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This paper proposes a flexible path-planning method for conducting agricultural observation tasks using multiple autonomous guided vehicles. The observation task was formulated as the rural postman problem by graphing the agricultural field to be observed and assigning costs to mandatory and optional paths. The observation area was re-divided according to the progress of each robot's work, and the latest path was planned to minimize the difference between the working time of individual robots, thereby reducing the overall working time. Simulations were conducted to verify the influence of the parameters used in the proposed method. The effect of workload adjustment on progress delay was verified, and the increase in overall working time owing to delay was reduced by an average of 32.9%. A performance comparison was conducted using Google OR-Tools, software specialized for combinatorial optimization. Superior solutions with shorter computation times were obtained using the proposed method. [ABSTRACT FROM AUTHOR]

The segmentation and extraction of oilseed rape crop rows are crucial steps in visual navigation line extraction. Agricultural autonomous navigation robots face challenges in path recognition in field environments due to factors such as complex crop backgrounds and varying light intensities, resulting in poor segmentation and slow detection of navigation lines in oilseed rape crops. Therefore, this paper proposes VC-UNet, a lightweight semantic segmentation model that enhances the U-Net model. Specifically, VGG16 replaces the original backbone feature extraction network of U-Net, Convolutional Block Attention Module (CBAM) are integrated at the upsampling stage to enhance focus on segmentation targets. Furthermore, channel pruning of network convolution layers is employed to optimize and accelerate the model. The crop row trapezoidal ROI regions are delineated using end-to-end vertical projection methods with serialized region thresholds. Then, the centerline of oilseed rape crop rows is fitted using the least squares method. Experimental results demonstrate an average accuracy of 94.11% for the model and an image processing speed of 24.47 fps/s. After transfer learning for soybean and maize crop rows, the average accuracy reaches 91.57%, indicating strong model robustness. The average yaw angle deviation of navigation line extraction is 3.76°, with a pixel average offset of 6.13 pixels. Single image transmission time is 0.009 s, ensuring real-time detection of navigation lines. This study provides upper-level technical support for the deployment of agricultural robots in field trials. [ABSTRACT FROM AUTHOR]

The development of autonomous agricultural robots using a global navigation satellite system aided by real-time kinematics and an inertial measurement unit for position and orientation determination must address the accuracy, reliability, and cost of these components. This study aims to develop and evaluate a robotic platform with autonomous navigation using low-cost components. A navigation algorithm was developed based on the kinematics of a differential vehicle, combined with a proportional and integral steering controller that followed a point-to-point route until the desired route was completed. Two route mapping methods were tested. The performance of the platform control algorithm was evaluated by following a predefined route and calculating metrics such as the maximum cross-track error, mean absolute error, standard deviation of the error, and root mean squared error. The strategy of planning routes with closer waypoints reduces cross-track errors. The results showed that when adopting waypoints every 3 m, better performance was obtained compared to waypoints only at the vertices, with maximum cross-track error being 44.4% lower, MAE 64.1% lower, SD 39.4% lower, and RMSE 52.5% lower. This study demonstrates the feasibility of developing autonomous agricultural robots with low-cost components and highlights the importance of careful route planning to optimize navigation accuracy. [ABSTRACT FROM AUTHOR]

Recently, the agriconstruction machinery sector has been involved in a great technological revolution. The reasons that may explain this are strictly connected to the mitigation of climate change. At the same time, there is a necessity to ensure an adequate production level in order to meet the increasing food demand due to the current population growth trend. In this context, the development of autonomously driven agricultural vehicles is one of the areas on which tractor manufacturers and academics are focusing. The fundamental prerequisite for an autonomous driving vehicle is the development of an appropriate motion strategy. Hence, the vehicle will be able to follow predetermined routes, accomplishing its missions. The aim of this study was the development of path-planning and path-following algorithms for an agricultural four-whee differential-drive vehicle operating in vineyard/orchard environments. The algorithms were completely developed within the MATLAB software environment. After a brief description of the geometrical characteristics of the vehicle, a parametric process to build a virtual orchard environment is proposed. Then, the functional principles of the autonomous driving algorithms are shown. Finally, the algorithms are tested, varying their main tuning parameters, and an indicator to quantify the algorithms' efficiency, named relative accuracy, is defined. The results obtained show the strong dependence between the relative accuracy and lookahead distance value assigned to the rover. Furthermore, an analysis of rover positioning errors was performed. The results in this case show a lower influence of the location error when the accuracy of the positioning device is within 2 cm. [ABSTRACT FROM AUTHOR]

The development of autonomous agricultural robots using a global navigation satellite system aided by real-time kinematics and an inertial measurement unit for position and orientation determination must address the accuracy, reliability, and cost of these components. This study aims to develop and evaluate a robotic platform with autonomous navigation using low-cost components. A navigation algorithm was developed based on the kinematics of a differential vehicle, combined with a proportional and integral steering controller that followed a point-to-point route until the desired route was completed. Two route mapping methods were tested. The performance of the platform control algorithm was evaluated by following a predefined route and calculating metrics such as the maximum cross-track error, mean absolute error, standard deviation of the error, and root mean squared error. The strategy of planning routes with closer waypoints reduces cross-track errors. The results showed that when adopting waypoints every 3 m, better performance was obtained compared to waypoints only at the vertices, with maximum cross-track error being 44.4% lower, MAE 64.1% lower, SD 39.4% lower, and RMSE 52.5% lower. This study demonstrates the feasibility of developing autonomous agricultural robots with low-cost components and highlights the importance of careful route planning to optimize navigation accuracy.

