Computers and Electronics in Agriculture最新文献

{"title":"Transfer large models to crop pest recognition—A cross-modal unified framework for parameters efficient fine-tuning","authors":"Jianping Liu ,&nbsp;Jialu Xing ,&nbsp;Guomin Zhou ,&nbsp;Jian Wang ,&nbsp;Lulu Sun ,&nbsp;Xi Chen","doi":"10.1016/j.compag.2025.110661","DOIUrl":"10.1016/j.compag.2025.110661","url":null,"abstract":"<div><div>Crop pest recognition is an important direction in agricultural research, which is of great significance for improving crop yield and scientifically classifying pests for precision agriculture. Traditional deep learning pest recognition usually trains proprietary models on single categories and scenes as well as unimodal information, achieving excellent performance. However, this scheme has a weak foundation of general knowledge, insufficient transferability, and unimodal information has limited effect on the recognition of pest background and different life stages. In recent years, transferring the general knowledge of Large pre-trained models (LPTM) to specific domains through full fine-tuning has become an effective solution. However, full fine-tuning requires massive data and operator resources to effectively adapt all parameters. Therefore, this paper proposes a cross-modal parameter efficient fine-tuning (PEFT) unified framework for crop pest recognition with the multimodal large model CLIP as the pre-training model. The proposed method employs CLIP as the encoder for both image and text modalities, introducing the Dual-<span><math><msup><mrow><mrow><mo>(</mo><mtext>PAL</mtext><mo>)</mo></mrow></mrow><mrow><mtext>G</mtext></mrow></msup></math></span> model. Firstly, learnable Prompt sequences are embedded in the input or hidden layers of the encoder. Secondly, multimodal LoRA is parallelly replaced in the dimension expansion layer of the fully connected layer. Then, the Gate unit integrates three PEFT methods—Prompt, Adapter, and LoRA, to enhance learning ability. We designed the GSC-Adapter and the parameter-efficient Light-GCS-Adapter for cross-modal semantic information fusion. To verify the effectiveness of the method, we conducted a large number of experiments on public datasets for crop pest recognition. Firstly, on the public dataset IP102 (for fine-grained recognition), we surpassed ViT and Swin Transformer with 66% of the sample size. In wolfberry pest dataset WPIT9K, using only about 15% of the sample size, it surpasses the previous state-of-the-art model ITF-WPI, achieving 98% accuracy. It also shows excellent performance on eight general tasks. This study provides a new technical solution for the field of agricultural pest recognition . This solution can efficiently transfer the general knowledge of multimodal LPTM to the specific pest recognition field under the condition of a few samples, with only a minimal number of parameters introduced. At the same time, this method has universality in cross-modal recognition tasks. <em>The code for this study will be posted on GitHub (</em><span><span><em>https://github.com/VcRenOne/Dual--PAL-G</em></span><svg><path></path></svg></span><em>)</em></div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110661"},"PeriodicalIF":7.7,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491670","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"In-situ analysis of nitrogen stress in field-grown wheat: Raman spectroscopy as a non-destructive and rapid method","authors":"Zhen Gao ,&nbsp;Daming Dong ,&nbsp;Guiyan Yang ,&nbsp;Xuelin Wen ,&nbsp;Juekun Bai ,&nbsp;Fengjing Cao ,&nbsp;Chunjiang Zhao ,&nbsp;Xiande Zhao","doi":"10.1016/j.compag.2025.110700","DOIUrl":"10.1016/j.compag.2025.110700","url":null,"abstract":"<div><div>Nitrogen, as a vital element for plant growth and development, significantly influences crop yields. Nitrogen deficiency severely impairs crop growth, while excess nitrogen harms the environment. To address this, there is an urgent need for rapid and on-site methods to assess the physiological status of crops under nitrogen stress. In this study, we utilized Raman spectroscopy, a non-destructive and rapid analytical technique, to evaluate the physiological status of wheat plants subjected to various nitrogen treatments. These treatments included optimal, low, excessive and zero nitrogen application. By leveraging Raman spectroscopy’s ability to identify characteristic peaks of metabolites in plant leaves and quantify them based on peak intensity, we analyzed the levels of carotenoids, chlorophylls, cellulose, lignin, and aliphatic components. Our results revealed significant