Computers and Electronics in Agriculture最新文献

{"title":"3-D shape control of deformable linear objects for branch handling using an adaptive Lyapunov-based scheme","authors":"Omid Aghajanzadeh ,&nbsp;Mohammadreza Shetab-Bushehri ,&nbsp;Miguel Aranda ,&nbsp;Juan Antonio Corrales Ramon ,&nbsp;Christophe Cariou ,&nbsp;Roland Lenain ,&nbsp;Youcef Mezouar","doi":"10.1016/j.compag.2025.109931","DOIUrl":"10.1016/j.compag.2025.109931","url":null,"abstract":"<div><div>Despite its various applications, robotic manipulation of deformable objects in agriculture has experienced limited development so far. This is due to the specific challenges in this domain, i.e., the variety of objects in this field is wide, and the deformation properties of the objects cannot be easily recognized in advance. In addition, deformable objects generally have complex dynamics and high-dimensional configuration space. In this paper, the manipulation of deformable linear objects (DLOs) is addressed by considering these challenges. Concretely, a new indirect adaptive control method is proposed to manipulate DLOs by controlling their shape in 3-D space towards previously defined targets, with a specific focus on agricultural applications. The proposed method can follow a desired dynamic evolution of the shape with a smooth deformation that brings about a stable gripper motion. This property of the method can protect the object from possible damages, even under large deformations, which is crucial in agricultural scenarios. An adaptation law is leveraged for estimating the system parameters, and Lyapunov analysis is employed to study the validity of the proposed control scheme. The scheme can be applied to diverse objects that can be modeled as linear, including tree branches or other rod-like structures. The effectiveness of the scheme is demonstrated through various experiments where, using shape feedback obtained from a 3-D camera, a robotic arm controls the shape of a flexible foam rod and of branches of different plants.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"232 ","pages":"Article 109931"},"PeriodicalIF":7.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143420612","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":"Using improved density peak clustering algorithm for flower cluster identification and apple central and peripheral flower detection","authors":"Mingyang Geng ,&nbsp;Yuying Shang ,&nbsp;Shiyu Xiang ,&nbsp;Jiachen Wang ,&nbsp;Lei Wang ,&nbsp;Huaibo Song","doi":"10.1016/j.compag.2025.110095","DOIUrl":"10.1016/j.compag.2025.110095","url":null,"abstract":"<div><div>Apple flower detection and positioning are crucial for the mechanical and chemical thinning of flowers, where typically only one or two of the strongest flowers in each cluster are retained. An improved method is proposed that leverages the YOLOv8n model for accurate flower detection. The DPC algorithm is enhanced to automatically determine the number of flower clusters and accurately identify the central flowers within those clusters. To evaluate the performance of the enhanced Single-Layer DPC algorithm, it was compared with several other clustering methods, including DPC, DPC with Shared Nearest Neighbors (DPC-SNN), K-means, K-medoids, Gaussian Mixture Model (GMM), Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Spectral Clustering (SC), minibatch and 3W-PEDP. The results demonstrated that the proposed method achieved that the Adjusted Mutual Information (AMI) and Adjusted Rand Index (ARI) were 0.7037 and 0.6043, respectively, on the Flame dataset, surpassing the highest scores obtained by other methods (0.5886 and 0.5116, respectively). Additionally, the improved algorithm reduced the deviation between the clustering center produced by the Single-Layer DPC and the true central flower. Overall, the algorithm effectively reduces clustering center deviations, showcasing its capability to accurately detect and position apple flowers.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"232 ","pages":"Article 110095"},"PeriodicalIF":7.