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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]

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]

• 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]

• 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]

• 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 paper, we present an overview of several challenges in arable farming that are well suited for research by the control engineering society. We discuss the global needs that these challenges are related to as well as the relation of these challenges to future applications of arable farming. For each of these opportunities we provide several concrete and detailed research questions. Particular attention is paid to the management of resources and sensors in farms. The objective of writing this paper is to further entice control engineers into the domains of agronomy and agricultural technology. [ABSTRACT FROM AUTHOR]

Recently, smart agriculture has become an essential part of modern agriculture approaches from tillage, via plant seeding and grow support to their collection. With modern technologies, farmers can use substances like pesticides, herbicides, or fertilizers at precise dosages or to identify places on a field with specific production rates. The main objective of this study is to introduce a novel and a unique aerial image dataset of various fields acquired by UAV containing crops/weeds in the early phenophases captured in two different resolutions (2 mm and 7 mm per pixel). Secondly, the best super-resolution technique for high-resolution images, substitution with lower resolution is explored. For data acquisition, we employed DJI Matrice 600 equipped with a full-frame Sony Alpha A7R IV285 image sensor. Data were captured at flight heights of 26 and 95 m from 4 different fields in Central Europe. In addition, we proposed a methodology focused on the selection of an appropriate super-resolution method to enhance low-resolution aerial images to obtain better accuracy of crop/weed detection. As a baseline crop/weed detector for super-resolution effect evaluation, YOLOv5 architecture was used. Next, we explored the performance of several super-resolution models (U-Net++, ESRGAN, SwinIR), and fine-tuned the best-performed one. We present the new dataset named SPAGRI-AI: a novel unique dataset of aerial images for super-resolution experiments in smart precision agriculture. The dataset contains 27,638 aerial images (1024 × 1024 px) and additionally, it contains a subset of 2014 labeled images with 45,548 bounding boxes of 12 classes. The main purpose of the SPAGRI-AI is to provide the scientific community with real-world data to test new methods for super-resolution (SR) and crop/weed detection. During the evaluation of selected super-resolution models, the YOLOv5 model trained on high-resolution images resulted in corn mAP@0.5 of 94.48%. The YOLOv5 model trained on low-resolution images resulted in corn mAP@0.5 of only 51.43%. Nevertheless, if the low-resolution images were pre-processed using the SwinIR super-resolution method, corn mAP@0.5 of 62.36% was achieved. To the best of our knowledge, it is one of the largest datasets available to the paper's publication date. Overall, the SPAGRI-AI dataset and the findings from our experiments contribute to the advancement of super-resolution techniques and crop/weed detection methods in the field of smart agriculture. By utilizing real-world data and optimizing image enhancement approaches, we paved the way for further developments in precision farming practices and applying emerging technologies in agriculture. [Display omitted] • Dataset consists of 27,638 aerial images captured at two different flight heights (26 and 95 m). • Dataset contains a subset of 2014 labeled images with 45,548 bounding boxes across 12 distinct classes. • Baseline model trained on higher resolution data (127 PPI) achieved up to 95% of mAP@0.5 for corn detection. • Model employing super-resolution method on lower resolution data (36 PPI) improved detection performance by 11%. [ABSTRACT FROM AUTHOR]