differences in metabolite peak intensity under different nitrogen treatments. Optimal nitrogen application promoted the accumulation of metabolites, while nitrogen deficiency led to a marked decrease in photosynthetic pigments and structural components. Excessive nitrogen caused a reduction in lignin and cellulose. To diagnose nitrogen stress, we developed classification models that accurately distinguished between healthy and nitrogen-stressed plants, achieving a training set accuracy of 99 %, a 5-fold cross-validation accuracy of 92 %, and a prediction set accuracy of 93 %. Furthermore, we differentiated wheat plants with varying degrees of nitrogen deficiency, achieving a maximum accuracy of 78 %. When considering both nitrogen deficiency and excess, the maximum accuracy reached 58 %. This study provides a fast, accurate, and non-destructive analytical method for analyzing and diagnosing nitrogen stress in field wheat based on Raman spectroscopy. Future research aims to extend this approach to the diagnosis of nitrogen stress in other crops and to explore its applications in nitrogen fertilization management.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110700"},"PeriodicalIF":7.7,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144481044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of spraying characteristics of a new multiple centrifugal nozzle applied to UAV","authors":"Shaoqing Xu ,&nbsp;Ye Jin ,&nbsp;Yuan Zhong ,&nbsp;Luna Luo ,&nbsp;Jianli Song","doi":"10.1016/j.compag.2025.110712","DOIUrl":"10.1016/j.compag.2025.110712","url":null,"abstract":"<div><div>Plant protection unmanned aerial vehicles (UAVs) have been widely used in fruit tree plant protection in recent years, especially in hilly and mountainous application scenarios. The ability of UAVs to meet the requirements of citrus red spider control is a current concern for UAV manufacturers, agricultural service organizations, and citrus growers. In this study, a UAV with a multiple centrifugal nozzle was tested. The nozzle has two atomizers, the inner atomizer (P) and the outer atomizer (C), which can achieve multiple droplet atomization. First, the droplet fragmentation characteristics and the droplet size of the nozzle were measured. A high-speed camera was used to study droplet fragmentation characteristics. The atomization process was divided into three stages: the first atomization triggered by the rotation of P, the second atomization caused by the impact of C, and the collisional agglomeration of small droplets around the nozzle. The result of the droplet size test showed that droplet size is inversely proportional to the rotational speed of P. The volume surface mean diameter (VMD) could reach a minimum of about 40 µm by adjusting the rotational speeds of atomizers P and C. In addition, an UAV(EA-30XP) equipped with this nozzle was used for field evaluations. The deposition under different atomizer rotational speed combinations was obtained. The result showed that a P atomizer rotational speed of 4600 rpm and a C rotational speed of 18000 rpm gave the highest deposition efficiency. Coverage on adaxial surface of the leaf in this combination was 3.6 %–9.3 %, with 102.7–184.3 droplets per square centimeter; coverage of the abaxial surface was 1.9 %–3.3 %, with 51.5–84.7 droplets per square centimeter. In addition, the advantages of multiple atomizing centrifugal nozzle in terms of deposition efficiency were also shown in a comparison with single atomizing centrifugal nozzle. The coverage and droplet density of abaxial surface by the single atomizing centrifugal nozzle were significantly lower than the above rotational speed combinations.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110712"},"PeriodicalIF":7.7,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of goat behaviour prediction with accelerometer data: a machine learning and pre-processing approach","authors":"Daniel Alexander Méndez ,&nbsp;Blanca Fajardo ,&nbsp;Sergi Sanjuan ,&nbsp;Jose Manuel Calabuig ,&nbsp;Roger Arnau ,&nbsp;Arantxa Villagrá ,&nbsp;Salvador Calvet-Sanz ,&nbsp;Fernando Estelles","doi":"10.1016/j.compag.2025.110701","DOIUrl":"10.1016/j.compag.2025.110701","url":null,"abstract":"<div><div>The increasing use of accelerometer data for monitoring livestock behaviour in Precision Livestock Farming (PLF) has prompted interest in optimizing machine learning models for real-time applications. This study evaluates the effects of pre-processing factors on predicting goat behaviours using accelerometer data collected in an intensive production environment. A triaxial