Computers and Electronics in Agriculture最新文献

{"title":"Counting wheat heads using a simulation model","authors":"Xiaoyong Sun ,&nbsp;Tianyou Jiang ,&nbsp;Jiming Hu ,&nbsp;Zuojie Song ,&nbsp;Yuheng Ge ,&nbsp;Yongzhen Wang ,&nbsp;Xu Liu ,&nbsp;Jianhao Bing ,&nbsp;Jinshan Li ,&nbsp;Ziyu Zhou ,&nbsp;Zhongzhen Tang ,&nbsp;Yan Zhao ,&nbsp;Jinyu Hao ,&nbsp;Changzhen Zuo ,&nbsp;Xia Geng ,&nbsp;Lingrang Kong","doi":"10.1016/j.compag.2024.109633","DOIUrl":"10.1016/j.compag.2024.109633","url":null,"abstract":"<div><div>Numerous studies have reported a significant positive correlation between wheat yield and the quantity of wheat heads. However, collecting data on wheat heads in the field poses a challenge for several reasons, including the uncontrollable nature of the environment, inconsistent data quality, and ambiguous data truth. To address these challenges, we developed a simulation strategy to replicate the conditions of a real wheat field, which enabled the data collection process to be conducted indoors over a short period. After applying grayscale image processing to process the simulated wheat images, we trained and tested nine deep learning models: Faster-RCNN, YOLOv7, YOLOv8, CenterNet, SSD, RetinaNet, EfficientDet, Deformable-DETR and DINO. Our results indicated that YOLOv7 performed the best (R² = 0.963, RMSE = 2.463). We then compared our model trained on simulated wheat data to a model trained on real wheat data (R² = 0.963 vs 0.972, RMSE = 2.463 vs 2.692). We also achieved good model performance on five test sets: GWHD, SDAU2021-SDAU2024. The results demonstrated the efficacy of our simulation, which provides an efficient and convenient strategy for the precision agriculture community.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"228 ","pages":"Article 109633"},"PeriodicalIF":7.7,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142759067","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 plant phenotyping system using Pan Tilt Zoom camera and verification of its validity","authors":"Dong Thanh Pham ,&nbsp;Nayeen AI Amin ,&nbsp;Daisuke Yasutake ,&nbsp;Yasumaru Hirai ,&nbsp;Takenori Ozaki ,&nbsp;Masaharu Koga ,&nbsp;Kota Hidaka ,&nbsp;Masaharu Kitano ,&nbsp;Hien Bich Vo ,&nbsp;Takashi Okayasu","doi":"10.1016/j.compag.2024.109579","DOIUrl":"10.1016/j.compag.2024.109579","url":null,"abstract":"<div><div>Quantitative analysis for plant growth attributes has gained prominence in plant science and agriculture. Despite the availability of automated phenotyping systems as a solution to labor-intensive manual measurement techniques, these systems often require specialized knowledge and face challenges in scaling for high-throughput applications. This research introduces a scalable high-throughput plant phenotyping technique utilizing a Pan Tilt Zoom (PTZ) camera. The primary objective is to assess the application of a PTZ camera in a plant phenotyping system. By integrating open-source software and hardware technologies, the method captures images of cucumber plants in a controlled greenhouse environment. The operational procedure of the robot consists of a series of steps. It begins with the robot’s initial movement to capture infrared images, followed by an analysis to detect Aruco markers serving as location identifiers for capturing plant images. Subsequently, the PTZ camera is adjusted to capture specific plant traits from predefined viewpoints. The captured images with location IDs, preset viewpoints, and timestamps are then sent to a remote server. Validation of the system’s dependability includes manual measurements on fundamental operations and the evaluation of the effectiveness of zoomed images captured by the PTZ camera, tested through plant feature detection. Experimental results demonstrate promising outcomes, achieving a mean average precision (mAP) of 94%, 97.6%, 98.4%, 90.1%, and 97.6% for apical buds, male flowers, female flowers, tiny cucumbers, and mature cucumbers respectively when using the trained YOLOv8s on an augmented dataset tested on highly zoomed images validation set, outperforming less zoomed or less detailed validation sets. These findings underscore the efficacy of this innovative approach in capturing real-time plant images. Leveraging the PTZ camera’s zoom, pan, and tilt capabilities enables comprehensive visualization of plant traits and adaptability to evolving growth patterns, thereby improving the results of plant feature detection. The amassed imagery serves a dual purpose by acting as training data for AI models, highlighting their potential to facilitate future research endeavors demanding extensive and scalable plant information.