A task planning algorithm for a dual-arm cooperative strawberry harvesting robot is designed to efficiently and non-destructively harvest strawberries from ridges. The algorithm aims to plan strawberry harvesting attribution, harvesting sequence, dual-arm conflict avoidance and picking paths. A dual-arm dual-warehouse model with partially overlapping harvesting areas is established based on the harvesting principle and robot architecture. To further shorten the harvesting time, the constructed model subdivides the harvesting time into picking start moments, picking completion moments and harvesting end moments. Subsequently, a strawberry-arm dual sequence coding scheme and Genetic Cheetah Optimization (GCO) algorithm are designed to solve the task assignment problem, in which the shortest picking path is generated based on Rapidly Exploring Random Tree Stars (RRT*). In the simulation, a 3D real environment map of the strawberry site is reconstructed based on the real test site, and the task planning simulations is conducted based on the acquired strawberry information. The simulation result shows that the GCO algorithm reduces the total harvesting time and total accumulated idling time by 23.1% and 74.2%, respectively, compared with the spatial planning method when solving the picking problem of harvesting 39 strawberries. In addition, the path planning algorithm based on the RRT* algorithm resulted in a 17.5% and 18.1% reduction in the harvesting paths for the two arms, respectively, compared to the RRT algorithm. Moreover, without considering the failure of the first harvesting attempt, when the number of strawberries in the shared area is 5, 10, and 15, respectively, the GCO algorithm reduced the total harvesting time by 18.2%, 22.9%, and 34.5%, and the total accumulated idling time was reduced by 0%, 56.6%, and 81.9%. After a second attempt, the effect was more significant, with the total harvesting time and the total accumulated idling time reduced by 23.8% and 70.6%, respectively. The proposed model and the designed GCO algorithm are proven to be effective in performing dual-arm harvesting task, adjusting harvesting conflicts and improving operational efficiency.

Real-time, accurate monitoring of potato miss- and multi-seeding is critical for precision seeding, but challenged by motion blur induced by high-speed seed metering. Severe non-uniform, multi-scale blur causes a substantial loss of semantically discriminative features, rendering conventional "deblur-then-detect" pipelines ineffective. To overcome this, we propose a novel end-to-end GAN (Generative Adversarial Network)-based deblurring-target detection (GDTD) framework, underpinned by a synergistic training mechanism that jointly optimizes image restoration and object detection. Evaluated under high-speed potato seeding conditions with dynamic non-uniform blur, The GDTD achieved an mAP@0.5:0.95 (mean Average Precision) of 0.6843 and a Precision of 0.9115, outperforming two-stage fusion frameworks built on state-of-the-art deblurring (TSFDeblurGAN) and detection (YOLO-based LGCSA-Detector) models by up to 19.7% and 5.73%, respectively. Furthermore, it sustained real-time inference at a processing speed of 27.3 FPS with low power consumption, making it suitable for embedded agricultural devices. By shifting the optimization paradigm from pixel-level fidelity to task-aware semantic restoration, this study provides important theoretical and practical support for precision seeding and other dynamic scenarios in smart agriculture, demonstrating that task-driven semantic restoration, rather than pixel-perfect deblurring, is the key to robust detection under extreme motion blur.

