Search Results (17)

Seedling transplantation, a pivotal component in advancing the cultivation of vegetables and cash crops, significantly bolsters crops’ resilience against drought, cold, pests, and diseases, while substantially enhancing yields. The implementation of transplanting machinery not only remarkably alleviates the labor-intensive nature of transplantation but also elevates the precision and uniformity of the process, thereby facilitating mechanized plant protection and harvesting operations. This article summarizes the research status and development trends of mechanized field transplanting technology and equipment. It also analyzes and summarizes the types and current status of typical representative automatic seedling picking mechanisms. Based on the current research status, the challenges of mechanized transplanting technology were analyzed, mainly the following: insufficient integration of agricultural machinery and agronomy; the standards for each stage of transplanting are not perfect; the adaptability of existing transplanting machines is poor; the level of informatization and intelligence of equipment is low; the lack of innovation in key components, such as seedling picking and transplanting mechanisms; and the proposed solutions to address the issues. Corresponding solutions are proposed, such as the following: strengthening interdisciplinary collaboration; establishing standards for transplanting processes; enhancing transplanter adaptability; accelerating intelligentization and digitalization of transplanters; strengthening the theoretical framework; and performance optimization of transplanting mechanisms. Finally, the development direction of future fully automatic transplanting machines was discussed, including the following: improving the transplanting efficiency and quality of transplanting machines; integrating research and development of testing, planting, and seedling supplementation for transplanting machines; unmanned transplanting operations; and fostering collaborative industrial development. Full article

At present, there is a lack of effective and usable machinery in the harvesting of aquatic vegetables. The harvesting of most aquatic vegetables such as Brasenia schreberi relies entirely on manual labor, resulting in a high labor demand and labor shortages, which restricts the industrial development of aquatic vegetables. To address this problem, an intelligent harvesting system for the aquatic vegetable Brasenia schreberi was developed in response to the challenging working conditions associated with harvesting it. The system is composed of a catamaran mobile platform, a picking device, and a harvesting manipulator control system. The mobile platform, driven by two paddle wheels, is equipped with a protective device to prevent vegetable stem entanglement, making it suitable for shallow pond aquatic vegetable environments. The self-designed picking device rapidly harvests vegetables through lateral clamping and cutting. The harvesting manipulator control system incorporates harvesting posture perception based on the YOLO-GS recognition algorithm and combines it with an improved RRT algorithm for robotic arm path planning. The experimental results indicate that the intelligent harvesting system is suitable for aquatic vegetable harvesting and the improved RRT algorithm surpasses the traditional one in terms of the planning time and path length. The vision-based positioning error was 4.80 mm, meeting harvesting accuracy requirements. In actual harvest experiments, the system showed an average success rate of 90.0%, with an average picking time of 5.229 s per leaf, thus proving its feasibility and effectiveness. Full article

Chili pepper (Capsicum annuum L.), a globally important economic crop, faces production challenges characterized by high labor intensity, cost, and inefficiency. Intelligent technologies offer key opportunities for sector transformation. This review begins by outlining the diversity of major chili pepper cultivars, differences in key quality indicators, and the resulting specific harvesting needs. It then reviews recent progress in intelligent perception, recognition, and automation within the chili pepper industry. For perception and recognition, the review covers the evolution from traditional image processing to deep learning-based methods (e.g., YOLO and Mask R-CNN achieving a mAP > 90% in specific studies) for pepper detection, segmentation, and fine-grained cultivar identification, analyzing the performance and optimization in complex environments. In terms of automation, we systematically discuss the principles and feasibility of different mechanized harvesting machines, consider the potential of vision-based keypoint detection for the point localization of picking, and explore motion planning and control for harvesting robots (e.g., robotic systems incorporating diverse end-effectors like soft grippers or cutting mechanisms and motion planning algorithms such as RRT) as well as seed cleaning/separation techniques and simulations (e.g., CFD and DEM) for equipment optimization. The main current research challenges are listed including the environmental adaptability/robustness, efficiency/real-time performance, multi-cultivar adaptability/flexibility, system integration, and cost-effectiveness. Finally, future directions are given (e.g., multimodal sensor fusion, lightweight models, and edge computing applications) in the hope of guiding the intelligent growth of the chili pepper industry. Full article