Recently, the agriconstruction machinery sector has been involved in a great technological revolution. The reasons that may explain this are strictly connected to the mitigation of climate change. At the same time, there is a necessity to ensure an adequate production level in order to meet the increasing food demand due to the current population growth trend. In this context, the development of autonomously driven agricultural vehicles is one of the areas on which tractor manufacturers and academics are focusing. The fundamental prerequisite for an autonomous driving vehicle is the development of an appropriate motion strategy. Hence, the vehicle will be able to follow predetermined routes, accomplishing its missions. The aim of this study was the development of path-planning and path-following algorithms for an agricultural four-whee differential-drive vehicle operating in vineyard/orchard environments. The algorithms were completely developed within the MATLAB software environment. After a brief description of the geometrical characteristics of the vehicle, a parametric process to build a virtual orchard environment is proposed. Then, the functional principles of the autonomous driving algorithms are shown. Finally, the algorithms are tested, varying their main tuning parameters, and an indicator to quantify the algorithms’ efficiency, named relative accuracy, is defined. The results obtained show the strong dependence between the relative accuracy and lookahead distance value assigned to the rover. Furthermore, an analysis of rover positioning errors was performed. The results in this case show a lower influence of the location error when the accuracy of the positioning device is within 2 cm.

Fruit- and vegetable-harvesting robots are a great addition to Agriculture 4.0 since they are gradually replacing human labor in challenging activities. In order to achieve the harvesting process accurately and efficiently, the picking robot's end-effector should be the first part to come into close contact with the crops. The design and performance requirements of the end-effectors are affected by the fruit and vegetable variety as well as the complexity of unstructured surroundings. This paper summarizes the latest research status of end-effectors for fruit- and vegetable-picking robots. It analyzes the characteristics and functions of end-effectors according to their structural principles and usage, which are classified into clamp, air suction, suction holding, and envelope types. The development and application of advanced technologies, such as the structural design of end-effectors, additional sensors, new materials, and artificial intelligence, were discussed. The typical applications of end-effectors for the picking of different kinds of fruit and vegetables were described, and the advantages, disadvantages, and performance indexes of different end-effectors were given and comparatively analyzed. Finally, challenges and potential future trends of end-effectors for picking robots were reported. This work can be considered a valuable guide to the latest end-effector technology for the design and selection of suitable end-effectors for harvesting different categories of fruit and vegetable crops. [ABSTRACT FROM AUTHOR]

The robustness and longevity of agricultural robots, specifically those utilizing caterpillar tracks for coconut harvesting, are based on understanding their strain, stress, and load thresholds. This study delves into the deformation characteristics of caterpillar track systems, pinpointing critical structural vulnerabilities and potential points of failure. Through a meticulous analysis of the maximum allowed strain and stress thresholds, this study unravels crucial insights to enhance performance and reliability in coconut field operations. Leveraging the power of ANSYS for structural analysis and simulation under varied constraints, this study aims to fortify the structural integrity of agricultural robots. By offering valuable insights and solutions, this study paves the way for advancements in agricultural robotics technology, ensuring that these machines can endure rigorous tasks while maintaining peak functionality. [ABSTRACT FROM AUTHOR]

This study addresses the challenges of low detection precision and limited generalization across various ripeness levels and varieties for large non-greenripe citrus fruits in complex scenarios. We present a high-precision and lightweight model, YOLOC-tiny, built upon YOLOv7, which utilizes EfficientNet-B0 as the feature extraction backbone network. To augment sensing capabilities and improve detection accuracy, we embed a spatial and channel composite attention mechanism, the convolutional block attention module (CBAM), into the head's efficient aggregation network. Additionally, we introduce an adaptive and complete intersection over union regression loss function, designed by integrating the phenotypic features of large non-green-ripe citrus, to mitigate the impact of data noise and efficiently calculate detection loss. Finally, a layerbased adaptive magnitude pruning strategy is employed to further eliminate redundant connections and parameters in the model. Targeting three types of citrus widely planted in Sichuan Province--navel orange, Ehime Jelly orange, and Harumi tangerine--YOLOC-tiny achieves an impressive mean average precision (mAP) of 83.0%, surpassing most other state-of-the-art (SOTA) detectors in the same class. Compared with YOLOv7 and YOLOv8x, its mAP improved by 1.7% and 1.9%, respectively, with a parameter count of only 4.2M. In picking robot deployment applications, YOLOC-tiny attains an accuracy of 92.8% at a rate of 59 frames per second. This study provides a theoretical foundation and technical reference for upgrading and optimizing low-computing-power ground-based robots, such as those used for fruit picking and orchard inspection. [ABSTRACT FROM AUTHOR]

[Objectives] In response to the impact of fragmented fields and obstacles on agricultural robot operations in hilly areas, this study aimed to propose a multi field block path planning method based on a combination of improved ant colony algorithm and A algorithm. [Methods] By using drones to construct remote sensing images of farmland, using grid method to model the environment of farmland, and then dividing and merging sub regions, a region heuristic function was introduced into the ant colony algorithm to distinguish the movement costs in different regions. The pheromone update method was improved by manually increasing the concentration of pheromones in the optimal path and establishing an adaptive pheromone volatilization coefficient, in order to solve the optimal traversal order of job sub intervals. The A algorithm with heuristic search function was used for sub region connection path planning, ultimately achieving multi field path planning. [Results] The MATLAB simulation test results showed that when the number of sub regions was 40, the average path length planned using the improved ant colony algorithm was reduced by 3.19% compared to the traditional ant colony algorithm, and the average iteration convergence number was reduced by 79.5%. In the path traversal simulation experiment, the coverage rate of the agricultural robot's traversal path could reach 100%, and the path repetition rate was 6.48%. The effectiveness of this method was further verified through simulation experiments of natural environment using remote sensing images of agricultural drones and field operation parameters. [Conclusions] The method proposed in this study could effectively solve the multi field path planning problem of agricultural robots in hilly areas, and provided path reference and technical support for large-scale operation of agricultural robots in hilly areas. [ABSTRACT FROM AUTHOR]

The goal of achieving autonomous navigation for agricultural robots poses significant challenges, mostly arising from the substantial natural variations in crop row images as a result of weather conditions and the growth stages of crops. The processing of the detection algorithm also must be significantly low for real-time applications. In order to address the aforementioned requirements, we propose a crop row detection algorithm that has the following features: Firstly, a projective transformation is applied to transform the camera view and a color-based segmentation is employed to distinguish crop and weed from the background. Secondly, a clustering algorithm is used to differentiate between the crop and weed pixels. Lastly, a robust line-fitting approach is implemented to detect crop rows. The proposed algorithm is evaluated throughout a diverse range of scenarios, and its efficacy is assessed in comparison to four distinct existing solutions. The algorithm achieves an overall intersection over union (IOU) of 0.73 and exhibits robustness in challenging scenarios with high weed growth. The experiments conducted on real-time video featuring challenging scenarios show that our proposed algorithm exhibits a detection accuracy of over 90% and is a viable option for real-time implementation. With the high accuracy and low inference time, the proposed methodology offers a viable solution for autonomous navigation of agricultural robots in a crop field without damaging the crop and thus can serve as a foundation for future research.

