farms, showcasing how these innovations are being practically applied to enhance productivity and animal welfare. Challenges remain, particularly in terms of environmental variability and data annotation for deep learning models. Nevertheless, the review emphasizes the scope for future innovations like voice-controlled robotics and virtual reality applications, which have the potential to enhance poultry farming to new levels of efficiency, humanity, and sustainability. The insights assert that the continued exploration and development in computer vision technologies are not only instrumental for the poultry sector but also offer a blueprint for agricultural enhancement at large. [ABSTRACT FROM AUTHOR]

Currently, aquaculture methods tend to combine scale and intelligence, which saves manpower and improves the survival rate of seafood at the same time. High-precision and high-efficiency fish individual recognition can provide key technical support for fish disease detection, feeding habits, body condition, etc. In the realm of intelligent aquaculture, it provides robust data support for precision fish farming. However, the current research methods for individual fish recognition struggle to maintain the network model's focus on the fish body in real marine underwater complex environments (e.g., environmental background interference such as coral reefs, overlap between fish bodies, light noise, etc.), leading to unsatisfactory recognition results. To this end, this paper proposes a method for fish individual recognition in underwater complex environments based on video object segmentation, which consists of three parts, including a fish individual segmentation detection module, a fish individual recognition module, and an all-in-one visualization module. The work adopts a combination of deep learning methods and video object segmentation algorithms to solve the problem of low attention and poor detection accuracy of fish individuals in real underwater complex environments, which effectively improves the accuracy and efficiency of fish individual recognition, and analyzes and discusses the comparison of recognition effects using different weights. The results of the simulation experiments show that the key metric Rank1 value of the method achieves more than 96% accuracy on the public datasets DlouFish, WideFish, and the Fish-seg dataset produced in this paper, and improves over the state-of-the-art methods for fish individual recognition by 2.23%, 1.33%, and 1.25%, respectively. • Individual recognition of fish contributes to strong data support for accurate fish farming. • This paper introduces a video segmentation-based fish recognition method for complex underwater environments. • The improved method has been used to report promising results, which are better than the current best YOLOv8. • The proposed algorithm has a small number of parameters, high computational efficiency and can produce real-time results. [ABSTRACT FROM AUTHOR]

This paper is motivated by robotic systems that solve difficult real-world problems such as search and rescue (SAR) or precision agriculture 1 1 This work was supported by NSF under Grant CNS-1932074.. These applications require robots to operate in complex, uncertain environments while maintaining safe interactions with human teammates within a specified level of performance. In this paper, we study the scheduling of real-time applications on heterogeneous hardware platforms inspired by such contexts. To capture the stochasticity due to unpredictable environments, we propose the stochastic heterogeneous parallel conditional DAG (SHPC-DAG) model, which extends the most recent HPC-DAG model in two regards. First, it uses conditional DAG nodes to model the execution of computational pipelines based on context , while the stochasticity of DAG edges captures the uncertain nature of a system's environment or the reliability of its hardware. Second, considering the pessimism of deterministic worst-case execution time (WCET), it uses probability distributions to model the execution times of subtasks (DAG nodes). We propose a new partitioning algorithm Least Latency Partitioned (LLP) , which considers precedence constraints among nodes during the allocation process. Coupled with a scheduling algorithm that accounts for varying subtask criticality and constraints, the end-to-end latencies of safety-critical paths/nodes are then minimized. We use tasksets inspired by real robotics to demonstrate that our framework allows for efficient scheduling in complex computational pipelines, with more flexible representation of timing constraints, and ultimately, safety-performance tradeoffs. [ABSTRACT FROM AUTHOR]

In spite of the insight commonality may have concerning agrarian practice, fact is that nowadays agricultural science diligence is accurate, precise, data-driven, and vigorous than ever. The emanation of the technologies based on Internet of Things (IoT) has reformed nearly each industry like smart city, smart health, smart grid, smart home, including "smart agriculture or precision agriculture". Applying machine learning using the IoT data analytics in agricultural sector will rise new benefits to increase the quantity and quality of production from the crop fields to meet the increasing food demand. Such world-shattering advancements are rocking the current agrarian approaches and generating novel and best chances besides a number of limitations. This paper climaxes the power and capability of computing techniques including internet of things, wireless sensor networks, data analytics and machine learning in agriculture. The paper proposed the prediction model of Apple disease in the apple orchards of Kashmir valley using data analytics and Machine learning in IoT system. Furthermore, a local survey was conducted to know from the farmers about the trending technologies and their effect in precision agriculture. Finally, the paper discusses the challenges faced when incorporating these technologies in the traditional farming approaches. [ABSTRACT FROM AUTHOR]

Digital transformation in manufacturing necessitates the integration of advanced fields such as robotics, the Internet of Things (IoT), and data science. The latter two, in particular, introduce a unique set of challenges related to data acquisition and processing, especially for emerging manufacturing sectors that lack substantial data volumes for analytical software. This paper specifically addresses the challenges associated with limited data within the context of precision poultry farming. We begin by identifying the specific challenges encountered during the development of a smart poultry management software system. Subsequently, we propose potential solutions for each identified challenge. The outcome of this study is a comprehensive algorithm for data processing that encapsulates these proposed solutions, offering a practical approach to overcoming data limitations in precision poultry farming. [ABSTRACT FROM AUTHOR]

The topic of food is broad and global, thereby representing an influential sector of the economy. Motivated by the advent of Industry 4.0, massive potential exists to implement cutting-edge technologies in the food industry. Recent years have seen a growing interest towards the applications of augmented/mixed reality (AR/MR) in the food sector. An extensive search of online journals focusing on Scopus was conducted using terms including 'augmented reality', 'mixed reality' and 'food' in the search fields of Title, Abstract, and Keywords. Full paper reading was implemented and ineligible articles (i.e., non-English-language, review articles, not peer-reviewed and without full paper) were removed. Our systematic search resulted in 111 eligible articles, eight of which related to MR technology. There is an overall increasing trend in the number of publications appearing annually since the first relevant publication in 2010. Analysing these publications demonstrates the multidisciplinary nature of this technology which is closely linked to machine learning, computer vision, the Internet of Things (IoT), and artificial intelligence. Our findings also revealed that AR/MR technology is mainly applied in the following areas: dietary assessment, food nutrition and traceability, food sensory science, retail food chain applications, food education and learning, and precision farming. Furthermore, we highlight the limitations and analytical challenges that hinder the application of AR/MR to food-related research, such as the lack of reliable wireless connection and the difficulty in recognizing food objects in a complex environment, while also describing future research needs and directions. • Applications of AR/MR for food is summarized from 111 eligible articles published since 2010. • Applications of AR/MR is closely linked to advanced technologies such as IoT, machine learning. • AR/MR is mainly applied for food safety, sensory, retail chain, education, and precision farming. • Limitations and challenges that hinder food applications of AR/MR are highlighted. • Future research needs and directions for AR/MR developments in food areas are presented. [ABSTRACT FROM AUTHOR]

Fruits and vegetables are major sources of nutrients for the majority of the population across the globe. With the rapid increase in population, the objectives of the future agro-industry are to reduce product loss while increasing product quality and productivity considerably. Consequently, farmers need to be assisted with cutting-edge technologies for sustainable, eco-friendly, and efficient farming. Smart farming for early disease recognition and control is the current hot-spot research objective in the fruitage domain. The precision agriculture era has been revolutionized by federating cutting-edge technologies like machine learning, deep learning, and, the Internet-of-Things. However, the existing studies focused on the impact of individual technology on single or multiple cultivars of edible fruits or vegetables. Limited areas of the fruitage disease remain explored, necessitating further investigation into the research gaps and challenges identified for implementing the smart practices in real-field farmlands. In this paper, a comprehensive survey of recent advancements in fruit and vegetable disease identification and classification is presented. The technology-wise state-of-the-art findings, gaps, challenges, and future opportunities for fruitage disease recognition have been presented, covering 99 research articles. Moreover, the corpus of publicly available fruit and vegetable datasets has been investigated, with the existing gaps, improvements, and future requirements. The research paper concludes with challenges and a future outlook that promises to be a very significant and valuable resource for researchers working in the area of agronomic disease monitoring. [ABSTRACT FROM AUTHOR]