In hydroponic leafy vegetable production, variation in seedling size can affect transplanting efficiency and growth uniformity, ultimately reducing market quality. Although machine vision has been widely applied to non-destructive seedling monitoring and grading, accurate seedling segmentation and quantitative classification remain challenging because of severe leaf overlap and complex canopy structures in densely planted trays. To address these challenges, this study proposes a quantitative grading framework for hydroponic lettuce seedlings based on an improved BlendMask network and graph-neural-network-assisted canopy optimization. The framework employs a ResNet50 backbone integrated with deformable convolutions (DCNs) and a DeepLabV3+ decoder to enhance multi-scale feature representation and leaf boundary extraction. A graph-neural-network-based canopy optimization algorithm was further developed to model spatial relationships among canopy fragments, suppress over-segmentation caused by overlapping leaves, and reconstruct biologically consistent canopy structures. The 95% confidence interval of the canopy projection area was adopted as the grading criterion for whole-tray quantitative classification. Experimental results showed that the proposed BlendMask-R50 + DCNi3 configuration achieved a precision of 98.2% and an AP of 72.0. After canopy optimization, seedling counting accuracy reached 98.0%–100% under different occlusion conditions, while grading accuracy remained above 93%. Repeated experiments on hundreds of seedling trays, each containing approximately 200 planting positions, demonstrated that the complete grading process was completed within 2.67 s per tray. These results indicate that the proposed framework provides an efficient and practical approach for automated lettuce seedling counting, canopy reconstruction, and quantitative grading in plant-factory production systems.

In response to the poor signal quality of satellite navigation in the highly shaded environment of the Panax notoginseng shade house and the navigation issues that may arise when tracks deviate significantly from their expected path in complex environments such as soft and sticky soil. This paper uses an enhanced YOLOv5s object detection algorithm and the least squares method to identify the mid-axis of the shade house. A fuzzy adaptive PID path tracking algorithm and a computer vision navigation control system based on the mid-axis were designed for the tracked robot. A tracked robot platform was constructed and a precision ginseng seeder with hole-pressing and seeding functions was designed. The path-tracking algorithm was developed in MATLAB/Simulink and simulation experiments were conducted. The simulation tests results showed that, even under conditions of significant environmental changes and disturbances, the proposed fuzzy adaptive PID algorithm outperformed conventional PID and fuzzy logic control. Field trials were conducted in a simulated environment. Experimental tests show that Im-YOLOv5 achieved an average accuracy of 94.9 % for the root base of ginseng, with maximum and average path deviations of 13.7 mm and 3.8 mm respectively for the tracked robot guided by the mid-axis. The robot exhibited no loss of control and was capable of navigating between rows within the shade house. Seedling experiments were conducted at various speeds. Field seeding pass rates exceeded 90 % in all cases and re-seeding and missed seeding rates were both controlled below 5 %, meeting operational standards. These results provide technical references for seeding and harvesting operations using shade house planting robots.

IntroductionThe openness grading of fresh-cut roses relies heavily on manual work, which can be inefficient and inconsistent.MethodsIn this study, an improved YOLOv8s model is proposed for openness grading in conjunction with a newly developed automatic grading machine for fresh-cut roses. The model identifies unopened inner petals and classifies openness into five levels: degree 1, degree 2, degree 3, degree 4, and deformity. To enhance detection accuracy while reducing the model complexity and computation, the backbone network of YOLOv8s is replaced by MobileNetV3. Additionally, an Efficient Multi-scale Attention (EMA) module is introduced to enhance focus on critical features, and a Wise-IoU loss function is incorporated to accelerate convergence.ResultsField experiments revealed that the openness predictions made by the automatic fresh-cut roses grader had errors of 6.9%, 9.1%, 10.0%, 6.5%, and 12.6%, respectively, compared to manual predictions.DiscussionTherefore, the improved YOLOv8s-F model effectively meets the requirements of fresh-cut rose openness grading.

Abstract In view of the problems of complex process and weak representation in regular atmospheric wind field detection, this paper adopts deep learning method, uses global high-altitude meteorological detection data, and establishes a deep learning model for calculating wind direction and speed with different altitudes and temperatures by using keras software package. The model is verified by using third-party independent sounding data from a meteorological observatory in Shanghai. The calculation accuracy of the model above 2000 m is 0.9830 and the value of loss function is 0.0482. The accuracy under 2000 m is 0.9164 and the value of loss function is 0.0377. There are significant differences in the performance of the model between under 2000 meters and above 2000 meters due to surface friction. The model shows that wind direction and speed of different height layers can be calculated by using only height and temperature at the same height. This model can also be used to check whether the quality of regular wind detection work is good or not with big old data.