Point-cloud semantic segmentation is a visual task essential for agricultural robots to comprehend natural agroforestry environments. However, owing to the extremely large amount of point-cloud data in agroforestry environments, learning effective features for semantic segmentation from large-scale point clouds is challenging. Therefore, to address this issue and achieve accurate semantic segmentation of different types of road-surface point clouds in large-scale agroforestry environments, this study proposes a point-cloud semantic segmentation network framework based on double-distance self-attention. First, a point-cloud local feature enhancement module is proposed. This module primarily extends the receptive field and enhances the generalizability of multidimensional features by incorporating reflection intensity information and a spatial feature-encoding block that is enhanced with contextual semantic information. Second, we introduce a dual-distance attention pooling (DDAPS) block based on the self-attention mechanism. This block initially learns the feature representation of the local neighborhood of each point through the self-attention mechanism. Then, it uses the DDAPS block to aggregate more discriminative local neighborhood point features. Finally, extensive experimental results on large-scale point-cloud datasets, SemanticKITTI and RELLIS-3D, demonstrate that our algorithm outperforms similar algorithms in large-scale agroforestry environments.

Gardening pruning robots are widely applied in green space construction. However, increase of green space environment complexity and obstacle number affect the coverage range and work efficiency of robots. To solve this problem, this research proposed a full-coverage path planning algorithm integrating hybrid genetic ant colony and A* algorithm. Specifically tailored to the lawn working environments of horticultural pruning robots, we initially employed visual simultaneous localization and mapping to create a 3D point cloud map, converting it into an occupancy grid map for future path planning. The obtained grid map was partitioned into multiple subareas on the basis of the locations of obstacles. The optimal traversal order of sub-regions was determined using hybrid genetic ant colony method and a new update strategy of heuristic and pheromone factors was developed for improving the ability of global search and probability of jumping out of local optimal solution. Boustrophedon method was applied to fully cover each sub-region, A* algorithm was adopted to connect various sub-regions, and connection strategy was optimized. Simulation results showed that compared with traditional ant colony algorithm and other full-coverage planning algorithms, the algorithm developed in this research presented superior performance in terms of traversal path length, starting distance, coverage rate and turning times on maps with various sizes and complexities. [ABSTRACT FROM AUTHOR]

Copyright of Journal of South China Agricultural University is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South China Agricultural University is the property of Gai Kan Bian Wei Hui and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

For robotic precision spray application in vegetable farms, simultaneous accurate instance segmentation and robust tracking of plants are of great importance and a prerequisite for the following spray action. With onboard cameras, agricultural robots can apply Multiple Object Tracking and Segmentation (MOTS) methods, for instance, segmentation and tracking of plants. By assigning a unique identification for each vegetable, it ensures the robot to spray each vegetable exactly once, while traversing along the farm rows. Conventional MOTS methods, which are mostly designed for tracking pedestrians or vehicles, usually extract their color and texture features for associating different targets in consecutive images. However, vegetable plants of the same species normally show similar color and texture, which leads to degraded performance when conventional MOTS methods are used. To solve the challenging problem of associating vegetables with similar color and texture in consecutive images, in this paper, a novel MOTS method that exploits contour and blob features is proposed, for instance, segmentation and tracking of multiple vegetable plants. The method takes advantage of the fact that different plants normally possess different shape contours and blob properties. With images captured on top of them, these features of the same plant show little difference in consecutively captured images. Comprehensive experiments including ablation studies are conducted, which prove its superior performance over two state‐of‐the‐art MOTS methods. Compared with the conventional MOTS methods, the proposed method is able to re‐identify objects which have gone out of the camera field of view and re‐appear again using the proposed data association strategy, which is important to ensure each vegetable be sprayed only once when the robot travels back and forth. Although the method is tested on lettuce farm, it can be applied to other similar vegetables, such as broccoli and canola. Both the code and the dataset of this paper are publicly released for the benefit of the community: https://github.com/NanH5837/LettuceMOTS. [ABSTRACT FROM AUTHOR]

Considering the vulnerability of satellite positioning signals to obstruction and interference in orchard environments, this paper investigates a navigation and positioning method based on real-time kinematic global navigation satellite system (RTK-GNSS), inertial navigation system (INS), and light detection and ranging (LiDAR). This method aims to enhance the research and application of autonomous operational equipment in orchards. Firstly, we design and integrate robot vehicles; secondly, we unify the positioning information of GNSS/INS and laser odometer through coordinate system transformation; next, we propose a dynamic switching strategy, whereby the system switches to LiDAR positioning when the GNSS signal is unavailable; and finally, we combine the kinematic model of the robot vehicles with PID and propose a path-tracking control system. The results of the orchard navigation experiment indicate that the maximum lateral deviation of the robotic vehicle during the path-tracking process was 0.35 m, with an average lateral error of 0.1 m. The positioning experiment under satellite signal obstruction shows that compared to the GNSS/INS integrated with adaptive Kalman filtering, the navigation system proposed in this article reduced the average positioning error by 1.6 m. [ABSTRACT FROM AUTHOR]