The intelligent detection of chili peppers is crucial for achieving automated operations. In complex field environments, challenges such as overlapping plants, branch occlusions, and uneven lighting make detection difficult. This study conducted comparative experiments to select the optimal detection model based on YOLOv8 and further enhanced it. The model was optimized by incorporating BiFPN, LSKNet, and FasterNet modules, followed by the addition of attention and lightweight modules such as EMBC, EMSCP, DAttention, MSBlock, and Faster. Adjustments to CIoU, Inner CIoU, Inner GIoU, and inner_mpdiou loss functions and scaling factors further improved overall performance. After optimization, the YOLOv8 model achieved precision, recall, and mAP scores of 79.0%, 75.3%, and 83.2%, respectively, representing increases of 1.1, 4.3, and 1.6 percentage points over the base model. Additionally, GFLOPs were reduced by 13.6%, the model size decreased to 66.7% of the base model, and the FPS reached 301.4. This resulted in accurate and rapid detection of chili peppers in complex field environments, providing data support and experimental references for the development of intelligent picking equipment. Full article

Pineapple harvesting is a key step in pineapple field production. At present, pineapple fruits are usually picked manually. With decreasing labor resources and increasing production costs, machines have been used instead of manual picking approaches in the modern pineapple industry. This paper briefly describes the basic situation of pineapple planting worldwide. Based on the degree of automation of mechanized pineapple harvesting equipment, the main structural forms, core technologies, and operation modes of semi-automatic, automatic, and intelligent pineapple harvesting equipment are summarized. The research status and existing problems of key pineapple fruit picking robots, such as fruit recognition, maturity classification, positioning, and separation of pineapple fruits, are analyzed. Considering the problems of pineapple harvesting equipment, such as difficulty entering the ground, low harvesting efficiency, low picking success rate, and fruit damage, innovative future research directions for mechanized pineapple harvesting technology are proposed, such as combining agricultural machinery and agronomical principles, integrating mechanized, automated, and intelligent technology, and developing modular designs and generalized approaches. Full article

Agaricus bisporus is widely cultivated worldwide due to its considerable economic benefits. The increasingly prominent contradiction between production and labor shortage necessitates the urgent replacement of human workers with intelligent picking technology and equipment. Therefore, a pressure-stabilized flexible end-effector was designed to achieve the rapid and low-loss picking of Agaricus bisporus. The dimensions of the end-effector were determined by measuring the external parameters of Agaricus bisporus. A mechanics model of the end-effector was constructed to analyze the picking process theoretically, and the pre-experiments identified the key performance factors of the end-effector: the thickness of the flexible membrane, the particle filler material, and the pressure stability. A series of experiments were conducted to investigate the factors mentioned above concerning the performance of the end-effector. The results show that the adsorption effect is best when the thickness of the flexible membrane is 0.9 mm, and the particle diameter is 200 mesh quartz. To control the adsorption force of the end-effector accurately during the picking process, a low-cost adsorption force-adjustment system was designed, and the stability of the system was verified. The experimental results showed that the device improved the stability of the adsorption force during the operation of the picking system by 84.71%. An experiment was conducted on the picking of Agaricus bisporus using the designed end-effector. The success rate of picking with the end-effector was 98.50%, and the picking damage rate was 2.50%. Full article

Future challenges in manufacturing will require automation systems with robots that are increasingly autonomous, flexible, and hopefully equipped with learning capabilities. The flexibility of production processes can be increased by using a combination of a flexible human worker and intelligent automation systems. The adoption of middleware software such as ROS2, the second generation of the Robot Operating System, can enable robots, automation systems, and humans to work together on tasks that require greater autonomy and flexibility. This paper has a twofold objective. Firstly, it provides an extensive review of existing literature on the features and tools currently provided by ROS2 and its main fields of application, in order to highlight the enabling aspects for the implementation of modular architectures to increase autonomy in industrial operations. Secondly, it shows how this is currently potentially feasible in ROS2 by proposing a possible high-level and modular architecture to increase autonomy in industrial operations. A proof of concept is also provided, where the ROS2-based framework is used to enable a cobot equipped with an external depth camera to perform a flexible pick-and-place task. Full article