This paper explains the evolving role of the local supply chain across different disruption scenarios. A systematic literature review of 91 papers from 33 journals supports definition of the local supply chain and explication of benefits before, during and after COVID-19 disruption. Resilience emerges as the prevalent benefit at each stage, but to varying scales. In the pre-COVID-19 era, where the local supply chain serves as a back-up approach to the more dominant global supply chain, localisation's capacity to absorb change promotes resilience to mitigate low-magnitude disruption. At initial outbreak of COVID-19 disruption the local supply chain was necessitated as an emergency response to the untenability of global operations, demonstrating transformational resilience to survive. As disruption continued, the local supply chain upscaled and adapted to recover. In the post-COVID-19 era, resilience is expected to remain a strategic priority, promoting continued investment in local operations to thrive with embedded resilience. As a result, the COVID-19 pandemic welcomed a point of transition for the local supply chain as capacities and benefits are revaluated and localisation recognised as critical in developing resilient supply networks for the future. This study consolidates this evolution to offer a propositional framework showcasing the local supply chain across different disruption scenarios. This offers long overdue definition and explanation of the local supply chain and its relationship with resilience, addressing an existing lack of academic attention and encouraging alignment of local-versus-global decisions with changing strategic priorities. [ABSTRACT FROM AUTHOR]

Precision agriculture has emerged as a dominant force in the United States, with widespread adoption of advanced technologies and decision support systems (DSS) since the 1980s. Key tools such as variable rate application (VRA), autopilot systems, and remote sensing have become integral for U.S. farmers, offering invaluable insights from crop, soil, and weather information to optimize agricultural production while minimizing environmental impact. To synthesize and categorize the extensive research available on precision agriculture, a systematic review protocol has been designed. Our objective is to offer clear and authoritative insights into the nature, scope, and volume of this field. Implementing a rigorous search strategy, we utilized renowned databases such as Scopus® and Web of ScienceTM to gather relevant and significant materiality. The retrieval process involved the use of indexing terms and Boolean operators, with a focus on 'precision agriculture' and 'precision farming', striking a balance between specificity and comprehensiveness. To ensure the credibility of our findings, only peer-reviewed papers authored by individuals affiliated with U.S. institutions have been included. Expert reviewers with deep knowledge in the field independently assessed the selected papers, thoroughly evaluating titles, abstracts, keywords, methods, conclusions, and declarations. Consistency and eligibility were paramount in determining which papers met the criteria for inclusion. Any discrepancies or disagreements were resolved through rigorous consensus-building discussions among the reviewers. Through this comprehensive meta-review, we provide a scientific contribution that enhances our understanding of precision agriculture, highlighting focus areas for further research and development (R&D). By synthesizing and categorizing the existing literature, we offer authoritative insights into the research landscape, informing future investigations and fostering innovation. Focusing specifically on the U.S., we shed light on the unique aspects and pioneering advancements in precision agriculture within the country. Ultimately, our findings have the potential to drive progress, contributing to sustainable development, increased productivity, enhanced environmental sustainability, and responsible agricultural practices. [ABSTRACT FROM AUTHOR]

• This paper introduced the functional role of robotic arms in precision agriculture. • This paper summarized the hardware and software technologies of agricultural robotic arms. • This paper presented the application of robotic arms in various agricultural environments, including greenhouses, fields, and orchards. • This paper discussed the challenges and future prospects of robotic arms in precision agriculture. In precision agriculture, robotic arms exhibit significant technical advantages, such as enhancing operational precision and efficiency, reducing labor costs, and supporting environmental sustainability. This paper provides a comprehensive overview of the application of ground-based robotic arms in precision agriculture, analyzing the hardware and software aspects and current application status across various agricultural settings, and discussing challenges and prospects in this field. First, this paper explores precision agriculture and agricultural robotic arms, highlighting their critical roles in enhancing agricultural efficiency and automation. Further, it addresses the challenges plaguing the practical applications of robotic arms and compares innovative robotic arm technologies with traditional models to establish a foundation for understanding these advancements in modern agriculture. Additionally, this paper analyses the hardware of robotic arms, including rigid and flexible manipulators, drivers, end-effectors, sensors, and controllers, emphasizing the importance of innovation and optimization for improved performance. For the software systems, this paper focused on classic workflows and advanced algorithms for perception, motion planning, and control, as these are essential for the precise and adaptable functioning of robotic arms in diverse agricultural environments. Furthermore, this paper reviews the research and application status of robotic arms across various settings, including greenhouses (e.g., ground planting, desktop planting, and vertical planting), fields (e.g., dry fields, moist, and paddy fields), and orchards (e.g., fruit tree orchards, vineyard orchards, and ground-level orchards) to demonstrate their broad applicability and efficient operational capabilities in diverse conditions. Lastly, this paper discusses the challenges and prospects of robotic arms, emphasizing the significance of integrating disciplines, such as agronomy and biomimetics, big data, artificial intelligence, digital twinning, and human–machine interaction. Advancements in these areas are pivotal for the progress of robotic arm technology and for introducing innovative, efficient solutions to precision agriculture. In summary, this review reveals the immense potential of the application of robotic arms in precision agriculture. With ongoing technological advancements, these robotic arms are expected to play an increasingly crucial role in future agricultural production, making substantial contributions to achieving more efficient, innovative, and sustainable farming practices, heralding a new era in agricultural technology. This paper will serve as a valuable guide for researchers and practitioners, offering comprehensive insights into the use of robotic arms in precision agriculture and providing essential knowledge for advancing the field. [ABSTRACT FROM AUTHOR]

The field of unmanned aerial vehicles (UAVs) has grown in the last years, showing its utility in broader applications. For instance, in surveillance, precision agriculture, pack delivery, among others. The UAVs characteristics demand more suitable configurations for increasing their flight time, maneuverability, stability, and reliability for attending a growing quantity of services. One of the UAV configurations that has gained popularity in the last years is the Convertible Unmanned Aerial Vehicle (CUAV). This paper aims to provide a control strategy to stabilize the CUAV in all the flight modes: hover, cruise, and transition mode, in which the CUAV changes between hover and cruise flight mode. For that, we propose longitudinal modeling that considers realistic aerodynamics and even disturbances. This model presents a precise balance between complexity and practicality for control implementations. The control algorithm design is based on the Lyapunov stability theory and uses saturation functions intending not to saturate the actuators. Besides, the control algorithm does not include any switching function, is easy-to-implement, and demands the usually available feedback in the vast majority of low-cost commercial autopilots. The control allocation problem for this control is also solved. A mathematical proof based on Lyapunov theory demonstrates that the proposed controller performs the closed-loop system globally exponentially stable. Simulation and real flight experiments conducted with the CUAV demonstrate the effectiveness of theoretical results. Moreover, we present several comparative studies with the state of the art that demonstrate the paper's contribution to the field of convertible aerial vehicles. • A tilt-rotor convertible unmanned aerial vehicle (CUAV) is presented. • The CUAV is modeled and stabilized in hovering, cruise, and transition flight modes. • A nonlinear smooth control solves the transition maneuver problem. • Several comparisons with the state-of-the-art are given. • Extensive flight experiments in the CUAV real platform are presented. [ABSTRACT FROM AUTHOR]