Aiming at the robotic pruning of tomatoes in greenhouses, a new PRRPR configuration robotic arm consisting of two prismatic (P) joints and three revolute (R) joints was designed to locate the end effector to handle randomly growing branches with an appropriate posture. In view of the various spatial posture of the branches, drawing on the skill of manual pruning operation, we propose a description method of the optimal operation posture of the pruning end effector, proposing a method of solving the inverse kinematics of the pruning arm based on the multi-objective optimization algorithm. According to the spatial distribution characteristics of the tomato branches along the main stem, the robotic arm structure is compact and the reachable space is maximized as the objective function, and a method of optimizing the key geometric parameters of the robotic arm is proposed. The optimal maximum length of the arm's horizontal slide joint was determined to be 953.149 mm and the extension maximum length of its telescopic joint was 632.320 mm. The verification test of the optimal structural parameter showed that the optimized robotic arm could reach more than 89.94% of the branches in the pruning target area with a posture that meets the pruning requirements. This study is supposed to provide technical support for the development of a tomato pruning robot. [ABSTRACT FROM AUTHOR]

In the context of rapid global population growth and climate change, balancing agricultural productivity with environmental sustainability has never been more important. Precision farming technologies, including robotics, are touted as having huge potential to increase farm productivity, reduce energy and resource use and compact soil, while reducing the overall environmental impact of on-farm production. This comprehensive study presents, for the first time, a detailed analysis and environmental benchmarking of two organic sugar beet production (SBP) systems, conventional (CONV) and robotic (RBT), based on field experiments in Lithuanian conditions where a solar-powered robot is integrated into the production system to carry out sowing and weeding operations. In order to reduce the potential environmental impact and to understand the consequences of using the robot in agriculture, a Life Cycle Assessment (LCA) of the entire SBP process up to the factory gate was carried out. The results of the analysis show that the conventional system has higher total GHG emissions than the robotic system, 36.98 and 27.18 kg CO2eq t−1, respectively, with poultry manure being the largest contributor. The higher beet yield in the RBT system, mainly due to effective weed control, resulted in a higher GHG emissions ratio (14.72) and a higher sustainability index (13.72). The LCA results showed that the CONV system had a higher negative environmental impact than the RBT in all eleven environmental impact categories assessed, with the most pronounced difference in the Ozone Depletion (OD) category. Diesel fuel was identified as the most important environmental factor for organically growing sugar beet in all considered impact categories, with the most notable environmental impact (about 94%) in the terrestrial ecotoxicity category in both systems. Normalization of the results showed that marine aquatic ecotoxicity (ME) had the greatest (78%) influence of all exposure categories for both cultivation systems, CONV – 22079.82, and RBT 18121.61 kg 1.4-DBeq per ton of produced sugar beet. The study found that increasing yields and reducing fossil fuel use in organic farming are the two most promising strategies for achieving sustainability and efficiency in food production.

Batteries are crucial in transitioning from fossil fuels to clean-powered mobility, for several applications such as Electric Vehicles and Agricultural Mobile Robots (AMRs). However, the adoption of AMRs is limited by several challenges related to battery management, including restricted operation time, long recharge periods, and safe operation. The State of Charge (SOC) provides information about the remaining energy in the battery and is essential for battery management. Therefore, an accurate SOC estimation is crucial to ensure safe and reliable operation, which is needed to overcome the aforementioned challenges. This paper proposes, implements, and validates an SOC estimation system for low-cost AMRs. The accuracy of the SOC estimation is improved by adding information about the battery’s Open Circuit Voltage (OCV) to the Equivalent Circuit Models (ECM). Two SOC estimation methods based on ECM were implemented and validated for a Lithium Iron Phosphate battery (LFP) and a Sealed Lead Acid (SLA) battery powering an AMR. Finally, the results indicate that adding the OCV information to the models improves the estimation accuracy for both chemistries, being particularly interesting for LFP batteries, whose OCV vs. SOC has a flat area in almost the entire useful region of the SOC.

There is a growing demand for tea harvesting robots due to the harsh harvesting environment and rising in labor costs. Despite considerable efforts by the research community, the robustness to hilly field environment and the harvesting efficiency of the device in mechanical harvesting of bulk tea remain unimproved. To lay the foundation for automated tea harvesting, this paper proposes an integrated system of bulk tea harvesting robots with autonomous profiling logic based on computer vision. A saliency detection algorithm is applied to detect tender leaves, and a target localization system is designed with it. Moreover, a depth cue-based profiling logic and the corresponding pose adjustment strategy for cutting tool are detailed. And an actuator driven by a combination of motors is developed to provide a precise and flexible motion control to the cutting tool. Additionally, the standard position of the RGB-D camera, accuracy and timeliness of the profiling operation are confirmed by the field experiment. The results of field experiment show that the average harvesting accuracy is 87.7%, and failure rate is controlled within 15%. An analysis of failure causes reveals that damage and cutting failure are the primary reasons for the unsuccessful harvesting.