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109579"},"PeriodicalIF":7.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743486","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization and testing of a mechanical roller seeder based on DEM-MBD rice potting tray","authors":"Xin Jin ,&nbsp;Zhuo Chen ,&nbsp;Lijun Zhao ,&nbsp;Bo Zhao ,&nbsp;Mingyong Li ,&nbsp;Linyong Zhou ,&nbsp;Jiangtao Ji","doi":"10.1016/j.compag.2024.109670","DOIUrl":"10.1016/j.compag.2024.109670","url":null,"abstract":"<div><div>Seeder is the key component of rice seeding equipment, and its seed filling effect will directly affect the quality of seeding. This paper optimized the size parameters of the seed filling port based on DEM-MBD simulation technology. In this study, Y Liangyou 1998, Shen Liangyou 5814, and Yi Xiangyou 1108 were used as material subjects, analyzed the physical and mechanical characteristics, constructed the simulation model of rice seeds with a 3D laser scanner, and completed coupling simulation tests. The simulation results were analyzed using Design-Expert data analysis software to establish the optimization regression equation of the seed filling port and explore the optimal solution of the parameters of the seed filling port. The optimized filling port chamfer angle adapted to Y Liangyou 1998 was 45.84°, the fillet radius was 7.05 mm, the filling port length was 13.38 mm, and the predicted filling rate was 98.25 %. The optimized filling port chamfer angle adapted to Shen Liangyou 5814 was 46.10°, the fillet radius was 7.12 mm, the filling port length was 14.38 mm, and the predicted filling rate was 98.28 %. The optimized filling port chamfer angle adapted to Yi Xiangyou 1108 was 46.38°, the fillet radius was 7.06 mm, the filling port length was 15.41 mm, and the predicted filling rate was 98.52 %. Finally, specialized seeding trials and generalized seeding trials were showed that when seeding with the corresponding seeds of seeding rollers respectively, the seeding uniformity was kept above 66 %, the seeding qualification rate was kept above 96 %, and the seeding missed rate was below 1.3 %. When seeding with seeding rollers of maximum size seed-optimized, the seeding uniformity was kept above 65 %, the seeding qualification rate was kept above 95 %, and the seeding missed rate was below 1.3 %, where the maximum value of the seeding uniformity was 67.07 %, the minimum value of the seeding missed rate was 0.63 %, and the maximum value of the seeding qualification rate was 97.79 %. These results indicate that the rice potting tray mechanical roller seeder optimized based on DEM-MBD meets the requirements of precision seeding and can provide a reference for improving the seeding quality of high-speed rice seeding assembly lines.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"227 ","pages":"Article 109670"},"PeriodicalIF":7.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142743485","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":"Pushing the boundaries of aphid detection: An investigation into mmWaveRadar and machine learning synergy","authors":"Yuan Liqiang ,&nbsp;Fan Haozheng ,&nbsp;Xie Jing ,&nbsp;Chang Shiran ,&nbsp;Amit Kumar Das ,&nbsp;Derrick Nguyen Hoang Danh ,&nbsp;Khoo Eng Huat ,&nbsp;Joe Jimeno ,&nbsp;Arokiaswami Alphones ,&nbsp;Mohammed Yakoob Siyal ,&nbsp;Muhammad Faeyz Karim","doi":"10.1016/j.compag.2024.109655","DOIUrl":"10.1016/j.compag.2024.109655","url":null,"abstract":"<div><div>Agriculture, essential for global sustenance and economic vitality, faces significant threats from pest-induced damages, resulting in substantial crop losses and affecting food supply if not detected on time. Traditional pest control methods, primarily reliant on pesticides. However, blindly applying pesticide may cause environmental issue. Therefore detecting the infested crops at early stage is crucial for application of sustainable pest management solutions. This study innovatively employs the IWR1443BOOST FMCW Millimeter Wave Radar (mmWaveRadar) in conjunction with machine learning algorithms such as SVM, Random Forest, Adaboost, Lightgbm, Catboost, and edRVFL for enhanced pest detection in crops. Our novel framework encompasses the collection and pre-processing of mmWaveRadar data from both healthy and infested crops, followed by comprehensive feature