• Unmanned aerial spraying systems (UASS) with variable rate spraying (VRS) explored for reducing pesticide ecological impact. • Examines the integration of UASS in variable rate spraying as a new dimension in precision agriculture. • Highlighted the benefits of VRS in increasing agricultural yields, reducing resource usage, and enhancing environmental sustainability. • Multispectral and hyperspectral sensor technologies are investigated for optimizing UASS-based VRS missions. • Addresses technical and regulatory challenges in UASS-based spraying, offering potential solutions. • Future innovations in sensor technology and AI-driven flow rate optimization for precision agriculture are discussed. Pesticides suppress pest populations and maintain agricultural yield; however, their overuse causes ecological damage. To mitigate the ecological damage caused by the overuse of pesticides, this paper explores the application of a precise and adaptable technique known as unmanned aerial spraying system (UASS)-based variable rate spraying (VRS). Herein, the current state of precision agriculture is examined, and the application of UASS in variable rate spraying is discussed. Then, the role of advanced sensors, including multispectral and hyperspectral technologies, in optimizing UASS-based VRS missions is studied, followed by the challenges in UASS-based spraying, ranging from technical intricacies to regulatory considerations, and related solutions. In addition to delving into pesticides, the paper explores alternative solutions such as herbicides, encompassing an integrated approach that aligns with sustainable farming practices for more effective pest management. VRS has advantages, including increased yields, reduced resource usage, and environmental sustainability. This paper concludes by delineating current challenges and envisioning future innovations, spotlighting ongoing studies on sensor technology and AI-driven flow rate optimization. This review provides insights into UASS-based VRS for precision agriculture. [ABSTRACT FROM AUTHOR]

Agriculture must overcome escalating problems in order to feed a growing population while preserving the environment and natural resources. Recently, it has become clear that sensors and the Internet of Things (IoT) are effective tools for boosting agricultural sustainability and food security. This study provides insights into the global market size for smart agriculture in future years from 2021 to 2030, In addition, this research offered four levels of the IoT architecture for smart agriculture: the perception or sensing and actuator layer, the network layer, the cloud layer, and the application layer. The state of the art in IoT and sensor technologies for agriculture is examined in this review paper, along with some of their potential uses, including 1) irrigation monitoring systems, 2) fertilizer administration, 3) crop disease detection, 4) monitoring (yield monitoring, quality monitoring, processing monitoring logistic monotoring), forecasting, and harvesting, 5) climate conditions monitoring, and 6) fire detection. Additionally, this review offers a number of sensors for agriculture that can detect parameters like soil NPK, moisture, nitrate, pH, electrical conductivity, CO2, temperature, humidity, light, weather station, water level, livestock, plant disease, smoke, flame, flexible wearable. Subsequently, this study highlights the advantages of IoT in smart agriculture, including superior efficiency, expansion, reduced resources, cleaner method, agility, and product quality improvement. However, there are still issues that need to be resolved in order for IoT technology to be used in agriculture where covered in this paper, and also provide insights into future research directions and opportunities. This study will contribute to helping future readers and researchers to better understand the state of academic achievement in this subject. [ABSTRACT FROM AUTHOR]

Rapid and accurate agricultural greenhouse extraction with remote sensing imagery is essential for providing spatial information for precision agriculture. Benefiting from local spatial perception, deep learning based object extraction methods have achieved satisfactory performances in extracting geo-objects. However, they fail to work for large-scale greenhouse extraction since the objects are sparsely distributed in the background and densely distributed in the foreground, where the local spatial perception causes redundant computation and false detection problems. In this paper, we propose a layout attention network (LANet) framework for large-scale greenhouse extraction using remote sensing imagery, which replaces the local spatial perception with spatial layout perception, i.e., a sparse global layout to identify the sparse background and a dense local layout to identify the dense foreground. To address the shortcoming of the sparse background, which leads to redundant computation, a sparse global layout awareness module is formulated as a scene classifier. This accommodates the global layout attention map of the global scene features by adopting a layout-shared convolutional neural network (CNN) backbone for generating class-agnostic layout priors and global channel attention for aggregating discriminative global layout features, ensuring robust sparse background identification. Then, to alleviate the problem of the dense foreground, which causes false detection, a dense local layout awareness module is proposed to incorporate the local layout attention map and rotated region of interest (RRoI) features. The RRoI features are then further embedded to guide the initial RRoIs for object location refinement by aligning the initial RRoI locations in a layout-sensitive attention mechanism and achieving semantic enhancement by taking the local layout density as a semantic prior to assign a reliable class score map. The experimental results obtained on an agricultural greenhouse benchmark dataset and a large-scale agricultural greenhouse extraction dataset illustrate that the proposed framework can outperform the state-of-the-art object extraction methods in both speed and accuracy, and has a high generalization ability for large-scale dense object extraction. [ABSTRACT FROM AUTHOR]

Precision agriculture plays a crucial role in optimizing crop yield, reducing environmental impact, and ensuring sustainable agricultural practices. Early detection and accurate diagnosis of leaf diseases are essential for preventing significant losses in crop production and maintaining food security. However, the inherent challenge of class imbalance in leaf disease datasets poses a significant obstacle for machine learning algorithms. In this paper, we explore and compare different techniques for handling class imbalance in leaf disease detection to improve the accuracy and reliability of machine learning models in the context of precision agriculture. We investigated the performance of different methods for leaf disease detection using the challenging New Plant Diseases Dataset (NPDD), which consists of image-based plant leaves. Our experiments reveal promising results, particularly with the hierarchical approach, achieving an accuracy of 97.17%. The outcomes of our study contribute to the growing body of knowledge in precision agriculture by providing a comprehensive analysis of techniques for handling class imbalance in leaf disease detection. Furthermore, our findings serve as a valuable resource for researchers and practitioners in the field, offering guidance on selecting and implementing the most effective approaches to tackle class imbalance challenges and improving the overall performance and reliability of machine learning models in the domain of precision agriculture. [ABSTRACT FROM AUTHOR]

• CNN architecture called MSFA-Net that acts as a texture feature extractor from raw images. • MSFA-Net requires to learn much fewer hyperparameters than state-of-the-art CNNs, and so reduces computation costs. • MSFA-Net outperforms several descriptors in multispectral texture classification and crop/weed recognition. • Texture features extracted from raw images are more discriminant than those extracted from demosaiced ones. Multispectral snapshot cameras fitted with a multispectral filter array (MSFA) acquire several spectral bands in one shot and provide a raw mosaic image in which a single channel value is available at each pixel. Texture features are classically extracted from fully-defined images that are estimated by demosaicing. Such an estimation may however cause spatio-spectral artifacts. Moreover, texture feature extraction becomes computationally inefficient and yields to high-dimensional features as the number of bands increases. In this paper, we propose an original approach based on a convolutional neural network called MSFA-Net to capture spatio-spectral interactions in raw images at reduced computation costs. Experiments of multispectral image classification and outdoor image segmentation show that the proposed approach outperforms several hand-crafted and deep learning-based feature extractors. [ABSTRACT FROM AUTHOR]

• Recent advances in weak and no supervision learning and their applications in agriculture. • A new taxonomy of label-efficient algorithms was proposed according to the degree of required supervision. • The remaining challenges and potential future directions for label-efficient learning in agriculture were discussed. The past decade has witnessed many great successes of machine learning (ML) and deep learning (DL) applications in agricultural systems, including weed control, plant disease diagnosis, agricultural robotics, and precision livestock management. However, a notable limitation of these ML/DL models lies in their reliance on large-scale labeled datasets for training, with their performance closely tied to the quantity and quality of available labeled data. The process of collecting, processing, and labeling such datasets is both expensive and time-consuming, primarily due to escalating labor costs. This challenge has sparked substantial interest among researchers and practitioners in the development of label-efficient ML/DL methods tailored for agricultural applications. In fact, there are more than 50 papers on developing and applying deep-learning-based label-efficient techniques to address various agricultural problems since 2016, which motivates the authors to provide a timely and comprehensive review of recent label-efficient ML/DL methods in agricultural applications. To this end, a principled taxonomy is first developed to organize these methods according to the degree of supervision, including weak supervision (i.e., active learning and semi-/weakly- supervised learning), and no supervision (i.e., un-/self- supervised learning), supplemented by representative state-of-the-art label-efficient ML/DL methods. In addition, a systematic review of various agricultural applications exploiting these label-efficient algorithms, such as precision agriculture, plant phenotyping, and postharvest quality assessment, is presented. Finally, the current problems and challenges are discussed, as well as future research directions. A well-classified paper list that will be actively updated can be accessed at https://github.com/DongChen06/Label-efficient-in-Agriculture. [ABSTRACT FROM AUTHOR]