[Significance]Autonomous and intelligent agricultural machinery, characterized by green intelligence, energy efficiency, and reduced emissions, as well as high intelligence and man-machine collaboration, will serve as the driving force behind global agricultural technology advancements and the transformation of production methods in the context of smart agriculture development. Agricultural robots, which utilize intelligent control and information technology, have the unique advantage of replacing manual labor. They occupy the strategic commanding heights and competitive focus of global agricultural equipment and are also one of the key development directions for accelerating the construction of China's agricultural power. World agricultural powers and China have incorporated the research, development, manufacturing, and promotion of agricultural robots into their national strategies, respectively strengthening the agricultural robot policy and planning layout based on their own agricultural development characteristics, thus driving the agricultural robot industry into a stable growth period.[Progress]This paper firstly delves into the concept and defining features of agricultural robots, alongside an exploration of the global agricultural robot development policy and strategic planning blueprint. Furthermore, sheds light on the growth and development of the global agricultural robotics industry; Then proceeds to analyze the industrial backdrop, cutting-edge advancements, developmental challenges, and crucial technology aspects of three representative agricultural robots, including farmland robots, orchard picking robots, and indoor vegetable production robots. Finally, summarizes the disparity between Chinese agricultural robots and their foreign counterparts in terms of advanced technologies. (1) An agricultural robot is a multi-degree-of-freedom autonomous operating equipment that possesses accurate perception, autonomous decision-making, intelligent control, and automatic execution capabilities specifically designed for agricultural environments. When combined with artificial intelligence, big data, cloud computing, and the Internet of Things, agricultural robots form an agricultural robot application system. This system has relatively mature applications in key processes such as field planting, fertilization, pest control, yield estimation, inspection, harvesting, grafting, pruning, inspection, harvesting, transportation, and livestock and poultry breeding feeding, inspection, disinfection, and milking. Globally, agricultural robots, represented by plant protection robots, have entered the industrial application phase and are gradually realizing commercialization with vast market potential. (2) Compared to traditional agricultural machinery and equipment, agricultural robots possess advantages in performing hazardous tasks, executing batch repetitive work, managing complex field operations, and livestock breeding. In contrast to industrial robots, agricultural robots face technical challenges in three aspects. Firstly, the complexity and unstructured nature of the operating environment. Secondly, the flexibility, mobility, and commoditization of the operation object. Thirdly, the high level of technology and investment required. (3) Given the increasing demand for unmanned and less manned operations in farmland production, China's agricultural robot research, development, and application have started late and progressed slowly. The existing agricultural operation equipment still has a significant gap from achieving precision operation, digital perception, intelligent management, and intelligent decision-making. The comprehensive performance of domestic products lags behind foreign advanced counterparts, indicating that there is still a long way to go for industrial development and application. Firstly, the current agricultural robots predominantly utilize single actuators and operate as single machines, with the development of multi-arm cooperative robots just emerging. Most of these robots primarily engage in rigid operations, exhibiting limited flexibility, adaptability, and functionality. Secondly, the perception of multi-source environments in agricultural settings, as well as the autonomous operation of agricultural robot equipment, relies heavily on human input. Thirdly, the progress of new teaching methods and technologies for human-computer natural interaction is rather slow. Lastly, the development of operational infrastructure is insufficient, resulting in a relatively low degree of "mechanization".[Conclusions and Prospects]The paper anticipates the opportunities that arise from the rapid growth of the agricultural robotics industry in response to the escalating global shortage of agricultural labor. It outlines the emerging trends in agricultural robot technology, including autonomous navigation, self-learning, real-time monitoring, and operation control. In the future, the path planning and navigation information perception of agricultural robot autonomy are expected to become more refined. Furthermore, improvements in autonomous learning and cross-scenario operation performance will be achieved. The development of real-time operation monitoring of agricultural robots through digital twinning will also progress. Additionally, cloud-based management and control of agricultural robots for comprehensive operations will experience significant growth. Steady advancements will be made in the innovation and integration of agricultural machinery and techniques.

Copyright of Journal of Intelligent Agriculture Mechanization is the property of Nanjing Institute of Agricultural Mechanization and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Complex farmland backgrounds and varying light intensities make the detection of guidance paths more difficult, even with computer vision technology. In this study, a robust line extraction approach for use in vision-guided farming robot navigation is proposed. The crops, drip irrigation belts, and ridges are extracted through a deep learning method to form multiple navigation feature points, which are then fitted into a regression line using the least squares method. Furthermore, deep learning-driven methods are used to detect weeds and unhealthy crops. Programmed proportional–integral–derivative (PID) speed control and fuzzy logic-based steering control are embedded in a low-cost hardware system and assist a highly maneuverable farming robot in maintaining forward movement at a constant speed and performing selective spraying operations efficiently. The experimental results show that under different weather conditions, the farming robot can maintain a deviation angle of 1 degree at a speed of 12.5 cm/s and perform selective spraying operations efficiently. The effective weed coverage (EWC) and ineffective weed coverage (IWC) reached 83% and 8%, respectively, and the pesticide reduction reached 53%. Detailed analysis and evaluation of the proposed scheme are also illustrated in this paper. [ABSTRACT FROM AUTHOR]