With the increasing deployment of agricultural robots, the traditional manual spray of liquid fertilizer and pesticide is gradually being replaced by agricultural robots. For robotic precision spray application in vegetable farms, accurate plant phenotyping through instance segmentation and robust plant tracking are of great importance and a prerequisite for the following spray action. Regarding the robust tracking of vegetable plants, to solve the challenging problem of associating vegetables with similar color and texture in consecutive images, in this paper, a novel method of Multiple Object Tracking and Segmentation (MOTS) is proposed for instance segmentation and tracking of multiple vegetable plants. In our approach, contour and blob features are extracted to describe unique feature of each individual vegetable, and associate the same vegetables in different images. By assigning a unique ID for each vegetable, it ensures the robot to spray each vegetable exactly once, while traversing along the farm rows. Comprehensive experiments including ablation studies are conducted, which prove its superior performance over two State-Of-The-Art (SOTA) MOTS methods. Compared to 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 code and the dataset of this paper is publicly released for the benefit of the community: https://github.com/NanH5837/LettuceMOTS.

Consistent root orientation is one of the important requirements of Panax notoginseng transplanting agronomy. In this paper, a Panax notoginseng orientation transplanting method based on machine vision technology and negative pressure adsorption principle was proposed. With the cut-main root of Panax notoginseng roots as the detection object, the YOLOv5s was used to establish a root feature detection model. A Panax notoginseng root orientation transplanting device was designed. The orientation control system identifies the root posture according to the detection results and controls the orientation actuator to adjust the root posture. The detection results show that the precision rate of the model was 94.2%, the recall rate was 92.0%, and the average detection precision was 94.9%. The Box-Behnken experiments were performed to investigate the effects of suction plate rotation speed, servo rotation speed and the angle between the camera and the orientation actuator(ACOA) on the orientation qualification rate and root drop rate. Response surface method and objective optimisation algorithm were used to analyse the experimental results. The optimal working parameters were suction plate rotation speed of 5.73 r/min, servo rotation speed of 0.86 r/s and ACOA of 35°. Under this condition, the orientation qualification rate and root drop rate of the actual experiment were 89.87% and 6.57%, respectively, which met the requirements of orientation transplanting for Panax notoginseng roots. The research method of this paper is helpful to solve the problem of orientation transplanting of other root crops.

It is essential for precision air-assisted ground sprayer to accurately detect the leaf density of fruit trees in agriculture. However, it is difficult to achieve real-time measurement of canopy leaf densities by using conventional sensors (such as LIDAR and camera). In fact, the interaction between wind and canopies of different leaf densities may generate different excited-audios during air-assisted spraying. Therefore, this paper proposes a method for leaf-density detection of fruit-tree canopies based on the variance of wind-excited audios. First, the fused spectrogram (FSP) was constructed by the short-time fast Fourier transform (STFT), consisting of spectral features and the spectrogram of audio signals. Then, the estimation model between the FSP and leaf densities was developed by using deep convolutional neural network (DCNN), which was enhanced by a spectral centroid attention (SCA) based on the distribution function of spectral centroids. Finally, the test results showed that: (1) the developed model achieved a 96.93% accuracy and a 97.96% precision in leaf-density recognition, and (2) compared with unimproved DCNN model, the accuracy and precision of the developed model were increased about 7.85% and 8.47%, respectively, which indicated that this method could achieve the prediction of leaf-density based on wind-excited audios. The study is expected to provide a reference for leaf-density detection of fruit tree canopies and real-time control of precision spray.