Now, deep learning technology has gradually matured and successfully applied in various fields, bringing great convenience to human life. However, people neglect the importance of early sample collection and data processing while continuously improving the quality of network models, which often leads to poor application effect of models in practical projects. At present, the Data-centric AI campaign has started in the field of deep learning, with the purpose of letting researchers pay attention to the quality of data. This paper proposes a sample selecting method based on feature density from the motion idea, which can be applied to practical application scenarios where there is a huge deviation in the distribution of train set and test set, the dataset redundancy is too high, and the dataset sampling is guided. This method uses ternary loss function to constrain and aggregate sample feature points, constructs feature density space through feature extractor, traverses feature points to calculate distance between samples, judges special category redundancy and deletes redundant samples, and finally retains samples as highly representative samples to optimize dataset distribution. In the experiment, the self-built dataset IP05 are used in this paper. Sufficient experimental results show that the samples selected by the feature density sample selecting method are indeed more representative. • We propose a sample screening method based on feature density which can reduce the redundancy of train set in smart agriculture. • Ternary loss is used in our proposed method to constrain or aggregate sample feature points, and constructs feature density space through feature extractor. • A feature density function is proposed to calculate the feature point density in the feature density space to assist reducing samples. [ABSTRACT FROM AUTHOR]

This paper reviews the application of metaheuristics for optimized sustainable supply chain management (SSCM). This paper explores the potential of metaheuristics to improve the supply chain's sustainability while enhancing its efficiency and competitiveness. The paper provides an overview of the principles of SSCM and the challenges businesses face in achieving sustainable supply chain management. It then introduces the concept of metaheuristics and describes their use in solving complex optimization problems. The paper reviews various metaheuristics algorithms applied to sustainable supply chain management and analyzes their effectiveness in addressing the challenges of SSCM. The paper also identifies the key factors that influence the success of using metaheuristics for SSCM, such as the choice of algorithm, problem complexity, and data quality. Finally, the paper provides recommendations for future research in this area and highlights the potential of metaheuristics to promote sustainable supply chain management. The review suggests that metaheuristics can be a valuable tool for optimizing sustainable supply chain management and improving supply chain operations' sustainability, efficiency, and competitiveness. [ABSTRACT FROM AUTHOR]

Precision Agriculture (PA) promises to boost crop productivity while reducing agricultural costs and environmental footprints, and therefore is attracting ever-increasing interests in both academia and industry. This management strategy is underpinned by various advanced technologies including Unmanned Aerial Vehicle (UAV) sensing systems and Artificial Intelligence (AI) perception algorithms. In particular, due to their unique advantages such as a low cost, high spatio-temporal resolutions, flexibility, automation functions and minimized risk of operation, UAV sensing systems have been extensively applied in many civilian applications including PA since 2010. In parallel, AI algorithms (deep learning since 2012 in particular) are also drawing ever-increasing attention in different fields, since they are able to analyse an unprecedented volume/velocity/variety of data (semi-) automatically, which are also becoming computationally practical with the advancements of cloud computing, Graphics Processing Units and parallel computing. In this survey paper, therefore, a thorough review is performed on recent use of UAV sensing systems (e.g., UAV platforms, external sensing units) and AI algorithms (mainly supervised learning algorithms) in PA applications throughout the crop life-cycle, as well as the challenges and prospects for future development of UAVs and AI in agriculture sector. It is envisioned that this review is able to provide a timely technical reference, demystifying and promoting research, deployment and successful exploitation of AI empowered UAV perception systems for PA, and therefore contributing to addressing future agricultural and human nutrition challenges. [ABSTRACT FROM AUTHOR]

• Characterization of the planting and harvesting planning problem and its modelling for medicinal plants. • Conceptual framework to characterize the crop planting and harvesting planning problem. • Model to plan planting and harvest of crops with variable molecules concentration and minimum time between harvests. • Model validation through the application to a natural food supplement supply chain case study. • Economic unfairness among farmers eliminated while respecting supply chain costs and concentration of plant molecules. The planting and harvesting of medicinal plants have characteristics that differentiate them from other crop types and complicate their planning. For example, drug processors do not require large quantities of product to be harvested but have a high concentration of active molecules. There is no evidence for any optimization tool to support the planting and harvesting of such plants. Given this sector's importance and its impact on populations' health, it is necessary to develop solutions to increase the sustainability of their supply chains. This paper aims to bridge this gap by proposing a conceptual framework to characterize a crop planting and harvesting planning problem, and a multi-objective optimization model for the planning of planting, harvesting, post-harvesting, distribution and storage of medicinal plants with variable concentrations of molecules and minimum time between harvests. The model optimizes three objectives aligned with sustainability: supply chain costs, concentration of molecules in plants, farmers' perceived economic unfairness. It is validated by its application to a case study of medicinal plants in the Basilicata region (Italy). The ε-constraint method is used to obtain 11 non dominated solutions showing the possibility of eliminating farmers' perception of economic unfairness by maintaining similar values for supply chain costs and concentrations of active molecules when planning the production of medicinal plants. Finally, the TOPSIS method is applied to select the best plan to be implemented into the supply chain. [ABSTRACT FROM AUTHOR]

• A novel method for the individual identification of lying cows was developed. • When herd members change, there is need to collect some images of new cows lying without retraining the model. • The models deployed in an embedded system achieved real-time identification. The lying time of cows is a key indicator of their health and comfort. The ability to automatically recognize the lying posture of cows while simultaneously realizing individual cow identification can play an important role in improving cow welfare, increasing milk yield, detecting cow diseases in a timely manner and enabling precision dairy farming management. In this paper, a method of individual identification for lying dairy cows in a barn based on YOLOX and a feature extraction network named CowbodyNet is proposed. When new cows join the herd, there is no need to collect a large number of images to retrain the model. It is very convenient to collect several images of newly added cows lying and store them in the database. First, the low-light images collected at night are enhanced by the multiscale retinex with chromaticity preservation (MSRCP) algorithm to improve the image quality. Then, the YOLOX target detection algorithm is applied to detect and segment cows in the lying posture. Following this, the segmented images of lying cows are input into CowbodyNet to generate feature vectors, which are used to construct a feature vector database. Subsequently, the Euclidean distances between the feature vector of a cow to be identified and the feature vectors in the database are calculated to determine the identification result. The proposed method achieves 94.43% lying cow identification accuracy on a data set containing top-view images of 72 cows. Finally, the individual cow detection and identification model is successfully deployed on the Jetson Xavier NX embedded platform. The results demonstrate the effectiveness and practicability of the proposed cow identification method. This study provides effective technical support for the application of identifying individual lying cows. [ABSTRACT FROM AUTHOR]

• The aim of the paper was to automatically estimate the posture of tea leaves. • Deep neural network for tea leaves pose estimation. • An algorithm named TeaPoseNet was proposed. • A one-bud-one-leaf tea leaves dataset was constructed. • The method can be used for future plant morphology analysis. The estimation of tea leaf pose is an emerging research topic. Recognising the morphological features of tea leaves can help accurately categorise, grade, and determine their level of maturity. Therefore, this study proposes a deep neural network, TeaPoseNet, to estimate tea leaf poses. The algorithm was trained and validated using a dataset of one-bud-one-leaf images of Yinghong No.9 tea leaves and was compared with four other pose estimation networks. At the same time, the contribution of TKS_NMS to the algorithm was validated through ablation experiments. The results indicate that TKS_NMS improved the EPE accuracy of pose recognition by 16.33 %. More specifically, the algorithm achieved a good overall performance, with PCK, AUC, EPE, and NME reaching 0.9800, 0.8147, 9.0955, and 0.0644, respectively. The average running speed for detecting the pose of a single tea leaf image was 40.01 ms. To the best of our knowledge, this is the first application of pose estimation technology to the detection and analysis of Yinghong No.9 tea leaves. The results show that the proposed algorithm can effectively estimate the pose of tea leaves, thus providing a reference for subsequent tea research. [ABSTRACT FROM AUTHOR]