extraction. Decision tree-based methods exhibited a remarkable detection accuracy of 98%. EdRVFL demonstrated a 95% detection accuracy. SVM, post-feature selection, achieved a 90% accuracy. The research reveals the efficacy of the mmWaveRadar as a robust tool, overcoming the environmental and concealment limitations of conventional image-based pest detection methods. The integration of curated features with machine learning algorithms has shown promising empirical results, establishing a connection between the discerned features and the real-world attributes of healthy and infested crops. This study underscores the potential of mmWaveRadar, coupled with specific machine learning algorithms, as a significant stride towards sustainable and effective pest management strategies in agricultural technology.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"229 ","pages":"Article 109655"},"PeriodicalIF":7.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142756999","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":"An IoT-based data analysis system: A case study on tomato cultivation under different irrigation regimes","authors":"Martina Galaverni ,&nbsp;Giulia Oddi ,&nbsp;Luca Preite ,&nbsp;Laura Belli ,&nbsp;Luca Davoli ,&nbsp;Ilaria Marchioni ,&nbsp;Margherita Rodolfi ,&nbsp;Federico Solari ,&nbsp;Deborah Beghè ,&nbsp;Tommaso Ganino ,&nbsp;Giuseppe Vignali ,&nbsp;Gianluigi Ferrari","doi":"10.1016/j.compag.2024.109660","DOIUrl":"10.1016/j.compag.2024.109660","url":null,"abstract":"<div><div>The exploitation of modern technologies in heterogeneous farming scenarios with different crops cultivation is nowadays an effective solution to implement the concept of Smart Agriculture (SA). Following this approach, in this study the tomato plants’ response to different irrigation regimes is investigated through the implementation of an Internet of Things (IoT)-oriented SA data collection and monitoring system. In particular, the experimentation is conducted on tomatoes grown at three different irrigation regimes: namely, at 100%, 60%, and 30% of the Italian irrigation recommendation service, denoted as Irriframe. The proposed platform, denoted as <em>Agriware</em>, is able to: (i) evaluate information from heterogeneous data sources, (ii) calculate agronomic indicators (e.g., Growing Degree Days, GDD), and (iii) monitor <em>on-field</em> parameters (e.g., water consumption). Different plant-related parameters have been collected to assess the response to water stress (e.g., Soil Plant Analysis Development (SPAD), chlorophyll content, fluorescence, and others), along with leaf color and final production evaluations. The obtained results show that the best irrigation regime, in terms of plant health and productivity, corresponds to 60% of Irriframe, allowing significant water savings for the cultivation.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"229 ","pages":"Article 109660"},"PeriodicalIF":7.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142756903","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"When synthetic plants get sick: Disease graded image datasets by novel regression-conditional diffusion models","authors":"Itziar Egusquiza ,&nbsp;Leire Benito-Del-Valle ,&nbsp;Artzai Picón ,&nbsp;Arantza Bereciartua-Pérez ,&nbsp;Laura Gómez-Zamanillo ,&nbsp;Andoni Elola ,&nbsp;Elisabete Aramendi ,&nbsp;Rocío Espejo ,&nbsp;Till Eggers ,&nbsp;Christian Klukas ,&nbsp;Ramón Navarra-Mestre","doi":"10.1016/j.compag.2024.109690","DOIUrl":"10.1016/j.compag.2024.109690","url":null,"abstract":"<div><div>This paper introduces DiffusionPix2Pix, an innovative extension of diffusion models (DMs) that revolutionizes synthetic image generation by seamlessly integrating image priors, surpassing existing state-of-the-art models. Key contributions include regression (graded) conditioning and an arbitrary binary mask, enabling regression-conditional image-to-image translation. DiffusionPix2Pix is compared with Pix2Pix-G and Pix2Pix-GD, two alternative models that rely on image-conditioned GANs adapted for an additional regression conditional task. The model is applied to generate a graded plant disease dataset focusing on <em>Puccinia striiformis</em> symptoms, using disease degree as an additional conditioning input to control the level of disease in generated images. Experiments demonstrate that DiffusionPix2Pix outperforms GAN-based approaches in both sample fidelity and diversity, achieving an Improved Precision (fidelity) of 0.81 (versus 0.45 and 0.47) and an Improved Recall (diversity) of 0.58 (versus 0.31 and 0.31). Furthermore, DiffusionPix2Pix obtained the best Fréchet Inception Distance (FID), with a score of 31.61 compared to 57.38 and 54.34 for GAN-based models. Additionally, perception-based tests with field technicians showed 71.3% of images generated by DiffusionPix2Pix were classified as authentic, significantly outperforming the 20.19% and 22.22% rates for GAN-based models. These findings substantiate the performance of the proposed DiffusionPix2Pix model, both quantitatively and through subjective assessments by domain experts, highlighting its potential in applications requiring precise regression conditioning.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"229 ","pages":"Article 109690"},"PeriodicalIF":7.7,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142757000","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":"Corrigendum to “A chlorophyll-constrained semi-empirical model for estimating leaf area index using a red-edge vegetation index” [Comput. Electron. Agric. 220 (2024) 108891]","authors":"Dong Li ,&nbsp;Jing M. Chen ,&nbsp;Weiguo Yu ,&nbsp;Hengbiao Zheng ,&nbsp;Xia Yao ,&nbsp;Yan Zhu ,&nbsp;Weixing Cao ,&nbsp;Tao Cheng","doi":"10.1016/j.compag.2024.109722","DOIUrl":"10.1016/j.compag.2024.109722","url":null,"abstract":"","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"229 ","pages":"Article 109722"},"PeriodicalIF":7.7,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142747221","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":"Human robot interaction for agricultural Tele-Operation, using virtual Reality: A feasibility study","authors":"Daniel Udekwe,&nbsp;Hasan Seyyedhasani","doi":"10.1016/j.compag.2024.109702","DOIUrl":"10.1016/j.compag.2024.109702","url":null,"abstract":"<div><div>With the increasing demand for efficient and sustainable agricultural practices, the automation of tasks such as crop inspection and harvesting has become a critical endeavor. However, the complex and dynamic nature of agricultural environments poses challenges for conventional methods that are fully autonomous or those relying on traditional interfaces. To address these challenges, we propose a solution that leverages the capabilities of Virtual Reality (VR) to provide operators with an intuitive and immersive control experience. This paper introduces a novel method for tele-operating a robotic system in agriculture using VR technology. By integrating a VR device with SteamVR and Unity 3D, users can control a mobile robotic module over a local network or the internet using VR hand controllers and a headset. In order to validates the system feasibility, we case studied two agricultural operations in lab settings: leaf inspection and crop harvesting.</div><div>The results of this study were evaluated based on the cycle completion time (CCT) and the success rate of robot-plant interaction (RPI). For fruit harvesting, with a sample size (N) = 5, the mean CCT was approximately 18 s, with a standard deviation of nearly 5 s, indicating an improvement compared to existing autonomous systems in the literature. Additionally, in the leaf inspections, the mean CCT resulted in approximately 26 s with the standard deviation of nearly 6 s with the same sample size. The RPI success rate reached up to 90 % in the fruit harvesting practices. And in leaf inspection practices, this metric averaged two attempts per diseased leaf, 50 %, to grasp it and bring it to the operator’s attention. Through this study, the combination of consumer-grade VR technologies with a mobile robotic manipulation system highlights the system’s promise in improving remote agricultural tasks, especially in response to labor scarcity and improving farmworker efficiency.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"228 ","pages":"Article 109702"},"PeriodicalIF":7.7,"publicationDate":"2024-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746252","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":"Design and experiment of monitoring system for feed rate on sugarcane chopper harvester","authors":"Baocheng Zhou ,&nbsp;Shaochun Ma ,&nbsp;Weiqing Li ,&nbsp;Jun Qian ,&nbsp;Wenzhi Li ,&nbsp;Sha Yang","doi":"10.1016/j.compag.2024.109695","DOIUrl":"10.1016/j.compag.2024.109695","url":null,"abstract":"<div><div>Real-time monitoring of sugarcane harvester feed rate is great significance for guiding harvesting operation and improving efficiency. In this study, a feed