It is important to obtain real-time leaf density of fruit-tree canopies for the precision spray control of plant-protection robots. However, conventional detection techniques for the characteristics of fruit-tree canopies cannot acquire the canopy internal information, which may provide an unsatisfactory accuracy of detection of leaf densities. This paper proposes a method for estimating canopy leaf density of fruit trees based on wind-excited audio. A wind-exciting implement was used to force fruit-tree canopy leaves vibrating to produce audio. Then, some correlation analysis methods were used to extract key characteristic parameters of wind-excited audio that were significantly correlated with leaf density. Finally, based on the data set of wind-excited audio, a few machine-learning methods were used to develop leaf-density estimation models. Test results showed that: (1) there were five key feature parameters of wind-excited audio that were significantly correlated with leaf density: the short-time energy, spectral centroid, the frequency average energy, the peak frequency, and the standard deviation of frequency. (2) the estimation model of leaf density developed based on backpropagation neural network for fruit-tree canopy showed the optimal estimation results, which can achieve the estimation of leaf density of fruit-tree canopies accurately. The overall correlation coefficient (R) of the estimation model was more than 0.84, the root-mean-square error was less than 0.73 m2 m-3, and the mean absolute error was less than 0.53 m2 m-3. This study is expected to provide a technical solution for the leaf-density detection of fruit-tree canopies of plant-protection robots.

The approach of dynamic tracking and counting for obscured citrus based on machine vision is a key element to realizing orchard yield measurement and smart orchard production management. In this study, focusing on citrus images and dynamic videos in a modern planting mode, we proposed the citrus detection and dynamic counting method based on the lightweight target detection network YOLOv7-tiny, Kalman filter tracking, and the Hungarian algorithm. The YOLOv7-tiny model was used to detect the citrus in the video, and the Kalman filter algorithm was used for the predictive tracking of the detected fruits. In order to realize optimal matching, the Hungarian algorithm was improved in terms of Euclidean distance and overlap matching and the two stages life filter was added; finally, the drawing lines counting strategy was proposed. ln this study, the detection performance, tracking performance, and counting effect of the algorithms are tested respectively; the results showed that the average detection accuracy of the YOLOv7-tiny model reached 97.23%, the detection accuracy in orchard dynamic detection reached 95.12%, the multi-target tracking accuracy and the precision of the improved dynamic counting algorithm reached 67.14% and 74.65% respectively, which were higher than those of the pre-improvement algorithm, and the average counting accuracy of the improved algorithm reached 81.02%. The method was proposed to effectively help fruit farmers grasp the number of citruses and provide a technical reference for the study of yield measurement in modernized citrus orchards and a scientific decision-making basis for the intelligent management of orchards.

Electrical capacitance tomography (ECT) is a technique to visualize the cross-sectional permittivity distribution in the sensing domain from inter-electrode capacitance measurements on the domain boundary. Conventional image reconstruction methods suffer from the low spatial resolution, as ECT inverse problem is inherently nonlinear, ill-conditioned and ill-posed. Deep learning methods has made considerable achievements and has great potential in ameliorating the reconstruction quality of ECT. Generative adversarial network (GAN) is a typical framework developed in the field of deep learning in recent years to solve image processing problems with ill-posed nature. This paper proposed a novel method based on deep convolutional generation adversarial networks (DCGAN) to enhance image quality of reconstructed images, especially for accurate shape reconstruction. The proposed method can abstract the mapping information from the low-resolution images reconstructed by a conventional reconstruction algorithm to the high-resolution target images, and then recover high-resolution images through an adversarial learning process. In this paper, the low-quality images reconstructed by the Landweber iterative algorithm are used as input for the framework of the proposed method. Numerical results validate the superiority of the DCGAN-based method in the enhancement of the ECT images, i.e., giving more accurate reconstructions of complex-shaped inclusions with lower image errors than Landweber iterative algorithm.