The stabilized collocation method (SCM) is a promising meshless collocation method that can overcome the instability defects in the classical direct collocation method. To improve the performance of the SCM, a superconvergent stabilized collocation method (SSCM) is developed in this paper for linear and nonlinear elliptic problems through the use of the moving least squares (MLS) approximation and its smoothed derivatives. Accuracy of the SSCM and the SCM is analyzed with an emphasis on the influence of boundary conditions, and precise error measures are presented for different types of boundary conditions. Numerical results validate the superconvergence of the SSCM and confirm the theoretical analysis. • A superconvergent stabilized collocation method is developed for linear and nonlinear elliptic problems. • Theoretical accuracy of the method is analyzed detailedly. • Theoretical and numerical results reveal that the method possesses superconvergence property. [ABSTRACT FROM AUTHOR]

• Research status of 3D reconstruction technology in agriculture was summarized. • Sensors, systems and methods used in 3D reconstruction were summarized. • The applications of 3D reconstruction methods in agriculture were summarized. • Challenges and future trends of 3D reconstruction in agriculture were reported. Perceiving the shape and structure of the real three-dimensional world through sensors and cameras is indispensable across various domains. The 3D reconstruction technology is dedicated to realizing this ideal process. 3D reconstruction technology serves as a transformative tool, enriching our ability to perceive the genuine shape and stereo structure of objects and scenes in the real world. Through combining advanced sensors, image processing algorithms and 3D reconstruction methods, it captures the shape and structural information of targets from multiple perspectives and dimensions, and creates highly realistic 3D models in the virtual environment. With the rapid modernization of agriculture and ongoing technological progress, the demand for more efficient and precise management and monitoring methods in agricultural production is increasing. Traditional observation and measurement methods face challenges such as low efficiency and incomplete data. 3D reconstruction technology provides more accurate and intelligent management tools for smart agriculture. This paper provides a detailed introduction to the research progress based on 3D reconstruction technology in smart agriculture. It delves into the characteristics and development of various sensors and sensing systems, discussing various methods to implement 3D reconstruction technology. Different from applications in industrial environments, agricultural environments and crops are usually complex and variable, and consideration of diverse factors is required for the selection of suitable sensors and reconstruction methods. Therefore, several aspects of applications are summarized, such as agricultural robotics, crop phenotyping, livestock, and the food industry. Finally, the challenges and potential future trends of 3D reconstruction in agriculture are given. [ABSTRACT FROM AUTHOR]

• A multi-layer perceptron model for straight-turn binary classification training using K-Fold cross validation is proposed. • A convolutional neural network-based model is proposed to proform turning classification between Omega turn and T turn. • The proposal of line and turn segmentation algorithms and turn type recognition algorithms provides new methods for estimating the efficiency of agricultural machinery operations. GPS technology is an indispensable technique in "precision agriculture". In the field of path planning, the evaluation of planned trajectories is based on GPS data. However, due to the unstructured operating environment, the existing methods for evaluating the efficiency of actual operating trajectories are not yet fully developed. In the context of agricultural machinery GPS trajectories, headland turning of the field is considered a non-working segment, which related to evaluation standard for trajectory planning efficiency in many studies. This paper proposes a processing method that evaluates GPS trajectories efficiency. A turning extraction algorithm based on changes in the angle of the agricultural machine's driving direction is proposed. The turning parts of the raw GPS data are preliminarily extracted, and a method for constructing a turning extraction dataset is presented. Then a multi-layer perceptron model for straight-turn binary classification training using K-Fold cross validation is designed and applied, which can implement robust segmentation of agricultural machinery trajectory points in actual environment. Two methods for building different turning classification datasets are proposed, and turning classification is performed using two deep learning models. A convolutional neural network-based model classifies headland turnings by their normalized sequences' visualization images, while a long short-term memory network-based model utilizes the trajectory point sequences. The CNN-based model performs relatively better than the LSTM-based model. Finally, some commonly used GPS trajectory efficiency evaluation standards in the field of agricultural machinery path planning are combined to evaluate the efficiency of actual GPS trajectories of agricultural machinery. [ABSTRACT FROM AUTHOR]

• A lightweight model for pig face recognition was developed using a fourth-order Runge-Kutta numerical method and a hybrid attention module. Subsequent experiments were conducted to evaluate its performance. • The model effectively enhances training and inference efficiency, reduces the number of parameters and memory usage, and improves accuracy in the extraction of pig face information. • Experimental tests show that the model achieves 99.26 % recognition accuracy and has a size of only 1.52 MB, which makes it suitable for deployment on embedded devices. • The heatmap visualization of the model's attention to the region of interest shows that the model pays more attention to the pig face region compared to other models. • The model can provide technical support for the scientific management of pig farms, thus improving the feasibility of deploying lightweight intelligent devices in smart pig farms. Pig face recognition plays a significant role in intelligent pig farming. Accurate and lightweight methods for recognizing pig faces are crucial for precision pig farming. Generally, under the condition of the same computing resources, the lower the image resolution, the faster the model inference speed. Based on the above idea, this paper proposes a lightweight pig face model called RKNet-HAM, based on the fourth-order Runge-Kutta and hybrid attention mechanism. This model exhibits the most prominent focus on the semantic information of low-resolution(64 × 64) pig face images and achieves high accuracy in recognizing similar pig faces with a low error classification rate. A dataset of 21,742 images of 32 pigs was constructed for individual pig recognition. The proposed RKNet-HAM model and several other deep learning models, e.g., RKNet-CBAM, RKNet, ShuffleNetV2.0, DesNet121, and ResNet50. RKNet-HAM has a size of 1.52 Megabytes. Experimental results show that among these models, the proposed model ofachieved the highest accuracy, precision, recall, and specificity, with an accuracy rate of 99.26 %. RKNet-HAM also exhibits good generalization ability. It provides experimental support for mobile and embedded applications. [ABSTRACT FROM AUTHOR]

The recent technological advances and their applications to agriculture provide leverage for the new paradigm of smart agriculture. Remote sensing applications can help optimise resources, making agriculture more ecological, increasing productivity and helping farmers to anticipate events that could not otherwise be avoided. Considering that losses caused by anomalies such as diseases, weeds and pests account for 20–40 % of overall agricultural productivity, a successful research effort in this area would be a breakthrough for agriculture. In this paper, we propose a methodology with which to discover and classify anomalies in images of crops, taken from a wide range of distances, using different Convolutional Neural Network architectures. This methodology also deals with several difficulties that usually appear in this kind of problems, such as class imbalance, the insufficient and small variety of images, overtraining or lack of models generalisation. We have implemented four convolutional neural network architectures in a high-performance computing environment, and propose a methodology based on data augmentation with the addition of Gaussian noise to the images to solve the above problems. Our approach was tested using two well-established open datasets that are unalike: DeepWeeds, which provides a classification of 8 weed species native to Australia using images that were taken at a distance of 1 m, and Agriculture-Vision, which classifies 6 types of crop anomalies using multispectral satellite imagery. Our methodology attained accuracies of 98 % and 95.3% respectively, improving the state-of-the-art by several points. In order to ease reproducibility and model selection, we have provided a comparison in terms of computational time and other metrics, thus enabling the choice between architectures to be made according to the resources available. The complete code is available in an open repository in order to encourage reproducibility and promote scientific advances in sustainable agriculture. • A convolutional deep learning model was developed for anomaly detection in agricultural images. • High and low-resolution imagery can be used as input for the model. • Data transformation and augmentation are part of our methodology. • Extensive experiments comparing 4 architectures and 2 datasets are shown. [ABSTRACT FROM AUTHOR]