rate monitoring system of sugarcane harvester is designed and developed. The system adopts the proposed iterative wavelet threshold denoising technology to enhance data quality. Compared with Fourier transform and traditional wavelet threshold method, the signal-to-noise ratio of the collected signal is increased by 41.6% and 10.5% respectively, and the root mean square error is reduced by 32.5% and 12% respectively. A nonlinear adjustment particle swarm optimization back propagation neural network (NAPSO-BPNN) is introduced and established with the hydraulic motor outlet pressure of the base cutter, the hydraulic motor outlet pressure of the lower conveyor roller, the displacement of the upper conveyor roller, and the flow rate of the hydraulic motor of the chopper as inputs, and the feed rate as the output. The NAPSO-BPNN demonstrated lower uncertainty in initial weight and threshold settings, with determination coefficients increasing by 0.12 and 0.06, and average relative errors decreasing by 8% and 3.8% compared to traditional BPNN and PSO-BPNN. Finally, the accuracy and reliability of NAPSO-BPNN feed monitoring model were verified in three plots with sugarcane growing well, growing poorly, and seriously lodging. The determination coefficients of NAPSO-BPNN feed monitoring model on three plots are 0.954, 0.93 and 0.911 respectively. The average relative errors are 7.43%, 8.16% and 9.26% respectively, and the root mean square errors are 0.157, 0.223 and 0.247 respectively. Therefore, the monitoring system of feed rate developed in this study is accuracy and reliability in different plots. The outcomes of this study are expected to provide robust technical support for adjusting the operational status of harvesters and optimizing real-time parameters.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"228 ","pages":"Article 109695"},"PeriodicalIF":7.7,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746251","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":"Design and experiment of facility elevated planting strawberry continuous picking manipulator","authors":"Minghui Wang ,&nbsp;Zhengdong Zhou ,&nbsp;Yulong Wang ,&nbsp;Jian Xu ,&nbsp;Yongjie Cui","doi":"10.1016/j.compag.2024.109703","DOIUrl":"10.1016/j.compag.2024.109703","url":null,"abstract":"<div><div>Strawberry picking manipulator is one of the key factors affecting the effectiveness of robot picking operations, elevated strawberries cultivated off the ground with hanging fruits provide convenient conditions for automatic strawberry picking. In order to adapt to the small space of elevated strawberry picking operation and to solve the problem of discontinuous picking process, a P-P-R-P type continuous strawberry picking manipulator was proposed. Based on the elevated cultivation mode and the clamping-shear picking method, the structural parameters and control system of the continuous picking manipulator were determined. The kinematics model of the continuous picking manipulator was established, the Jacobian condition number and performance of each joint in the picking operation were solved, and its flexibility and operability were analyzed. According to the growth characteristics of elevated strawberries, the picking simulation routes and operation cycles were set. Using MATLAB software, the simulation tests were carried out to analyze the working space of the continuous picking manipulator, the maximum speed and acceleration changes of each joint. In order to improve the success rate of strawberry picking, the operating parameters such as rotational speed, end picking angle and clamping position of the continuous picking manipulator were optimized using Box-Behnken test. Based on the simulation results and parameter optimization results, a continuous picking manipulator and motion control system were constructed and validation tests were conducted. The results showed that the average picking success rate of the continuous picking manipulator was 74.21 %, and the average picking efficiency was 12.87 s per piece. In conclusion, the P-P-R-P type continuous picking manipulator proposed in this study meets the automated operation requirements of strawberry picking and can be adapted to the elevated operating environment.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"228 ","pages":"Article 109703"},"PeriodicalIF":7.7,"publicationDate":"2024-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142746347","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}