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403563","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":"Hysteresis in flag leaf temperature based on meteorological factors during the reproductive growth stage of wheat and the design of a predictive model","authors":"Baolin Wu ,&nbsp;Yidong Song ,&nbsp;Weiwei Wang ,&nbsp;Weifan Xu ,&nbsp;Jiahao Li ,&nbsp;Fengli Sun ,&nbsp;Chao Zhang ,&nbsp;Shuqin Yang ,&nbsp;Jifeng Ning ,&nbsp;Yajun Xi","doi":"10.1016/j.compag.2025.110113","DOIUrl":"10.1016/j.compag.2025.110113","url":null,"abstract":"<div><div>The physiological state of functional leaves in crops plays a vital role in yield formation. Over two consecutive winter wheat growing seasons, we continuously monitored the flag leaf temperature (Tf) during the reproductive growth stage and collected key meteorological indicators, including air temperature (Ta), relative humidity (Ha), soil temperature (Ts), and photosynthetically active radiation (PAR). Pearson correlation analysis, stepwise regression analysis, and path analysis revealed that Ta, PAR, Ts, and Ha are the main environmental factors influencing Tf. These variables were identified as key for further analysis. Notably, Tf exhibited a positive time lag correlation with PAR, while Ta and Ts lag showed positive lag correlation with Tf, and Ha demonstrated a negative lag correlation with Tf. Among the analyzed meteorological factors, soil temperature displayed the smallest lag effect relative to Tf, consistently trailing behind it. PAR showed a pronounced lag effect, shifting an hour earlier than Tf, while Ta exhibited a significant hour-long delay after Tf. Ha primarily functioned as a cooling influence, lagging approximately one hour behind Tf. Moreover, the intensity of the time delay effect will vary depending on the developmental stage. Integrating these time-lag relationships significantly enhanced the accuracy of Tf simulations. Support Vector Regression (SVR) demonstrated robust predictive performance (<em>R²</em> = 0.937, RMSE = 2.048 °C), indicating its potential for accurate prediction of Tf in wheat production. This study highlights the time-delay effects between Tf and meteorological factors during the reproductive growth stage of wheat, offering a predictive model that provides a foundation for monitoring crop physiological conditions in real time.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"232 ","pages":"Article 110113"},"PeriodicalIF":7.7,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395726","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":"Dynamic changes and spectrometric quantitative analysis of antioxidant enzyme activity of TYLCV-infected tomato plants","authors":"Jiheng Ni,&nbsp;Yawen Xue,&nbsp;Jialin Liao","doi":"10.1016/j.compag.2025.110109","DOIUrl":"10.1016/j.compag.2025.110109","url":null,"abstract":"<div><div>To investigate the dynamic changes in superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) activities in <em>Tomato yellow leaf</em> curl <em>virus</em> (TYLCV)-infected tomato leaves is essential for monitoring tomato growth, selecting disease-resistant varieties and disease control. Utilizing hyperspectral technique offers a rapid and non-destructive method to estimate antioxidant enzyme activity in tomato leaves. This study focuses on one a TYLCV-susceptible tomato variety (Hezuo 908) and two TYLCV-resistant varieties (Dingyanfen No. 3 and No. 5) to analyze the changes in photosynthetic characteristics and antioxidant enzyme activity following TYLCV infection. Hyperspectral data were used to quantify the correlation between antioxidant enzyme activity and spectral features under different pretreatments, as well as the relationship between enzyme activity and classical spectral indices. Several optimized indices were developed by iterating on the condition of R, forming a combined index. Considering efficiency and complexity, the support vector machine regression algorithm was employed to evaluate the predictive performance of the models. The results showed a decline in photosynthetic rate and relative chlorophyll content, while stomatal conductance initially decreased and then increased. The activities of the three antioxidant enzymes increased. activities of the three antioxidant enzymes post-TYLCV infection, with POD activity correlating with tomato variety resistance—a potential auxiliary index for antiviral variety identification. Among the models, the CAT prediction model performed the best, with a test set determination coefficient (R²) of 0.82, followed by POD with an R² of 0.67, and SOD R² of 0.43. These findings demonstrate that spectra enable rapid and non-destructive estimation of antioxidant enzyme activity in tomatoes under viral stress. This study provides valuable insights for antiviral variety breeding and the early warning of viral diseases.