Nowadays, the agriculture constitutes one of the leading sources of foods and it contributes to the national income and livelihood of most countries. However, in the last two decades, the agriculture is facing several challenges such as the increasing demand for food, the extreme weather conditions, the rising of climate change, etc. Hence, in order to overcome these challenges, the agriculture needs to be smarter by integrating new technologies with the aim to enhance the productivity and reduce the agriculture cost and waste. Recently, the emergence of Internet of Things (IoT) technology has led to a new revolution in the agriculture called as smart agriculture. Such technology consists of a set of sensors that allow to remotely monitoring the field conditions and sending real-time data about their status to the farmer for a decision-making. However, the smart agriculture suffers from the huge amount of collected data that complicates the decision-making process of the farmer and consumes the limited available energy of the sensors. In this paper, we propose a real-time AGRiculture mOnitoring, called AGRO, mechanism for efficiently sensing the soil moisture of a field and enhancing the irrigation system. AGRO divides the monitored field into small zones, called grids, where each grid assigned a grid leader (GL). Then, AGRO consists of three phases: smart sensing, energy saving and decision-making. The first phase aims to efficiently sense the soil moisture condition and update the farmer about the progress of the field status while reducing the amount of data transmitted to the GL. The second phase studies the variation of the field status during successive period times and allows to adapt the sensing frequency of the sensor in order save its energy and extend its lifetime. The last phase aims to study the data collected by the sensors at the same grid in order to allow the farmer to take a right decision based on a predefined decision table. We conducted a set of simulations in order to show the efficiency of our mechanism in terms of data reduction, energy conservation and accurate decision-making compared to other existing techniques. [ABSTRACT FROM AUTHOR]

Precision agriculture focuses on the development of site-specific harvest considering the variability of each crop area. Vegetation indices allow the study and delineation of different characteristics of each field zone, generally invisible to the naked-eye. This paper introduces a new big data triclustering approach based on evolutionary algorithms. The algorithm shows its capability to discover three-dimensional patterns on the basis of vegetation indices from vine crops. Different vegetation indices have been tested to find different patterns in the crops. The results reported using a vineyard crop located in Portugal depicts four areas with different moisture stress particularities that can lead to changes in the management of the vineyard. Furthermore, scalability studies have been performed, showing that the proposed algorithm is suitable for dealing with big datasets. [ABSTRACT FROM AUTHOR]

Focusing on buckling problem under hydrostatic pressure of ring stiffened shells and shells with stiffened cutout fabricated by filament winding process, a new form of equilibrium equations is given in this paper based on high-order shear deformation theory (HSDT). A novel theoretical model is proposed based on Messager's model of geometrical imperfection. The model contains both ring stiffened shell model (RSSM) and stiffened shell with cutout model (SSCM). In this model, changes in the shells' thickness z ˜ k (x , y) , k = 1,2 , ... , n are described using a Fourier series representation, which is included in HSDT stiffness matrix A, B, D, E, F, H. To evaluate the critical buckling pressure p c r , the model is applied and compared with the conventional stiffened cylindrical shell (SCS) model. The results demonstrate a significant reduction in error, ranging from 71.08% to 81.22%, when using the proposed model for shells stiffened by a ring stiffener in the middle, as compared to the SCS model. This highlights the enhanced accuracy provided by the proposed model. The results indicate that small stiffened cutouts in thin shells and large stiffened cutouts in moderately thick shells have relatively small influence on the critical buckling pressure. Conversely, the presence of a ring stiffener in the middle has a more pronounced effect. • Theoretical model contains ring stiffened shell model (RSSM) and the stiffened shell with cutout model (SSCM) is proposed. • A new form of equilibrium equations is given in this paper based on the high-order shear deformation theory (HSDT). • The middle ring stiffener shows a great effect on the global buckling load, while the small cutout shows little. [ABSTRACT FROM AUTHOR]

• Microwave detection and image processing have great potential for livestock farming. • Continuous detection and early disease and aberrant behavior identification are made possible by non-contact sensing technology. • Integration of non-contact sensing technologies in smart farms to enhance precision livestock farming practices and improve efficiency. • This paper also summarizes the advantages, challenges and prospects of non-contact sensing technology. More efficient, sustainable, and precise agri-sensing technologies will be adopted in the future to promote precision livestock farming (PLF). Through a detailed survey of research related to sensing technology, we believe that non-contact sensing technology (infrared detection technology, Microwave radiometry technology, Image processing and machine vision, and acoustic detection technology) hold promise for application in intensive livestock production system. Animal welfare has always been a concern of the scientific community, as well as improving production efficiency has been the goal of the intensive livestock industry. With the help of the Internet of Things, big data, artificial intelligence, etc., non-contact sensing technology can better play its role. In this paper, we analyzed the background and technical characteristics of non-contact sensing technology and proposed a new application for applying non-contact sensing technology to intensive livestock farming. These technologies are now gradually being applied and practiced in the livestock industry and will facilitate physiological detection in the intensive livestock industry, potentially protecting animal welfare. The precise adoption of management measures based on better knowledge of animal status can also improve production efficiency. In addition, this paper discusses the advantages, challenges, and prospects of non-contact sensing technology in livestock farming. [ABSTRACT FROM AUTHOR]

• Package types had a great influence on the mechanical damage of pears. • Positions resulted in different response vibration of pears. • Models to predict mechanical damage of pears under vibration level were developed. • Links between mechanical damage of pears and vibration duration were developed. The mechanical damage of fresh fruit under random vibration during postharvest transportation is a major problem in agricultural industry. This paper investigated the mechanical damage of 'Huangguan' pear (Pyrus bretschneideri Rehd. 'Huangguan') under random vibration excitation considering package type, vibration level, and vibration duration. Limited white noise with the frequency 3 Hz – 80 Hz was used as the excitation spectrum. Results showed: vibration results in the obvious decline of pear firmness. Firmness of pears with five package types after vibration decreased 9 % to 26 %. Package type had a great effect on the mechanical damage of pears. The package type of expanded polystyrene (EPS) tray + expandable polyethylene (EPE) net cover can provide the best cushioning effect for pears. The response vibrations of pears are related to positions. The resonant frequencies of stacked pears were concentrated in the range of 23 Hz–56 Hz. Response vibrations of pears in the corner were more severe compared with the pears in the center. In addition, the damage rules of pears with the package type of corrugated paperboard division + paper wrapping under different vibration levels and durations were revealed. The relationships of damage area and vibration level, firmness and vibration level, damage area and vibration duration conformed to linear correlation. Exponential function can be used to describe the relationships of damage volume and acceleration level, damage volume and vibration duration, firmness and vibration duration. This study provides references for vibration resistant packaging design of fruit, and thus to minimize the mechanical damage of fruit in supply chain. [ABSTRACT FROM AUTHOR]

Weed and crop segmentation is becoming an increasingly integral part of precision farming that leverages the current computer vision and deep learning technologies. Research has been extensively carried out based on images captured with a camera from various platforms. Unmanned aerial vehicles (UAVs) and ground-based vehicles including agricultural robots are the two popular platforms for data collection in fields. They all contribute to site-specific weed management (SSWM) to maintain crop yield. Currently, the data from these two platforms is processed separately, though sharing the same semantic objects (weed and crop). In our paper, we have proposed a novel method with a new deep learning-based model and the enhanced data augmentation pipeline to train field images alone and subsequently predict both field images and UAV images for weed segmentation and mapping. The network learning process is visualized by feature maps at shallow and deep layers. The results show that the mean intersection of union (IOU) values of the segmentation for the crop (maize), weeds, and soil background in the developed model for the field dataset are 0.744, 0.577, 0.979, respectively, and the performance of aerial images from an UAV with the same model, the IOU values of the segmentation for the crop (maize), weeds and soil background are 0.596, 0.407, and 0.875, respectively. To estimate the effect on the use of plant protection agents, we quantify the relationship between herbicide spraying saving rate and grid size (spraying resolution) based on the predicted weed map. The spraying saving rate is up to 90 % when the spraying resolution is at 1.78 × 1.78 cm2. The study shows that the developed deep convolutional neural network could be used to classify weeds from both field and aerial images and delivers satisfactory results. To achieve this performance, it is crucial to perform preprocessing techniques that reduce dataset differences between two distinct domains. [ABSTRACT FROM AUTHOR]