Photovoltaic installations can be environmentally beneficial to a greater or lesser extent, depending on the conditions. If the energy produced is not used, it is redirected to the grid, otherwise a battery with a high ecological footprint is needed to store it. To alleviate this problem, an innovative recommender system is proposed for residents of smart homes equipped with battery-free solar panels to optimise the energy produced. Using artificial intelligence, the system is designed to predict the energy produced and consumed for the day ahead using three data sources: sensor logs from the home automation solution, data collected by the solar inverter, and weather data. Based on these predictions, recommendations are then generated and ranked by relevance. Data collected over 76 days were used to train two variants of the system, considering or without considering energy consumption. Recommendations selected by the system over 14 days were randomly picked to be evaluated for relevance, ranking, and diversity by 11 people. The results show that it is difficult to predict residents’ consumption based solely on sensor logs. On average, respondents reported that 74% of the recommendations were relevant, while the values contained in them (i.e., accuracy of times of day and kW energy) were accurate in 66% (variant 1) and 77% of cases (variant 2). Also, the ranking of the recommendations was considered logical in 91% and 88% of cases. Overall, residents of such solar-powered smart homes might be willing to use such a system to optimise the energy produced. However, further research should be conducted to improve the accuracy of the values contained in the recommendations. Full article

After the residual film recycling machine recovers the film, some small pieces of the film will remain on the surface of the field. To solve the problem of collecting small pieces of film, it is necessary to develop a piece of intelligent picking equipment. The detection of small pieces of film is the first problem to be solved. This study proposes a method of an object detection algorithm fusing multi-scale features (MFFM Faster R-CNN) based on improved Faster R-CNN. Based on the Faster R-CNN model, the feature pyramid network is added to solve the problem of multiscale change of residual film. The convolution block attention module is introduced to enhance the feature extraction ability of the model. The Soft-NMS algorithm is used instead of the NMS algorithm to improve the detection accuracy of the model in the RPN network. The experimental results show that the model is able to effectively detect surface residual film in complex environments, with an AP of 83.45%, F_1-score of 0.89, and average detection time of 248.36 ms. The model is compared with SSD and YOLOv5 under the same experimental environment and parameters, and it is found that the model not only ensures high-precision detection but also ensures real-time detection. This lays the theoretical foundation for the subsequent development of field surface residual film intelligent picking equipment. Full article

Accurate and rapid detection of tea shoots within the tea canopy is essential for achieving the automatic picking of famous tea. The current detection models suffer from two main issues: low inference speed and difficulty in deployment on movable platforms, which constrain the development of intelligent tea picking equipment. Furthermore, the detection of tea canopy shoots is currently limited to natural daylight conditions, with no reported studies on detecting tea shoots under artificial light during the nighttime. Developing an all-day tea picking platform would significantly improve the efficiency of tea picking. In view of these problems, the research objective was to propose an all-day lightweight detection model for tea canopy shoots (TS-YOLO) based on YOLOv4. Firstly, image datasets of tea canopy shoots sample were collected under low light (6:30–7:30 and 18:30–19:30), medium light (8:00–9:00 and 17:00–18:00), high light (11:00–15:00), and artificial light at night. Then, the feature extraction network of YOLOv4 and the standard convolution of the entire network were replaced with the lightweight neural network MobilenetV3 and the depth-wise separable convolution. Finally, to compensate for the lack of feature extraction ability in the lightweight neural network, a deformable convolutional layer and coordinate attention modules were added to the network. The results showed that the improved model size was 11.78 M, 18.30% of that of YOLOv4, and the detection speed was improved by 11.68 FPS. The detection accuracy, recall, and AP of tea canopy shoots under different light conditions were 85.35%, 78.42%, and 82.12%, respectively, which were 1.08%, 12.52%, and 8.20% higher than MobileNetV3-YOLOv4, respectively. The developed lightweight model could effectively and rapidly detect tea canopy shoots under all-day light conditions, which provides the potential to develop an all-day intelligent tea picking platform. Full article

To create a digital unmanned orchard with automation of “picking, load and transportation” in the hills and mountains, it is vital to determine a cargo-carrying situation and monitor the real-time transport conditions. In this paper, a cargo-carrying analysis system based on RGB-D data was developed, taking citrus transportation as the scenario. First, the improved YOLOv7-tiny object detection algorithm was used to classify and obtain 2D coordinate information on the carried cargo, and a region of interest (ROI) was obtained from the coordinate information for cargo height measurement. Second, 3D information was driven by 2D detection results using fewer computing resources. A depth map was used to calculate the height values in the ROI using a height measurement model based on spatial geometry, which obtained the load volume of the carried cargo. The experimental results showed that the improved YOLOv7 model had an accuracy of 89.8% and an average detection time of 63 ms for a single frame on the edge-computing device. Within a horizontal distance of 1.8 m from the depth camera, the error of the height measurement model was ±3 cm, and the total inference time of the overall method was 75 ms. The system lays a technical foundation for generating efficient operation paths and intelligently scheduling transport equipment, which promote the intelligent and sustainable development of mountainous agriculture. Full article