accelerometer placed on goats’ necks recorded movement data, which was synchronized with video-based ethograms for behavioural annotation. Multiple pre-processing techniques, including filtering, windowing, overlapping and sampling frequency with several feature extraction parameters, were assessed to identify optimal combinations for behaviour classification. Various machine learning algorithms, including classification trees, logistic regression, and multilayer perceptron (MLP) models, were applied to predict <em>eating</em>, <em>walking</em>, and <em>inactive</em> behaviours. Results indicate that some of the pre-processing methods applied could induce inflated evaluation metrics and the importance of the selection of train and test sets. Tree-based classifiers and MLPs demonstrate robust performance, achieving average accuracies above 0.9. Battery performance demonstrate that MLP extends the battery life of the accelerometer device by ∼25 %. These findings highlight the potential of machine learning models in real-time behavioural monitoring to enhance livestock management with goats.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110701"},"PeriodicalIF":7.7,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491636","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GrapeCPNet: A self-supervised point cloud completion network for 3D phenotyping of grape bunches","authors":"Wenli Zhang ,&nbsp;Chao Zheng ,&nbsp;Chenhuizi Wang ,&nbsp;Pieter M. Blok ,&nbsp;Haozhou Wang ,&nbsp;Wei Guo","doi":"10.1016/j.compag.2025.110595","DOIUrl":"10.1016/j.compag.2025.110595","url":null,"abstract":"<div><div>The measurement of phenotypic parameters of fresh grapes, especially at the individual berry level, is critical for yield estimation and quality control. Currently, these measurements are done by humans, making it costly, labor-intensive, and often inaccurate. Advances in 3D reconstruction and point cloud analysis allow extraction of detailed traits for grapes, yet current methods struggle incomplete point clouds due to occlusion. This study presents a novel deep-learning-based phenotyping pipeline designed specifically for 3D point cloud data. First, individual berries are segmented from the grape bunch using the SoftGroup deep learning network. Next, a self-supervised point cloud completion network, termed GrapeCPNet, addresses occlusions by completing missing areas. Finally, morphological analyses are applied to extract berry radius and volumes. Validation on a dataset of four fresh grape varieties yielded <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> values of 85.5% for berry radius and 96.9% for berry volume, respectively. These results demonstrate the potential of the proposed method for rapid and practical extraction of 3D phenotypic traits in grape cultivation.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110595"},"PeriodicalIF":7.7,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144480877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UAV-based path planning for efficient localization of non-uniformly distributed weeds using prior knowledge: A reinforcement-learning approach","authors":"Rick van Essen,&nbsp;Eldert van Henten,&nbsp;Gert Kootstra","doi":"10.1016/j.compag.2025.110651","DOIUrl":"10.1016/j.compag.2025.110651","url":null,"abstract":"<div><div>UAVs are becoming popular in agriculture, however, they usually use time-consuming row-by-row flight paths. This paper presents a deep-reinforcement-learning-based approach for path planning to efficiently localize weeds in agricultural fields using UAVs with minimal flight-path length. The method combines prior knowledge about the field containing uncertain, low-resolution weed locations with in-flight weed detections. The search policy was learned using deep Q-learning. We trained the agent in simulation, allowing a thorough evaluation of the weed distribution, typical errors in the perception system, prior knowledge, and different stopping criteria on the planner’s performance. When weeds were non-uniformly distributed over the field, the agent found them faster than a row-by-row path, showing its capability to learn and exploit the weed distribution. Detection errors and prior knowledge quality had a minor effect on the performance, indicating that the learned search policy was robust to detection errors and did not need detailed prior knowledge. The agent also learned to terminate the search. To test the transferability of the learned policy to a real-world scenario, the planner was tested on real-world image data without further training, which showed a 66% shorter path compared to a row-by-row path at the cost of a 10% lower percentage of found weeds. Strengths and weaknesses of the planner for practical application are comprehensively discussed, and directions for further development are provided. Overall, it is concluded that the learned search policy can improve the efficiency of finding non-uniformly distributed weeds using a UAV and shows potential for use in agricultural practice.