• Precision agriculture method applicable to remote on-tree pomegranate pixel-based fruit size monitoring. • Comparison of Deep Learning and zero-shot vision Transformer instance segmentation models. • Image-Based size estimation algorithm for occluded fruits with protuberances. In situ automatic fruit monitoring is of great interest for more accurate and cost-efficient decision making in agriculture. For this purpose, the development of computer vision-based tools is essential. Deep Learning techniques have shown good performance in fruit detection and segmentation. Recently, new models based on Transformer architecture have emerged with promising potential and zero-shot inference capability. In this paper, a Deep Learning model, Mask R-CNN, was trained for on-tree pomegranate fruit segmentation and compared with foundational models based on Vision Transformer, Grounding DINO and Segment Anything Model. Results with Mask R-CNN proved a better performance, according to F1 score and AP metrics, and a lower computational cost, according to prediction time. One of the most interesting derived applications from fruit segmentation is fruit size estimation. However, segmented fruit masks are frequently incomplete due to occlusions. Therefore, image fruit size estimation is not a straightforward process. In this work, we also propose a novel algorithm to estimate and monitor the fruit size in pixel units from the automated masks. A median relative error of 1.39% was obtained, demonstrating the potential and feasibility of future fully-automatic fruit size estimators. [ABSTRACT FROM AUTHOR]

The past decade has witnessed the rapid development and adoption of machine and deep learning (ML & DL) methodologies in agricultural systems, showcased by great successes in applications such as smart crop management, smart plant breeding, smart livestock farming, precision aquaculture farming, and agricultural robotics. However, these conventional ML/DL models have certain limitations: they heavily rely on large, costly-to-acquire labeled datasets for training, require specialized expertise for development and maintenance, and are mostly tailored for specific tasks, thus lacking generalizability. Recently, large pre-trained models, also known as foundation models (FMs), have demonstrated remarkable successes in language, vision, and decision-making tasks across various domains. These models are trained on a vast amount of data from multiple domains and modalities. Once trained, they can accomplish versatile tasks with just minor fine-tuning and minimal task-specific labeled data. Despite their proven effectiveness and huge potential, there has been little exploration of applying FMs to agriculture artificial intelligence (AI). Therefore, this study aims to explore the potential of FMs in the field of smart agriculture. In particular, conceptual tools and technical background are presented to facilitate the understanding of the problem space and uncover new research directions in this field. To this end, recent FMs in the general computer science (CS) domain are reviewed, and the models are categorized into four categories: language FMs, vision FMs, multimodal FMs, and reinforcement learning FMs. Subsequently, the process of developing agriculture FMs (AFMs) is outlined and their potential applications in smart agriculture are discussed. In addition, the unique challenges and risks associated with developing AFMs are discussed, including model training, validation, and deployment. Through this study, the advancement of AI in agriculture is explored by introducing AFMs as a promising paradigm that can significantly mitigate the reliance on extensive labeled datasets and enhance the efficiency, effectiveness, and generalization of agricultural AI systems. To facilitate further research, a well-classified and actively updated list of papers on AFMs is organized and accessible at https://github.com/JiajiaLi04/Agriculture-Foundation-Models. • Basics of large language and foundation models. • Review of potential applications of large language and foundation models in agriculture. • Outline challenges and opportunities. [ABSTRACT FROM AUTHOR]

• AI-driven tools are pivotal for optimizing agriculture productivity in developing and emerging economies. • Big data and ML serve as catalysts for sustainable practices. •.Standardized frameworks is essential for comparing AI in agriculture, fostering collaboration among stakeholders. • Tailored digital solutions are imperative for overcoming specific agricultural challenges. • Collaboration of government, private sector, and educational institutions is essential to bring innovative technologies and methodologies. Agriculture is the backbone of numerous developing and emerging economies, supporting millions of livelihoods and playing a crucial role. The transformative integration of AI-driven tools, big-data analytics, and advanced technologies, such as drones and satellites, can transform agricultural decision-making in these developing and emerging economies and globally. Acknowledging the intricate web of biophysical and socioeconomic factors that influence agricultural systems, we highlight the pressing need for innovative approaches. We explore the critical role of big-data analytics, Computational Fluid Dynamics (CFD), Machine Learning (ML), and remote sensing in transforming the sector, with a focus on enhancing efficiency, sustainability, and productivity. In this context, continuous real-time monitoring becomes essential, allowing farmers to manage their agricultural systems with precision. Implementing AI-driven tools and devices enables the collection and analysis of data in real time, leading to timely decisions that can significantly improve crop yields and resource management. This study discusses the impacts of big-data analytics on agriculture, including reduced pesticide use and improved crop yields. It presents concrete examples of AI applications in crop and animal farming, emphasizing precision irrigation, fertilizer management, animal health monitoring, and reproductive efficiency. We advocate for customizing these technologies to suit local contexts, which can lead to ensuring deep integration into developing and emerging economies' agricultural practices. In conclusion, the paper calls for collaborative efforts from governments, the private sector, and educational institutions to make these technologies accessible, affordable, and culturally relevant. [ABSTRACT FROM AUTHOR]

By quantifying the soil water movement (SWM) in crop planting systems, we can better understand the soil water consumption (SWC) and crop yield relationship; this finding is significant for determining the field water cycle and reducing agricultural water waste. In this paper, a case study was conducted on cotton production. Soil moisture sensors were set at depths of 10–110 cm under three cotton cropping systems (monoculture cotton (MC), wheat/delayed intercropped cotton (WIC), and wheat/direct-seeded cotton (WDC)) based on spatial grid methods; a geostatistical grid calculus was used to calculate SWM; and the crop and meteorological influence mechanisms on cotton lint yield were comprehensively analyzed. At the squaring stage, SWC and vertical SWM were significantly correlated with light, temperature and water conditions. At the flowering and boll development stage, SWC and vertical SWM were collectively affected by meteorological conditions and crops, and they were positively correlated with lint yield. The aboveground and belowground biomass accumulation at the flowering and boll development stage positively affected vertical SWM in and between cotton rows. Vertical SWM in cotton rows increased SWC in cotton rows. SWC in cotton rows and aboveground biomass positively impacted lint yield formation; SWC between rows negatively impacted lint yield. The SWC and vertical SWM between rows in the MC seedling stage exceeded those in cotton rows, and more precise irrigation at the seedling stage reduced water waste. The WIC horizontal SWC at the squaring and flowering and boll opening stages was relatively high, moving from the row midline to cotton row. A better SWC distribution in and between cotton rows promoted water utilization in the cotton rows; this method was feasible for improving cotton yield in diverse planting systems. The results could optimize precision irrigation management at different cotton growth stages and provide a theoretical reference for promoting sustainable agricultural production and climate adaptation. [Display omitted] • Quantifying soil water movement using novel geostatistical grid calculus method. • Diversified planting systems promotes horizontal soil water movement. • Water and cotton yield relationship are strongest at flowering and boll development stage. • Soil water consumption and vertical movement in cotton rows increase lint yield. [ABSTRACT FROM AUTHOR]

• Described the current status of land-leveling implements. • Analyzed the current status of laser-controlled leveling technology. • Analyzed the current status of GNSS-control leveling technology. • Analyzed the benefits of the application of land-leveling technology. • Discussed the challenges and future trends of land-leveling technology. Land leveling technology is necessary for land cultivation and is an important support for sustainable agricultural development. First, this paper reviews the current status of land-leveling implements, including dry-land and paddy-field leveling implements. Second, two precision land-leveling technologies, laser-controlled systems and the Global Navigation Satellite System (GNSS), are reviewed. The current status of laser-controlled leveling technology is considered in terms of its three primary components, the laser transmitter, laser receiver, and control terminal. The current status of GNSS-controlled leveling technology is also considered in terms of three aspects: three-dimensional (3D) topographical measurement of farmland, calculation of reference height and soil volume, and planning of work paths. The actual benefits of land-leveling technology applied to the production of different crops are statistically analyzed using metrics including improved land utilization, water savings, and increased yields. Finally, the challenges and future trends in land-leveling implements and technologies are discussed. [ABSTRACT FROM AUTHOR]