Vision based autonomous navigation is widely used for agricultural robots. However, factors such as large area of weed, discontinuous crop rows, and differences in ambient lighting condition during different plant growth stages have brought challenges to autonomous robotic navigation in farms. This paper presents a vision based method of fusing vegetation index and ridge segmentation for robust and precise extraction of navigation lines in lettuce farms. Firstly, vegetation index from the captured image is computed, and farm ridges are extracted using a semantic segmentation net. Then, vegetation index and ridge segmentation result are fused to obtain plant segmentation result. Since the method only needs to segment ridges, it does not need tedious manual labeling of vegetable plants in pixels to train plant segmentation net, yet provides accurate and reliable plant segmentation. Secondly, a modified Progressive Sample Consensus (PROSAC) algorithm and a distance filtering are proposed to fit line using the center points of plants, which effectively eliminates outliers and extract reliable and accurate center line of the current lane for autonomous navigation. Comprehensive experiments are carried out to validate the effectiveness of the method. The results show that the proposed method outperforms the conventional methods based on only vegetation index or ridge segmentation, by effectively reducing the interference caused by weeds, irregular branches and leaves, and missing rows. The proposed method runs at 10 Frames Per Second (FPS), thus satisfies real-time navigation of robots. Although the proposed method is only demonstrated in lettuce farms, it can be naturally applied to other vegetable farms, e.g. broccoli and early stage sugar beet farms.

针对柑橘园采收运输劳动力缺乏以及自主导航方式实现难度较大的问题,设计跟随导航的方式实现柑橘的自动化运输.基于激光雷达和视觉信息融合,采用HSV阈值分割图像,获取采摘引导机器人的方向,并在方向范围内聚类识别引导机器人标志物点云,获得运输跟随机器人与采摘引导机器人之间的相对位姿,并通过控制算法使运输机器人进行跟随完成自主导航.在模拟环境中对该系统进行测试,结果系统在最大0.5 m/s的速度下,直线跟随时平均纵向偏差1.5 cm,平均横向偏差1.0 cm,平均航向角偏差1.107°,弧线轨迹下持续跟随目标,跟随机器人在行间的停车试验也基本满足工作要求.系统为柑橘自主运输机械提供了技术支撑,为实际果园工作奠定基础.

The control of PH and EC in the nutrient solution of hydroponic plant factories is a key factor affecting the yield and quality of plants. In this paper, a cloud-based system for environmental factors in plant factories is studied for the need of real-time, remote, automatic and accurate monitoring and controlling of PH and EC values of nutrient solution in multi-species vegetable hydroponic plant factories. The system includes information acquisition module, data transmission module, remote monitoring module, local control and execution module, which can realize information monitoring and control of nutrient solution environmental data of multi-equipment and multi-species vegetables in plant factories. Real-time data monitoring and control of PH and EC values in nutrient solution can be carried out both remotely and on site. Meanwhile, the automatic control of PH and EC values in nutrient solution based on PID algorithm ensures that each environmental factor is in the target range suitable for plant growth. The test results show that the system is stable and reliable, and can realize intelligent detection and control of nutrient solution environment in multiple areas of plant factories.

DATA REPORT article Front. Plant Sci., 29 August 2023Sec. Sustainable and Intelligent Phytoprotection Volume 14 - 2023 | https://doi.org/10.3389/fpls.2023.1175743

IntroductionThe 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.MethodsIn 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.ResultsThe 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.DiscussionThe 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.

To achieve accurate readings of pointer instruments in complex scenes of substations and promote the development of substation intelligence, this paper proposes a pointer instrument reading method based on image enhancement and deep learning. Firstly, an improved CLAHE image algorithm is introduced to enhance image enhancement details, and then, based on the YOLOv8 network, the dial area and range digital area in the instrument image are detected, and the instrument types are classified. Secondly, a perspective transformation method based on range digital information is proposed to correct the dial image for the phenomenon of instrument tilt and rotation. Finally, the semantic segmentation technology is used to extract pointer pixels in the dial area, and the pointer deflection angle is calculated in polar coordinates to obtain the instrument reading. Finally, to verify the effectiveness of this method, experiments are conducted on instrument images in general scenes, scenes with varying exposures, and scenes with varying dial tilts. The experimental results show that the proposed method has an average citation error of 0.37% and an average recognition time of 0.17 seconds. It has good adaptability in complex environments, such as lighting effects and instrument tilting, and significantly improves the accuracy and practicality of pointer-type instrument reading recognition.