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"232 ","pages":"Article 110109"},"PeriodicalIF":7.7,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403564","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":"Efficient motion planning for chili flower pollination mechanism based on BI-RRT","authors":"Zelong Ni ,&nbsp;Qingdang Li ,&nbsp;Mingyue Zhang","doi":"10.1016/j.compag.2025.110063","DOIUrl":"10.1016/j.compag.2025.110063","url":null,"abstract":"<div><div>To achieve obstacle avoidance for a chili flower pollination robotic arm in complex and narrow environments, this paper proposes a pollination action planning algorithm based on Map Preprocessed Step-by-Step Informed Rapidly-exploring Random Trees (PSBI-RRT). The algorithm, which is based on the Bidirectional Rapidly-exploring Random Tree (BI-RRT) algorithm, pre-processes the robot’s task space to obtain local candidate points. These points are used to divide the task space and determine segmented local goal points based on the distribution of obstacles. In the PSBI-RRT algorithm, a segmented dynamic sampling space is used instead of a fixed sampling space to reduce invalid sampling points. By introducing a hybrid expansion method combining greedy expansion with adaptive goal point attraction, the algorithm rapidly generates paths, improving the flexibility of the PSBI-RRT algorithm in exploring unknown spaces and enhancing its adaptability to the environment. 3D simulation experiments in various environmental types show that the PSBI-RRT algorithm reduces search time by over 95%, and it decreases invalid sampling nodes by 70% compared to traditional methods. The quality of the pollination path is optimized, with an average path length reduction of 30%. Pollination experiments with a 6-DOF robotic arm in both simulated and real environments show that the collision-free paths planned by the algorithm successfully guide the robotic arm from the initial to the target position without collisions. Additionally, in several typical pollination tasks, the success rate of path planning remains at 95%, significantly improving the planning success rate.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"232 ","pages":"Article 110063"},"PeriodicalIF":7.7,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395100","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":"Factors affecting deep learning model performance in citizen science–based image data collection for agriculture: A case study on coffee crops","authors":"Juan C. Rivera-Palacio ,&nbsp;Christian Bunn ,&nbsp;Masahiro Ryo","doi":"10.1016/j.compag.2025.110096","DOIUrl":"10.1016/j.compag.2025.110096","url":null,"abstract":"<div><div>Citizen science is an effective approach for collecting extensive data scalable for deep learning, although data quality is debatable. However, few studies have determined the factors associated with data collection that affect model performance and potential sampling bias. This study aims to identify the factors that significantly influence the performance of a deep learning object detection model in agricultural prediction tasks. To do so, we analyzed errors in a You Only Look Once (YOLO v8) model trained for counting the number of coffee cherries in mobile pictures. The model was trained with 436 images taken in Colombia and Peru collected by local farmers as a citizen science approach. We analyzed the prediction errors of the model using 637 additional pictures. We then applied a linear mixed model (LMM) and a decision tree machine learning model to regress the model’s error against predictor variables related to the following categories: photographer influence, geographic location, mobile phone characteristics, picture characteristics, and coffee varieties. Our results show the strong influence of photographer identity and adherence (whether the image collection protocol was followed or not) on model prediction error. Following the protocol can increase model performance from an <span><math><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup></math></span> of 0.48 to 0.73. Additionally, model performance varied significantly depending on photographer identity, with <span><math><msup><mrow><mi>R</mi></mrow><mn>2</mn></msup></math></span> ranging from 0.45 to 0.93. In contrast, factors such as mobile phone characteristics (e.g., frontal camera resolution, flash type, and screen size), using the screen behind the branch to obscure other cherries, coffee varieties, and geographic location did not significantly affect prediction error. These findings demonstrate that data quality in citizen science–based data collection for enhancing model prediction can be achieved through straightforward and comprehensive protocols, customized volunteer training, and regular feedback from experts. Such measures collectively support the robust application of deep learning models in agriculture. Furthermore, this study demonstrated that any mobile device with a camera can contribute to citizen science initiatives, underscoring the potential and scalability of this approach in agricultural research.