Autonomous and intelligent agricultural machinery, characterized by green intelligence, energy efficiency, and reduced emissions, as well as high intelligence and man-machine collaboration, will serve as the driving force behind global agricultural technology advancements and the transformation of production methods in the context of smart agriculture development. Agricultural robots, which utilize intelligent control and information technology, have the unique advantage of replacing manual labor. They occupy the strategic commanding heights and competitive focus of global agricultural equipment and are also one of the key development directions for accelerating the construction of China's agricultural power. World agricultural powers and China have incorporated the re‐ search, development, manufacturing, and promotion of agricultural robots into their national strategies, respectively strengthening the agricultural robot policy and planning layout based on their own agricultural development characteristics, thus driving the agricultural robot industry into a stable growth period. [Progress] This paper firstly delves into the concept and defining features of agricultural robots, alongside an exploration of the global agricultural robot development policy and strategic planning blueprint. Furthermore, sheds light on the growth and development of the global agricultural robotics industry; Then proceeds to analyze the industrial backdrop, cutting-edge advancements, developmental challenges, and crucial technology aspects of three representative agricultural robots, including farmland robots, orchard picking robots, and indoor vegetable production robots. Finally, summarizes the disparity between Chinese agricultural robots and their foreign counterparts in terms of advanced technologies. (1) An agricultural robot is a multi-degree-of-freedom autonomous operating equipment that possesses accurate perception, autonomous decision-making, intelligent control, and automatic execution capabilities specifically designed for agricultural environments. When combined with artificial intelligence, big data, cloud computing, and the Internet of Things, agricultural robots form an agricultural robot application system. This system has relatively mature applications in key processes such as field planting, fertilization, pest control, yield estimation, inspection, harvesting, grafting, pruning, inspection, harvesting, transportation, and livestock and poultry breeding feeding, inspection, disinfection, and milking. Globally, agricultural robots, rep‐ resented by plant protection robots, have entered the industrial application phase and are gradually realizing commercialization with vast market potential. (2) Compared to traditional agricultural machinery and equipment, agricultural robots possess advantages in per‐ forming hazardous tasks, executing batch repetitive work, managing complex field operations, and livestock breeding. In contrast to industrial robots, agricultural robots face technical challenges in three aspects. Firstly, the complexity and unstructured nature of the operating environment. Secondly, the flexibility, mobility, and commoditization of the operation object. Thirdly, the high level of technology and investment required. (3) Given the increasing demand for unmanned and less manned operations in farmland production, China's agricultural robot research, development, and application have started late and progressed slowly. The existing agricultural operation equipment still has a significant gap from achieving precision operation, digital perception, intelligent management, and intelligent decision-making. The comprehensive performance of domestic products lags behind foreign advanced counterparts, indicating that there is still a long way to go for industrial development and application. Firstly, the current agricultural robots predominantly utilize single actuators and operate as single machines, with the development of multi-arm cooperative robots just emerging. Most of these robots primarily engage in rigid operations, exhibiting limited flexibility, adaptability, and functionality. Secondly, the perception of multi-source environments in agricultural settings, as well as the autonomous operation of agricultural robot equipment, relies heavily on human input. Thirdly, the progress of new teaching methods and technologies for human-computer natural interaction is rather slow. Lastly, the development of operational infrastructure is insufficient, resulting in a relatively low degree of "mechanization". [Conclusions and Prospects] The paper anticipates the opportunities that arise from the rapid growth of the agricultural robotics industry in response to the escalating global shortage of agricultural labor. It outlines the emerging trends in agricultural robot technology, including autonomous navigation, self-learning, real-time monitoring, and operation control. In the future, the path planning and navigation information perception of agricultural robot autonomy are expected to become more refined. Furthermore, improvements in autonomous learning and cross-scenario operation performance will be achieved. The development of real-time operation monitoring of agricultural robots through digital twinning will also progress. Additionally, cloud-based management and control of agricultural robots for comprehensive operations will experience significant growth. Steady advancements will be made in the innovation and integration of agricultural machinery and techniques. [ABSTRACT FROM AUTHOR]

The segmentation and extraction of oilseed rape crop rows are crucial steps in visual navigation line extraction. Agricultural autonomous navigation robots face challenges in path recognition in field environments due to factors such as complex crop backgrounds and varying light intensities, resulting in poor segmentation and slow detection of navigation lines in oilseed rape crops. Therefore, this paper proposes VC-UNet, a lightweight semantic segmentation model that enhances the U-Net model. Specifically, VGG16 replaces the original backbone feature extraction network of U-Net, Convolutional Block Attention Module (CBAM) are integrated at the upsampling stage to enhance focus on segmentation targets. Furthermore, channel pruning of network convolution layers is employed to optimize and accelerate the model. The crop row trapezoidal ROI regions are delineated using end-to-end vertical projection methods with serialized region thresholds. Then, the centerline of oilseed rape crop rows is fitted using the least squares method. Experimental results demonstrate an average accuracy of 94.11% for the model and an image processing speed of 24.47 fps/s. After transfer learning for soybean and maize crop rows, the average accuracy reaches 91.57%, indicating strong model robustness. The average yaw angle deviation of navigation line extraction is 3.76°, with a pixel average offset of 6.13 pixels. Single image transmission time is 0.009 s, ensuring real-time detection of navigation lines. This study provides upper-level technical support for the deployment of agricultural robots in field trials.

Fruit- and vegetable-harvesting robots are a great addition to Agriculture 4.0 since they are gradually replacing human labor in challenging activities. In order to achieve the harvesting process accurately and efficiently, the picking robot’s end-effector should be the first part to come into close contact with the crops. The design and performance requirements of the end-effectors are affected by the fruit and vegetable variety as well as the complexity of unstructured surroundings. This paper summarizes the latest research status of end-effectors for fruit- and vegetable-picking robots. It analyzes the characteristics and functions of end-effectors according to their structural principles and usage, which are classified into clamp, air suction, suction holding, and envelope types. The development and application of advanced technologies, such as the structural design of end-effectors, additional sensors, new materials, and artificial intelligence, were discussed. The typical applications of end-effectors for the picking of different kinds of fruit and vegetables were described, and the advantages, disadvantages, and performance indexes of different end-effectors were given and comparatively analyzed. Finally, challenges and potential future trends of end-effectors for picking robots were reported. This work can be considered a valuable guide to the latest end-effector technology for the design and selection of suitable end-effectors for harvesting different categories of fruit and vegetable crops.

In the development of automated manipulators for fruit and vegetable picking technologies, the challenge of ensuring an efficient, stable, and loss-free picking process has been a complex problem. In such an environment, manipulators require the most efficient and robust control for effective operations. In this paper, a serial 9-DOF redundant manipulator (1P8R) is proposed with various controllers for trajectory tracking problems in agricultural applications. The dynamic analysis of redundant manipulator has been carried out using the Recursive Newton-Euler method. The joint configurations of the robot are determined using optimization techniques for specific Task Space Locations (TSLs) by avoiding obstacles. The process of generating joint trajectories has been implemented by considering the cubic polynomial function. For the task of controlling the robot trajectory tracking in the virtual agricultural environment, different combinations of Proportional (P), Integrative (I), Derivative (D), and Feed-Forward (FF) controllers are employed, and a comparative analysis has been performed among these controllers. To verify the performance of the manipulator, simulations are carried out in a virtual environment using Simulink software. Results show that the robot is able to reach specific TSL accurately with better control and it is found that the implementation of Feed-Forward and PID-CTC controllers has better performance in a complex environment.