In smart agriculture, the accurate prediction of evapotranspiration plays a crucial role in optimizing water usage and maximizing crop yield. However, the increasing adoption of IoT sensor technologies has resulted in the accumulation of large amounts of data, which are frequently contaminated by noise and pose a significant challenge to extract reliable knowledge through data modeling. This research addresses the problem of noisy IoT sensor data and its impact on evapotranspiration prediction, an essential aspect of agricultural practices. The effect of noise on sensor variables and evapotranspiration is extensively analyzed by simulating different noise levels in evapotranspiration datasets collected from various agricultural areas in Spain, enabling a comprehensive evaluation of its impact on the performance of data science models. Despite the potential consequences of this type of errors, a noise preprocessing stage is often overlooked in existing literature in this field, which is necessary to improve data quality prior to modeling. In order to address this challenge, this paper proposes the usage of regression noise filters as approach to mitigate the detrimental effects of noisy IoT sensor data on evapotranspiration prediction. Additionally, we introduce the rgnoisefilt R package, which offers a practical and efficient implementation of noise filtering techniques for regression datasets, providing a valuable solution for handling noisy data in smart agriculture applications. The experimental results obtained emphasize the negative impacts of noise on evapotranspiration prediction performance and highlight the importance of an appropriate data treatment to mitigate system deterioration. Furthermore, the findings of this research emphasize the efficacy of the regression noise filters implemented in the rgnoisefilt software, enhancing the performance of the models built and providing a valuable tool for improving data quality in smart agriculture. • Noisy IoT sensor data in evapotranspiration prediction are addressed. • This research analyzes the impact of different types of noise in the data. • Regression noise filters are proposed as a tool to improve data quality. • The rgnoisefilt R package provides efficient solutions for noisy sensor data. • The effectiveness of filters with noisy evapotranspiration data is shown. [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]

This study used machine learning, particularly k-means clustering, to identify distinct clusters of users and their technological readiness to adopt various precision livestock farming (PLF) technologies based on their responses to a carefully designed questionnaire. The analysis revealed initially two as well as three distinct clusters representing different levels of technological readiness among farmers considering the adoption of various PLF technologies. In addition to the validation of the cluster results by internal metrics, a related principal component analysis, and a focus group evaluation, this paper describes the application of a Decision Tree as an explainable supervised machine learning approach to investigate the predictive power of specific survey questions. In combination, this research aims to provide valuable insights for understanding farmers' technological readiness, to enhance further survey designs, and to support the development of targeted strategies to promote the successful adoption of PLF technologies in the agricultural sector generally. • Technological readiness of pig and poultry farms was explored. • Two and three clusters of technological readiness were observed by K-Means. • User characteristics for targeted interventions have been highlighted. • Importance of survey questions for cluster identification was calculated through Entropy. • Results have been validated by internal metrics, a PCA, and a Focus Group. [ABSTRACT FROM AUTHOR]

Smart agriculture – i.e., the increasing use of information technologies, sensors, autonomous vehicles, data analytics, predictive modelling, and other digital technologies related to agricultural activities – has been strongly argued for as a means to significantly contribute to increased food security, reduced water consumption, reduced fertilizer and pesticide input, and increased farm profitability. Despite this, the adoption rate of smart agricultural technologies is still low and varies significantly according to the specific technology and the geographical area considered. The goals of this paper are to: (1) propose a conceptual framework for smart agriculture and digital twins, which takes into account the needs and characteristics of the farms; (2) present the application of the proposed conceptual framework as a case study; and (3) shed light on the challenges of and the future perspectives on smart agriculture. We first propose a framework for the design of farm information systems consisting of four key phases (i.e., data collection, data processing, data analysis and evaluation, and information use) based on the infological approach. We then apply the framework to present and discuss a field application of smart agriculture and digital twins on crop nitrogen (N) fertilization. The case study, along with the cited literature, highlights the need to specify the optimal N fertilizer input as well as defining the spatial variability of the land area, the soil characteristics and crop yield, and the integration of these with temporal variability. Finally, we discuss challenges and future perspectives, with particular focus on geographical areas characterized by small average farm size. We argue that, thanks to digital twins, the wide set of data collected can enable predictive (and stability) analyses that if implemented can benefit the farmer and the environmental, social, and economic sustainability of the agricultural system. • We propose a conceptual framework for smart agriculture and digital twin. • We apply the proposed conceptual framework to a case study of N fertilization management. • We shed light on challenges and future perspectives on smart agriculture. [ABSTRACT FROM AUTHOR]