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110651"},"PeriodicalIF":7.7,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144480878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hyperspectral imaging VIS-NIR and SWIR fusion for improved drought-stress identification of strawberry plants","authors":"Mohammad Akbar Faqeerzada ,&nbsp;Hangi Kim ,&nbsp;Moon S. Kim ,&nbsp;Insuck Baek ,&nbsp;Diane E. Chan ,&nbsp;Byoung-Kwan Cho","doi":"10.1016/j.compag.2025.110702","DOIUrl":"10.1016/j.compag.2025.110702","url":null,"abstract":"<div><div>Hyperspectral imaging systems that operate in the visible-near infrared (VIS-NIR) and short-wave infrared (SWIR) spectral regions are increasingly recognized as practical and effective tools for enhancing crop management. However, hyperspectral systems can have some limitations when focusing on specific spectral ranges, particularly for spatial and spectral resolution. Image fusion techniques combining information from different sensors to enhance hyperspectral data can significantly improve spatial and spectral resolution. Fusion data of image and spectral data from the two HSI cameras (VIS-NIR<!--> <!-->and<!--> <!-->SWIR)<!--> <!-->provide complementary information on plant physiology, biochemistry, and morphology before visible plant stress symptoms. This study presents advancements in hyperspectral image fusion achieved by using two line-scan sensors, one for VIS-NIR (397–1003 nm) and the other for SWIR (894–2504 nm), to detect asymptomatic drought stress in strawberry plants. The images from both hyperspectral imaging systems were aligned based on feature and intensity, combined with various geometric transformations for fusion. The resulting fused hyperspectral cube contained 403 bands covering a broad spectrum from 397 to 2500 nm. Given the vulnerability of strawberry plants to drought, which can significantly affect growth and yield, this study aimed to explore the potential of hyperspectral image fusion for high-throughput detection of drought-stressed strawberry plants. The fused images improved the performance of the PLS-DA detection model, increasing classification accuracy by up to 10 %, achieving 99 % accuracy in the prediction set, and reducing error rates compared to independently generated models.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110702"},"PeriodicalIF":7.7,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491634","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generating crop type labels from historical annual crop inventory data with an ensemble learning method","authors":"Yue Wu ,&nbsp;Xin Chen ,&nbsp;Chunhua Liao ,&nbsp;Xuanhong Xu ,&nbsp;Yongjun He ,&nbsp;Jinfei Wang ,&nbsp;Tianxing Wang","doi":"10.1016/j.compag.2025.110670","DOIUrl":"10.1016/j.compag.2025.110670","url":null,"abstract":"<div><div>Timely and accurate pre-season crop maps are essential for agricultural applications, disaster management, and policy formulation. However, pre-season mapping methods face significant challenges due to the lack of ground truth labels. In this study, an ensemble learning model was proposed to generate crop type labels from the historical Annual Crop Inventory (ACI) data. By introducing the rotation stability rate (RSR) metric to assess the stability of different crop planting sequences and adaptively setting appropriate probability thresholds for different crops, this method significantly improves the model’s prediction accuracy. In comparison to the “trusted pixels” approach, this method exhibited better accuracies for soybean, winter wheat, and pasture. Tested by ground truth data acquired in 2020 and 2021, the overall accuracies were both greater than 90 %. Tested by ACI data at another three regions located in Quebec, Ontario, and Manitoba, the overall accuracies are between 73 % and 86 %. The results demonstrated that the combination of RSR and ensemble learning model enhanced the ability to extract crop type label pixels using historical annual crop inventory data in regions with diverse cropping systems.