Path planning is an essential algorithm in any autonomous mobile robot, including agricultural robots. One of the reinforcement learning methods that can be used for mobile robot path planning is the Q-Learning algorithm. However, the conventional Q-learning method explores all possible robot states in order to find the most optimum path. Thus, this method requires extensive computational cost especially when there are considerable grids to be computed. This study modified the original Q-Learning algorithm by removing the impassable area, so that these areas are not considered as grids to be computed. This modified Q-Learning method was simulated as path finding algorithm for autonomous mobile robot operated at the Agribusiness and Technology Park (ATP), IPB University. Two simulations were conducted to compare the original Q-Learning method and the modified Q-Learning method. The simulation results showed that the state reductions in the modified Q-Learning method can lower the computation cost to 50.71% from the computation cost of the original Q-Learning method, that is, an average computation time of 25.74s as compared to 50.75s, respectively. Both methods produce similar number of states as the robot’s optimal path, i.e. 56 states, based on the reward obtained by the robot while selecting the path. However, the modified Q-Learning algorithm is capable of finding the path to the destination point with a minimum learning rate parameter value of 0.2 when the discount factor value is 0.9.

Obstacle avoidance control and navigation in unstructured agricultural environments are key to the safe operation of autonomous robots, especially for agricultural machinery, where cost and stability should be taken into account. In this paper, we designed a navigation and obstacle avoidance system for agricultural robots based on LiDAR and a vision camera. The improved clustering algorithm is used to quickly and accurately analyze the obstacle information collected by LiDAR in real time. Also, the convex hull algorithm is combined with the rotating calipers algorithm to obtain the maximum diameter of the convex polygon of the clustered data. Obstacle avoidance paths and course control methods are developed based on the danger zones of obstacles. Moreover, by performing color space analysis and feature analysis on the complex orchard environment images, the optimal H-component of HSV color space is selected to obtain the ideal vision-guided trajectory images based on mean filtering and corrosion treatment. Finally, the proposed algorithm is integrated into the Three-Wheeled Mobile Differential Robot (TWMDR) platform to carry out obstacle avoidance experiments, and the results show the effectiveness and robustness of the proposed algorithm. The research conclusion can achieve satisfactory results in precise obstacle avoidance and intelligent navigation control of agricultural robots. [ABSTRACT FROM AUTHOR]

Legged agricultural transportation robots are efficient tools that can autonomously transport goods over agricultural terrain, and their introduction helps to improve the efficiency and quality of agricultural production. Their effectiveness depends on their adaptability to different environmental conditions, which is especially true for heavy-duty robots that exert ground forces. Therefore, this study proposes a motion-control strategy for a heavy-duty transport hexapod robot. Two critical tasks were accomplished in this paper: (1) estimating the support surface angle based on the robot's foot position and body posture, and accordingly determining the motion constraint conditions on this support surface and the body posture based on energy optimization; (2) proposing an adaptive fuzzy impedance algorithm for real-time force–position composite control for adjusting foot position, in order to reduce the steady-state force tracking error caused by terrain stiffness, thus ensuring body stability through tracking of variable foot-end forces. An element of hardware in the loop control platform for a 3.55-ton device was designed and compared with the current popular force-control methods under different external contact terrains. The results show that the proposed control method can effectively reduce force errors, establish support forces faster on less-stiff environments, and reduce the torso tilt during phase switching. [ABSTRACT FROM AUTHOR]

Introduction: The accurate extraction of navigation paths is crucial for the automated navigation of agricultural robots. Navigation line extraction in complex environments such as Panax notoginseng shade house can be challenging due to factors including similar colors between the fork rows and soil, and the shadows cast by shade nets. Methods: In this paper, we propose a new method for navigation line extraction based on deep learning and least squares (DL-LS) algorithms. We improve the YOLOv5s algorithm by introducing MobileNetv3 and ECANet. The trained model detects the seven-fork roots in the effective area between rows and uses the root point substitution method to determine the coordinates of the localization base points of the seven-fork root points. The seven-fork column lines on both sides of the plant monopoly are fitted using the least squares method. Results: The experimental results indicate that Im-YOLOv5s achieves higher detection performance than other detection models. Through these improvements, Im-YOLOv5s achieves a mAP (mean Average Precision) of 94.9%. Compared to YOLOv5s, Im-YOLOv5s improves the average accuracy and frame rate by 1.9% and 27.7%, respectively, and the weight size is reduced by 47.9%. The results also reveal the ability of DL-LS to accurately extract seven-fork row lines, with a maximum deviation of the navigation baseline row direction of 1.64°, meeting the requirements of robot navigation line extraction. Discussion: The results shows that compared to existing models, this model is more effective in detecting the seven-fork roots in images, and the computational complexity of the model is smaller. Our proposed method provides a basis for the intelligent mechanization of Panax notoginseng planting. [ABSTRACT FROM AUTHOR]

Aiming at the suboptimal local path, slow convergence speed, and many inflection points in the path planning of fruit and vegetable picking robots in complex environments, a global planning method combining particle swarm optimization (PSO) algorithm and A* algorithm was proposed. Firstly, Manhattan distance was taken as a heuristic function of global programming based on the A* algorithm. Secondly, the step size of PSO was adjusted to optimize the path, shorten the path length, and reduce the number of inflection points. Finally, the planned path of the fruit and vegetable picking robot was smoothed so that it could steadily move along a smoother driving path in real scenarios. The experimental results show that compared with the traditional PSO algorithm, the hybrid algorithm based on the improved A* algorithm and PSO algorithm achieves a smoother path and fewer folding points. In comparison with the PSO algorithm, moreover, this algorithm can guarantee the path generation efficiency and the global optimum. In the end, the effectiveness of the proposed method was verified by shortening the path length and reducing the accumulative number of inflection points. [ABSTRACT FROM AUTHOR]