Intelligent detection is vital for achieving the intelligent picking operation of daylily, but complex field environments pose challenges due to branch occlusion, overlapping plants, and uneven lighting. To address these challenges, this study selected an intelligent detection model based on YOLOv5s for daylily, the depth and width parameters of the YOLOv5s network were optimized, with Ghost, Transformer, and MobileNetv3 lightweight networks used to optimize the CSPDarknet backbone network of YOLOv5s, continuously improving the model’s performance. The experimental results show that the original YOLOv5s model increased mean average precision (mAP) by 49%, 44%, and 24.9% compared to YOLOv4, SSD, and Faster R-CNN models, optimizing the depth and width parameters of the network increased the mAP of the original YOLOv5s model by 7.7%, and the YOLOv5s model with Transformer as the backbone network increased the mAP by 0.2% and the inference speed by 69% compared to the model after network parameter optimization. The optimized YOLOv5s model provided precision, recall rate, mAP, and inference speed of 81.4%, 74.4%, 78.1%, and 93 frames per second (FPS), which can achieve accurate and fast detection of daylily in complex field environments. The research results can provide data and experimental references for developing intelligent picking equipment for daylily. Full article

Due to their rapid development and wide application in modern agriculture, robots, mobile terminals, and intelligent devices have become vital technologies and fundamental research topics for the development of intelligent and precision agriculture. Accurate and efficient target detection technology is required for mobile inspection terminals, picking robots, and intelligent sorting equipment in tomato production and management in plant factories. However, due to the limitations of computer power, storage capacity, and the complexity of the plant factory (PF) environment, the precision of small-target detection for tomatoes in real-world applications is inadequate. Therefore, we propose an improved Small MobileNet YOLOv5 (SM-YOLOv5) detection algorithm and model based on YOLOv5 for target detection by tomato-picking robots in plant factories. Firstly, MobileNetV3-Large was used as the backbone network to make the model structure lightweight and improve its running performance. Secondly, a small-target detection layer was added to improve the accuracy of small-target detection for tomatoes. The constructed PF tomato dataset was used for training. Compared with the YOLOv5 baseline model, the mAP of the improved SM-YOLOv5 model was increased by 1.4%, reaching 98.8%. The model size was only 6.33 MB, which was 42.48% that of YOLOv5, and it required only 7.6 GFLOPs, which was half that required by YOLOv5. The experiment showed that the improved SM-YOLOv5 model had a precision of 97.8% and a recall rate of 96.7%. The model is lightweight and has excellent detection performance, and so it can meet the real-time detection requirements of tomato-picking robots in plant factories. Full article

A cantilever roadheader is the main tunneling equipment for underground coal mine roadways. The key to the safe, efficient and intelligent development of coal enterprises is to achieve the autonomous cutting and intelligent control of the cantilever roadheader. In order to realize the automatic cutting shaping control of a large-section coal roadway, the path planning and control method of secondary automatic cutting of a cantilever roadheader were studied. The Wangjialing 12307 belt roadway was used as the engineering background, the vertical displacement law of the roadway roof under different cutting paths was simulated with the FLAC 3D software, the reasonable cutting path was determined according to the actual situation, and the underground industrial test was carried out. The simplified model and spatial position and attitude coordinate system of the roadheader were established, the kinematics of the roadheader was analyzed, and the position and attitude expression of the cutting head center in the roadway coordinate system was obtained. The simplified model of the cutting head was established, the position expression of the pick in the roadway coordinate system was derived, the position coordinate of the inflection point and the cutting step distance were determined according to the relationship between the cutting head and the roadway boundary, and the cutting path control flow was designed. Finally, the reliability of the cutting path control method was verified with a MATLAB simulation. The research works provide a theoretical foundation for path planning and control to realize “secondary autonomous cutting of cantilever roadheader”. Full article

In this paper, a four-degree-of-freedom camellia-pollen-picking manipulator is proposed and designed. It can solve the problem of having no mechanized equipment for picking camellia pollen in agricultural machinery as the labor intensity of manual pollen extraction is high. To make the manipulator reach the target space quickly and efficiently, a structural-parameter-optimization method that reduces the working space to a more versatile cube is proposed. The numerical optimization algorithm is used to calculate the optimization result. Through the static analysis of the manipulator, the stability of the manipulator structure is verified. The working space of the manipulator is simulated and analyzed, and the simulation results are further verified by experiments. This research provides reliable technical support for the structural optimization, manufacturing, and intelligent upgrading of the camellia-pollen-picking robot. Full article