In this study, we propose the use of Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) to generate synthetic data for crop recommendation (CR). CR is critical in agriculture, assisting farmers in making informed decisions about crop cultivation, considering factors like soil conditions, weather patterns etc. Unfortunately, the availability of labeled data for CR is often limited, posing a significant challenge in training accurate recommendation models. VAEs and GANs are employed to create synthetic data that closely mirrors real-world crop data. VAEs are utilized to extract latent representation from the input data, enabling the generation of new samples with similar characteristics. GANs play a crucial role in generating data by training a generator network to produce synthetic samples that closely resemble real data, while a discriminator network distinguishes between genuine and synthetic data. The generated synthetic data serves as a valuable resource to prepare datasets for CR, enhancing the performance of recommendation models. Our research explores the effectiveness of VAEs and GANs in producing high-quality synthetic CR data, facilitating improved training and evaluation of recommendation systems. This paper presents the architecture and training process of the proposed models and evaluates the quality and utility of the generated synthetic data using various experiments, including visualizations such as heatmaps, scatter plots, cumulative sum per feature plots, and distribution per feature plots. The results of this study hold the potential to make a significant contribution to the field of agriculture by providing a reliable and abundant source of training data for CR systems. • AI-driven synthetic data boosts crop recommendation. • VAE and GAN create realistic crop data. • Overcoming data scarcity in agriculture. • Addressing limited labeled data in crop recommendation. • Potential impact: abundant training data for agriculture. [ABSTRACT FROM AUTHOR]

A profitable agriculture system is the fundamental foundation of a rising economy. Precise prediction of crop yield focuses primarily on agriculture research that has a significant effect on making decisions such as import-export, pricing, and distribution of specific crops. There is a severe need to utilize advanced technologies in order to improve yield quality and creation, anticipate crop yields, and study crop diseases/infections. The most prevalent issue among farmers is that they do not select the appropriate crop based on their soil needs. As a result, they see a significant decrease in production. In this paper, we presented a deep reinforcement learning (DRL)-based crop classification system for precision agriculture selection to solve the farmers' dilemma. DRL-based advanced agriculture techniques eliminate bad options and boost production in the crop recommendation system. We compared the proposed recommendation system with the various machine learning algorithms, such as Random Tree, Naive Bayes, and K-Nearest Neighbor, for a site-specific crop with effective accuracy. [ABSTRACT FROM AUTHOR]

[Display omitted] • Employing the FDEA method to determine the suitable regions for sugarcane cultivation considering sustainability criteria. • Developing an environmental MILP model to design a sugarcane-based bioenergy supply chain network. • Using the robust stochastic approach to treat the inherent uncertainty in this supply chain network. • Applying the proposed approach in a real case study in Iraq. Reasons such as volatile fossil fuel prices, environmental limitations, and swift economic growth have increased the demand for clean renewable energy. Bioenergy has been received much attention from researchers due to its various benefits compared to other renewable energy alternatives. In this paper, a three-stage approach is developed to design a sustainable sugarcane-based bioenergy supply chain network under an uncertain environment. The fuzzy data envelopment analysis method is employed in the first stage to determine the appropriate regions for opening the sugarcane fields according to climatic, ecological, and social criteria. The nominated regions are then integrated into a mathematical optimization model as candidate locations for sugarcane fields to configure the proposed supply chain design. This stage helps reduce the computational complexity and provides a reasonable solution by excluding the inappropriate regions. In the second stage, a robust mixed-integer linear programming model (MILP) is formulated to optimize a set of strategic and tactical decision variables. The enviromental impacts from CO2 equivalent emissions (CO2-eq emissions), water, and energy consumption in all echelons of the supply chain are incorporated in the developed model. This model is capable of providing a steady design against different expected scenarios. In the last stage, an experimental analysis is conducted, to examine the performance of the developed model in comparison with an expected scenario model. A real case study in Iraq is applied to check the applicability of the developed model. The results demonstrated the superiority of the proposed robust model over the expected scenario model in terms of the mean and standard deviation of their objective functions by 18% and 51% respectively. [ABSTRACT FROM AUTHOR]

Precision farming is made possible by rapid advances in deep learning (DL) and the internet of things (IoT) for agriculture, allowing farmers to upgrade their agriculture operations to sustainably fulfill the future food supply. This paper presents a comprehensive overview of recent research contributions in DL and IoT for precision agriculture. This paper surveys the diverse research on DL applications in agriculture, such as detecting pests, disease, yield, weeds, and soil, including fundamental DL techniques. Also, the work describes the IoT architecture and analyzes sensor categorization, agriculture sensors, and unmanned arial vehicles (UAVs) used in recent research. Besides that, data acquisition, annotation, and augmentation for agriculture datasets were covered, and a few widely used datasets were listed. This work also discusses some challenges and issues that DL and IoT face. Furthermore, the research proposed a bootstrapping approach of Transfer learning where fine-tuned VGG16 is fused with optimized and improved newly built fully connected layers for pest detection. The performance of the proposed model is evaluated and compared with other models, such as custom VGG16 as a classifier; fine-tuned VGG16 is optimized with other optimizers like SGD, RMSProp, and Adam. The results show that the proposed model for pest detection outperforms all other models with an accuracy of 96.58 % and a loss of 0.15%. The review and the proposed work presented in this paper will significantly direct researchers toward DL and IoT for intelligent farming. • Precision agriculture is made possible by combining recent advances in Deep Learning (DL) and Internet of Things (IoT). • DL applications in precision agriculture were discussed such as detection of pest/disease, soil, yield, and more. • IoT architecture, smart devices like sensors and UAVs for smart agriculture were discussed. • Data set study covers data acquisition, augmentation, annotation, and also identifies benchmark dataset for smart agriculture. • The paper proposed a bootstrap approach, where fine-tuned VGG16 is fused with improved dense layers for plant pest detection. [ABSTRACT FROM AUTHOR]

Counting objects in images is a very time-consuming task for humans that yields to errors caused by repetitiveness and boredom. In this paper, we present a novel object counting method that, unlike most of the recent works that focus on the regression of a density map, performs the counting procedure by localizing each single object. This key difference allows us to provide not only an accurate count but the position of every counted object, information that can be critical in some areas such as precision agriculture. The method is designed in two steps: first, a CNN is in charge of mapping arbitrary objects to blob-like structures. Then, using a Laplacian of Gaussian (LoG) filter, we are able to gather the position of all detected objects. We also propose a semi-adversarial training procedure that, combined with the former design, improves the result by a large margin. After evaluating the method on two public benchmarks of isometric objects, we stay on par with the state of the art while being able to provide extra position information. [ABSTRACT FROM AUTHOR]

• A systematic review of applications of weed management in precision agriculture. • Review of 60 technical research papers on weed detection in the past decade. • Investigated research gaps on deep learning techniques in weed detection. • Novel deep learning approaches were discussed for weed identification. Deep Learning (DL) has been described as one of the key subfields of Artificial Intelligence (AI) that is transforming weed detection for site-specific weed management (SSWM). In the last demi-decade, DL techniques have been integrated with ground as well as aerial-based technologies to identify weeds in still image context and real-time setting. After observing the current research trend in DL-based weed detection, techniques are advancing by assisting precision weeding technologies to make smart decisions. Therefore, the objective of this paper was to present a systematic review study that involves DL-based weed detection techniques and technologies available for SSWM. To accomplish this study, a comprehensive literature survey was performed that consists of 60 closest technical papers on DL-based weed detection. The key findings are summarized as follows, a) transfer learning approach is a widely adopted technique to address weed detection in majority of research work, b) less focus navigated towards custom designed neural networks for weed detection task, c) based on the pretrained models deployed on test dataset, no one specific model can be attributed to have achieved high accuracy on multiple field images pertaining to several research studies, d) inferencing DL models on resource-constrained edge devices with limited number of dataset is lagging, e) different versions of YOLO (mostly v3) is a widely adopted model for detecting weeds in real-time scenario, f) SegNet and U-Net models have been deployed to accomplish semantic segmentation task in multispectral aerial imagery, g) less number of open-source weed image dataset acquired using drones, h) lack of research in exploring optimization and generalization techniques for weed identification in aerial images, i) research in exploring ways to design models that consume less training hours, low-power consumption and less parameters during training or inferencing, and j) slow-moving advances in optimizing models based on domain adaptation approach. In conclusion, this review will help researchers, DL experts, weed scientists, farmers, and technology extension specialist to gain updates in the area of DL techniques and technologies available for SSWM. [ABSTRACT FROM AUTHOR]