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110670"},"PeriodicalIF":7.7,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144480876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The application of UAV technology in maize crop protection strategies: A review","authors":"Yongwei Yan,&nbsp;Fa Song,&nbsp;Jiyu Sun","doi":"10.1016/j.compag.2025.110679","DOIUrl":"10.1016/j.compag.2025.110679","url":null,"abstract":"<div><div>Unmanned aerial vehicles (UAVs) play an important role in precision agriculture (PA). UAVs can achieve efficient plant protection operations by carrying advanced sensors for real-time monitoring and pesticide spraying of maize crops. This paper aims to summarize recent research progress on UAVs in corn crop protection across various agricultural processes, to identify potential applications of biomimetic technology and to propose ideas for the future application of UAVs in corn crop protection. First, protection strategies for maize crops at different growth stages are explored. Agricultural applications of UAVs in maize crop protection are subsequently investigated, demonstrating their significant advantages. UAVs can facilitate efficient crop monitoring, including nutrient assessment, health assessment, disease detection, weed control, and yield estimation, providing accurate agricultural management information. Moreover, UAVs have performed well in terms of crop spraying, thereby achieving precise pest control, fertilization, and pollination, and increasing operational efficiency and crop yield. However, seeding and pollination should be investigated further. In addition, the application of biomimetic technology in crop protection UAVs is described, including drone designs, applications of biomimetic sensors and artificial intelligence technology, which can increase the monitoring accuracy, operational accuracy, and intelligence level of UAVs. Finally, future development trends in crop protection UAVs are outlined, and associated challenges are analysed.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110679"},"PeriodicalIF":7.7,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144480879","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decoupled motion planning method for 7-DOF manipulator and lifting joint in automated tomato harvesting","authors":"Jiacheng Rong ,&nbsp;Yu Liu ,&nbsp;Xianjun Li ,&nbsp;Chao Gao ,&nbsp;Pengbo Wang ,&nbsp;Ting Yuan ,&nbsp;Wei Li","doi":"10.1016/j.compag.2025.110693","DOIUrl":"10.1016/j.compag.2025.110693","url":null,"abstract":"<div><div>Robotic harvesting tasks must contend with complex operation environments, where varying grasping poses, fruit heights, and obstacle distributions impose higher demands on the robot’s effective working space and dexterity. A fixed robotic arm base placement for the robotic arm can result in targets being outside the arm’s reachable space or unreachable with the given approach pose, which reduces the robot’s harvesting success rate. In this work, we propose a step-by-step path planning method that decouples the robotic arm and the lift joint. This method is based on the reachability map of the robotic arm and takes into account the arm’s manipulation costs (including similarity cost and gravity cost) as well as the lift joint’s movement cost. After performing a fast non-dominated sorting of the costs for the discrete point candidates, it generates sorted lift height candidates. Finally, improved RRT* for the robotic arm is executed at the optimal position of the lift joint. Field test results conducted on 36 samples validate the effectiveness of the proposed method. In observation tasks, the system achieved a success rate of 35 out of 36 (97.2 %), with a lift joint planning time of 3.09 ms. For grasping tasks, the decoupled approach achieved an overall success rate of 83.3 % (30/36) with a lift joint planning time of 5.95 ms, significantly outperforming the fixed-set method (36.1 %, 13/36) and Reuleaux method (77.8 %, 28/36). This indicates that the method can significantly improve the success rate of tomato harvesting by robots in vertical farms, with an acceptable time cost. The findings establish a robust foundation for improving robotic harvesting systems by addressing the limitations of conventional approaches. The proposed method not only achieves high success rates and motion efficiency but also provides a scalable and practical solution for modern agricultural robotics.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"237 ","pages":"Article 110693"},"PeriodicalIF":7.7,"publicationDate":"2025-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144491669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}