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"232 ","pages":"Article 110096"},"PeriodicalIF":7.7,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143403565","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":"Autonomous navigation method for agricultural robots in high-bed cultivation environments","authors":"Takuya Fujinaga","doi":"10.1016/j.compag.2025.110001","DOIUrl":"10.1016/j.compag.2025.110001","url":null,"abstract":"<div><div>This study proposes an autonomous navigation method for agricultural robots designed for high-bed cultivation. The proposed method integrates two navigation strategies: waypoint navigation, which directs the robot to a predefined waypoint, and cultivation bed navigation, which ensures precise movement between cultivation beds. By alternating between these navigation methods, the robot can achieve self-navigation within a farm without relying on path planning, which requires accurate localization in areas with limited environmental features. The robot uses light detection and ranging (LiDAR) point cloud data to navigate effectively. The navigation approach was initially simulated in a virtual environment and then evaluated in a real-world strawberry farm. The results demonstrated the ability of the robot to maintain a specified distance of ± 0.05 m and an orientation angle of ± 5° relative to the cultivation bed. These findings confirm the feasibility of the proposed method for achieving accurate and stable navigation on a farm. This study also highlights the importance of simulations in agricultural robotics development. Simulated environments provide a cost-effective platform for refining robot specifications, such as sensor selection and navigation algorithms, before real-world deployment. For example, simulations have shown that reducing the maximum measurement range of the LiDAR can significantly impact localization accuracy and navigation stability. Future work will focus on creating dynamic simulation environments that replicate real-world conditions, such as uneven surfaces and varying farm layouts. Enhancing simulation fidelity will improve the reliability of evaluations and accelerate the practical implementation of agricultural robots, contributing to their broader adoption and efficiency in farming operations.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"231 ","pages":"Article 110001"},"PeriodicalIF":7.7,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143394964","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":"Spatio-temporal changes in subsurface soil salinity based on electromagnetic induction and environmental covariates at the Tarim River Basin, southern Xinjiang, China","authors":"Fei Wang ,&nbsp;Yang Wei ,&nbsp;Shengtian Yang","doi":"10.1016/j.compag.2025.110108","DOIUrl":"10.1016/j.compag.2025.110108","url":null,"abstract":"<div><div>Changes in water and salt content in the root zone directly affect the survival strategies and health status of natural vegetation in arid regions. Since the 1980s, climate change and intensified human activity have resulted in spatial changes in soil salinity in the root zone (SSRZ, 30–100 cm) of the oases in the Tarim Basin. These changes have not been explored adequately. Therefore, this study quantifies the SSRZ based on Landsat OLI, electromagnetic induction (EMI), and auxiliary variables to evaluate the difference between current conditions and the historical record. Environmental covariates derived from the SCORPAN framework were employed for SSRZ modelling using the random forest (RF) algorithm. The results were as follows: (1) The EC_a spatial model established based on ECa readings and environmental variables showed that the R² values of the validation models were between 0.77 and 0.84 at four EMI modes. (2) The EC_a maps were then used as environmental variables for RF-SSRZ model construction with 84 % modelling accuracy using a calibration dataset (70 %) and validation dataset (30 %), which indicated that 83 % of the SSRZ spatial variation could be explained by this method. (3) Variable importance calculated by recursive feature elimination (RFE) showed that the contribution of four EMI dipole modes occupied the top four positions in the variable importance ranking. (4) The spatial variability pattern of the ECa maps and the RF-SSRZ map was approximately similar to the EC_eHWSD (30–100 cm) affiliated with the Harmonized World Soil Database (HWSD) generated in the 1980 s. (5) The SSRZ change over the past 40 years was calculated based on the EC_eHWSD and SSRZ maps with HWSD soil type units, and 43 % of the area showed alleviation. The three desalination levels were 2.88 % for –10 to 0 dS·m⁻¹; 12.49 % for –10 to –28.33 dS·m⁻¹; and 27.58 % for &lt;–28.33 dS·m⁻¹. Within the oasis and on both sides of the Tarim River, a 57.05 % increasing trend was computed between 0 and 2.08 dS·m⁻¹.