• This paper showcases the potential of information and communication technologies in traditional agriculture, as well as the issues to be encountered when they are applied to farming practices. • The challenges of robotics, IoT devices, and machine learning, as well as the roles of machine learning, artificial intelligence, and sensors used in agriculture, are all described in detail. In addition, drones are under consideration for conducting crop surveillance as well as for managing crop yield optimisation. • Additionally, whenever appropriate, global and state-of-the-art IoT-based farming systems and platforms are mentioned. We perform a detailed study of the recent literature in each field of our work. • From this extensive review, we conclude that the current and future trends of artificial intelligence (AI) and identify current and upcoming research challenges on AI in agriculture. The digitalization of data has resulted in a data tsunami in practically every industry of data-driven enterprise. Furthermore, man-to-machine (M2M) digital data handling has dramatically amplified the information wave. There has been a significant development in digital agriculture management applications, which has impacted information and communication technology (ICT) to deliver benefits for both farmers and consumers, as well as pushed technological solutions into rural settings. This paper highlights the potential of ICT technologies in traditional agriculture, as well as the challenges that may arise when they are used in farming techniques. Robotics, Internet of things (IoT) devices, and machine learning issues, as well as the functions of machine learning, artificial intelligence, and sensors in agriculture, are all detailed. In addition, drones are being considered for crop observation as well as crop yield optimization management. When applicable, worldwide and cutting-edge IoT-based farming systems and platforms are also highlighted. We do a thorough review of the most recent literature in each area of expertise. We conclude the present and future trends in artificial intelligence (AI) and highlight existing and emerging research problems in AI in agriculture due to this comprehensive assessment. [ABSTRACT FROM AUTHOR]

The terms Internet of Things and Big Data are currently subject to much attention, though the specific impact of these terms in our practical lives are difficult to apprehend. Data-driven approaches do lead to new possibilities, and significant improvements within a broad range of domains can be achieved through a cloud-based infrastructure. In the agricultural sector, data-driven precision agriculture shows great potential in facilitating the increase in food production demanded by the increasing world population. However, the adoption rate of precision agriculture technology has been slow, and information and communications technology needed to promote the implementation of precision agriculture is limited by proprietary integrations and non-standardized data formats and connections. In this paper, an open geospatial data infrastructure is presented, based on standards defined by the Open Geospatial Consortium (OGC). The emphasis in the design was on improved interoperability, with the capability of using sensors, performing cloud processing, carrying out regional statistics, and provide seamless connectivity to machine terminals. The infrastructure was implemented through open source software, and was complemented by open data from governmental offices along with ESA satellite imagery. Four use cases are presented, covering analysis of nearly 50 000 crop fields and providing seamless interaction with an emulated machine terminal. They act to showcase both for how the infrastructure enables modularity and interoperability, and for the new possibilities which arise from this new approach to data within the agricultural domain. [ABSTRACT FROM AUTHOR]

• A goal point determination method is proposed for the problem of selecting the optimal path point by Pure Pursuit. • An evaluation function is established to select the optimal goal point based on tractor position prediction model. • The performance of the proposed path tracking algorithm is studied by field test. The development of precision agriculture requires the implementation of automatic navigation technology for agricultural machinery. Path tracking control as one of the key steps in automatic navigation technology has great research value. Pure Pursuit algorithm as a popular control algorithm for automatic navigation technology for agricultural machinery, the look-ahead distance is the key to the tracking effect, while the calculation of look-ahead distance has the problem that many influencing factors are difficult to be accurately described by mathematical expressions, which leads to the difficulty to select a suitable goal point tracking path. In order to solve the above problems, this paper proposes a path tracking algorithm for agricultural machinery based on the optimal goal point. The algorithm simulates the look-ahead behavior of the driver and searches for optimal goal point in the look-ahead area according to the evaluation function. The objective of the study is to minimize the lateral error and heading error to achieve the adaptive optimization of the goal point. Finally, the feasibility of the algorithm proposed in this paper was verified in simulations and bumpy field tests under various different conditions, with tracking errors reduced by more than 20% compared to the pure pursuit algorithm. The tracking accuracy is significantly improved. [ABSTRACT FROM AUTHOR]