Drivable area detection is crucial for the autonomous navigation of agricultural robots. However, semi-structured agricultural roads are generally not marked with lanes and their boundaries are ambiguous, which impedes the accurate segmentation of drivable areas and consequently paralyzes the robots. This paper proposes a deep learning network model for realizing high-resolution segmentation of agricultural roads by leveraging contextual representations to augment road objectness. The backbone adopts HRNet to extract high-resolution road features in parallel at multiple scales. To strengthen the relationship between pixels and corresponding object regions, we use object-contextual representations (OCR) to augment the feature representations of pixels. Finally, a differentiable binarization (DB) decision head is used to perform threshold-adaptive segmentation for road boundaries. To quantify the performance of our method, we used an agricultural semi-structured road dataset and conducted experiments. The experimental results show that the mIoU reaches 97.85%, and the Boundary IoU achieves 90.88%. Both the segmentation accuracy and the boundary quality outperform the existing methods, which shows the tailored segmentation networks with contextual representations are beneficial to improving the detection accuracy of the semi-structured drivable areas in agricultural scene. [ABSTRACT FROM AUTHOR]

Considering the vulnerability of satellite positioning signals to obstruction and interference in orchard environments, this paper investigates a navigation and positioning method based on real-time kinematic global navigation satellite system (RTK-GNSS), inertial navigation system (INS), and light detection and ranging (LiDAR). This method aims to enhance the research and application of autonomous operational equipment in orchards. Firstly, we design and integrate robot vehicles; secondly, we unify the positioning information of GNSS/INS and laser odometer through coordinate system transformation; next, we propose a dynamic switching strategy, whereby the system switches to LiDAR positioning when the GNSS signal is unavailable; and finally, we combine the kinematic model of the robot vehicles with PID and propose a path-tracking control system. The results of the orchard navigation experiment indicate that the maximum lateral deviation of the robotic vehicle during the path-tracking process was 0.35 m, with an average lateral error of 0.1 m. The positioning experiment under satellite signal obstruction shows that compared to the GNSS/INS integrated with adaptive Kalman filtering, the navigation system proposed in this article reduced the average positioning error by 1.6 m.

Acute shortages and rising costs of labour in the food and farming sector across Europe exhibit the fragility of agrifood systems. Agricultural robots present an opportunity to strengthen agrifood systems by addressing labour shortages and reduce CO2 emissions. This study proposes a method to investigate the potential use of an autonomous robotic system based on a case study on a typical high-tech Dutch farm that implements both an agricultural robot (AGR) and a conventional tractor (TRC) on a farm of 200 hectares in the region of Oldambt. To demonstrate the optimal use of an AGR, five farming operations (seeding, catch crop seeding, tine weeding, harrowing, inter-row hoeing, and spot spraying) in a crop rotation system of five crops (sugar beets, pumpkins, onions, spring barley and winter wheat) was chosen. The agricultural robot is here considered as a supplement (not substitute) to the farms overall cropping capability. It is found that price of fuel and labour are critical factors where higher fuel and labour price increases the benefit and use of the robot. Besides, time needed for remote supervision of the robot plays an important role.

In the development of automated manipulators for fruit and vegetable picking technologies, the challenge of ensuring an efficient, stable, and loss-free picking process has been a complex problem. In such an environment, manipulators require the most efficient and robust control for effective operations. In this paper, a serial 9-DOF redundant manipulator (1P8R) is proposed with various controllers for trajectory tracking problems in agricultural applications. The dynamic analysis of redundant manipulator has been carried out using the Recursive Newton-Euler method. The joint configurations of the robot are determined using optimization techniques for specific Task Space Locations (TSLs) by avoiding obstacles. The process of generating joint trajectories has been implemented by considering the cubic polynomial function. For the task of controlling the robot trajectory tracking in the virtual agricultural environment, different combinations of Proportional (P), Integrative (I), Derivative (D), and Feed-Forward (FF) controllers are employed, and a comparative analysis has been performed among these controllers. To verify the performance of the manipulator, simulations are carried out in a virtual environment using Simulink software. Results show that the robot is able to reach specific TSL accurately with better control and it is found that the implementation of Feed-Forward and PID-CTC controllers has better performance in a complex environment. [ABSTRACT FROM AUTHOR]

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Localized management based on multipoint measurements of crops and soil is expected to improve agricultural productivity. The difficulties with this process are as follows: it is time-consuming due to the many measurement points; it requires various sensors for various measurements; it can lead to unstable measurements due to sunlight and wind. To solve the above issues, the system we propose has the advantages of efficient measurements performed by a robot, various measurements performed through exchangeable sensor units, and stable measurements through protecting the crop from sunlight and wind. As a pilot study for the system, we conducted an experiment to simultaneously measure the crops and soil in a cabbage field. The robot achieved mobility in the field, >4 h of operation time, and the ability to obtain soil electrical conductivity measurements and crop imaging at 100 points. Furthermore, the stability of the measurement conditions within the sensor unit during the experiment was evaluated. Compared to the case without the covering, the illuminance became 280-fold stabler (standard deviation = 0.4 lx), and the wind-induced crop shaking became 20-fold lower (root mean square error of the image pixels = 0.5%). The potential impacts of this research include high reproducibility because of the shareable sensor unit and the expectation of new discoveries using precise indoor sensors. [ABSTRACT FROM AUTHOR]

Cucumbers, pumpkins, and muskmelons use flowering as one of the most important pollination processes. Natural pollinators such as bees and butterflies are declining, drastically reducing harvests. The aim of this research is to use UAV images to build deep transfer learning to detect floral patterns in autonomous pollination of cucurbit plants. The images taken by the drone equipped with an OpenMV camera from farm in various situation are collected and stored in the CuCuflower database for flower identification. For self-pollination, pollen area and other identifiable characteristics for classification of flower gender and family are observed. In particular, the fast detection and high accuracy of small, densely packed objects of a large farm in real time is still an open challenge. In this work, we address this challenge by finding and identifying small objects in a bloom of a complete image using the method of transfer learning based on YOLOv structure. However, the cheek component of the flower was relatively small compared to the total relative environmental image. The results revealed that the proposed model performs best in detecting the pollinating flower and the AP50 Momentum Factor with 0.95 is 91% and 92%, respectively, which are better than other detection methods. [ABSTRACT FROM AUTHOR]