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"232 ","pages":"Article 110108"},"PeriodicalIF":7.7,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395103","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":"A novel hybrid modeling approach based on empirical methods, PSO, XGBoost, and multiple GCMs for forecasting long-term reference evapotranspiration in a data scarce-area","authors":"Ali EL Bilali ,&nbsp;Abdessamad Hadri ,&nbsp;Abdeslam Taleb ,&nbsp;Meryem Tanarhte ,&nbsp;El Mahdi EL Khalki ,&nbsp;Mohamed Hakim Kharrou","doi":"10.1016/j.compag.2025.110106","DOIUrl":"10.1016/j.compag.2025.110106","url":null,"abstract":"<div><div>Estimation of the Reference Evapotranspiration (ET_o) is critical in water resources management under climate change, especially in arid and semi-arid regions. Thus, estimating baseline ET_o poses significant challenges, particularly in inadequate climatological monitoring regions. In this study, a hybrid modeling approach based on the incorporation of empirical models, Particle Swarm Optimization (PSO), and XGBoost algorithm (Empirical-PSO-XGBoost) was developed and evaluated to forecast ET_o under limited climate variables. The results showed the Empirical-PSO-XGBoost outperformed the purely calibrated empirical and Temperature-PSO-XGBoost models for estimating monthly (daily) ET_o with NSE reaching 0.99 (0.86) and 0.98 (0.67) for the calibration and validation phases, respectively. Besides, up to 63 CMIP6 projections were coupled with Empirical-PSO-XGBoost for forecasting the long-term ET_o under SSP245 and SSP585 climate change scenarios. Thus, the simulation showed a significant increase in ET_o and seasonal patterns compared to the baseline ET_o where the change in range of [+5, +10] % is associated with probability values of 0.65 and 0.78 for SSP245 and SSP585, respectively. Overall, the developed framework is useful for implementing adaptation strategies to mitigate climate change effects on water resource allocation and agricultural management. It provides the ET_o associated with Exceedance probability for each month which is useful for assessing the water availability-related-risk in scheduling irrigation and sowing date of crops.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"232 ","pages":"Article 110106"},"PeriodicalIF":7.7,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395104","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 verification of a litchi combing and cutting end-effector based on visual-tactile fusion","authors":"Zhaoshen Yao ,&nbsp;Juntao Xiong ,&nbsp;Jiayuan Yang ,&nbsp;Xiao Wang ,&nbsp;Zexing Li ,&nbsp;Yuhua Huang ,&nbsp;Yanan Li","doi":"10.1016/j.compag.2025.110077","DOIUrl":"10.1016/j.compag.2025.110077","url":null,"abstract":"<div><div>According to the characteristics of litchi tree branching and litchi fruit growing in clusters, this paper designs a litchi combing and cutting end-effector based on visual-tactile fusion with reference to human hair-cutting behavior. The litchi information in the image is perceived visually using the zero-sample prediction large model and the Hough circle detection principle, the tactile timing of the litchi fruit stem is captured by the tactile sensors between the clamping fingers, and the fruit stem clamping status is monitored through the tactile timing update mechanism, and finally the fusion at the decision-making level is realized by integrating visual and tactile in information, realizing the perception of the litchi fruit stem for the litchi picking robot. The picking trial results show that the static tactile fruit stem perception rate is only 16.7%, while the use of visual-tactile fusion increases the rate to 86.7%. The average of a single picking achieves 1.08 bunches. The end actuator and clamping and cutting picking method in this paper provide technical basis for efficient and low-loss litchi picking.</div></div>","PeriodicalId":50627,"journal":{"name":"Computers and Electronics in Agriculture","volume":"232 ","pages":"Article 110077"},"PeriodicalIF":7.7,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395105","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}