• The density estimation clustering algorithm based on wheat ear-layer point cloud data separation is studied. • The application of 3D methods from indoor control models is analyzed under complex natural canopy conditions. • The number of crop plants was highly correlated with the number of point clouds on the ears after point cloud segmentation. • The test method can provide a reference for the non-destructive measurement of crops. The sustainable development of agriculture needs to rely on precision agricultural technology. The premise and key to realizing precision agriculture is the research on the characteristics of crops in the field. For wheat plants, the ear is the flower or fruit part at the top of the wheat stem, which is one of the important components of yield, and its density can be said to be one of the most important traits. Its number is arguably-one of the most important phenotypic traits. The traditional method of density measurement is manual, which is time-consuming and laborious. Therefore, an efficient and convenient wheat density estimation scheme is needed to provide data support for crop yield monitoring, to achieve a better production management system. Stereo vision is a 3D imaging method that allows rapid measurement of plant structures, and point cloud segmentation is the key to studying the 3D spatial characteristics of plants. In this paper, the three-dimensional point cloud of wheat reconstructed by stereo vision technology is used for segmentation and clustering, and a method for clustering dense wheat rows is proposed. Firstly, a binocular camera was used to record video to reconstruct the wheat point cloud; then, point cloud pre-processing was used to remove noise; then, the octree splitting algorithm and voxel mesh merging algorithm were used to divide the dense wheat, and then clustering algorithm was used to get the point cloud of wheat ears; finally, the relationship model between the number of wheat ears point clouds and the number of wheat plants was established by linear regression analysis with R 2 of 0.97. To verify the effectiveness of the algorithm, the actual field measurements and the predicted values of the algorithm were compared, with R 2 of 0.93. The density estimation method provides a new method for the study of phenotypic information of crop population information and also provides a reference for nondestructive crop measurements. [ABSTRACT FROM AUTHOR]

As an effective risk guarantee mechanism, crop insurance can not only disperse agricultural operation risk, but also guide the agricultural green development. Based on Chinese provincial panel data from 2002 to 2015, this paper uses SBM-GML (Global-Malmquist-Luenberger) index model to measure agricultural green total factor productivity and systematically examines the impact of crop insurance on agricultural green total factor productivity as well as its mechanism. This study supplements the determinants of agricultural green total factor productivity. It is found that crop insurance has a significant positive impact on agricultural green total factor productivity. The promotion effect of crop insurance on agricultural green total factor productivity increases with the expanded use of agricultural green technologies such as precision sowing, deep fertilization, subsoiling and no-tillage. Moreover, our results show that the role of crop insurance in promoting agricultural green total factor productivity increases with the increase in operational scales. In addition, compared with food crops, the promotion effect is stronger for cash crops. Therefore, the Chinese government should continue to support the implementation of crop insurance policy. It is necessary to not only increase crop insurance subsidies, but also set up differentiated subsidy systems in different operational scales and planting structures to promote agricultural green development. • Crop insurance has a significant positive impact on AGTFP. • The promotion effect of crop insurance on AGTFP increases with the expanded use of agricultural green technologies. • The role of crop insurance in promoting AGTFP increases with the increase in operational scales. • Compared with food crops, the promotion of crop insurance on AGTFP is stronger for cash crops. [ABSTRACT FROM AUTHOR]

• Challenges in the current generation of mobile network and the need for 5G in agricultural sector. • 5G based UAV navigation beyond line of sight for real time monitoring for agricultural applications. • Application of 5G in AI driven robots and cloud-based processing. • AR and VR takes precision farming to the next level with 5G network. Over the next decade, the superfast 5G network will play a critical role in farming industries to improve the yields and quality of crops while using minimal labor. Smart and precision farming allows farmers to be more informed and productive. The advent of 5G will considerably change the nature of jobs in farming and agriculture. The internet of things (IoT)-based cloud computing service in the 5G network provides flexible and efficient solutions for smart farming. This will allow the automated operation of various unmanned agricultural machines for the plowing, planting, and management phases of crop farming and will ultimately achieve secure, reliable, environmentally friendly, and energy-efficient operations and enable unmanned farms. This paper provides a complete survey on 5G technology in the agricultural sector and discusses the need for and role of smart and precision farming; benefits of 5G; applications of 5G in precision farming such as real-time monitoring, virtual consultation and predictive maintenance, data analytics and cloud repositories; and future prospects. [ABSTRACT FROM AUTHOR]