Artificial Intelligence in Agriculture最新文献

{"title":"Picking point localization method based on semantic reasoning for complex picking scenarios in vineyards","authors":"Xuemin Lin ,&nbsp;Jinhai Wang ,&nbsp;Jinshuan Wang ,&nbsp;Huiling Wei ,&nbsp;Mingyou Chen ,&nbsp;Lufeng Luo","doi":"10.1016/j.aiia.2025.05.004","DOIUrl":"10.1016/j.aiia.2025.05.004","url":null,"abstract":"<div><div>In the complex orchard environment, precise picking point localization is crucial for the automation of fruit picking robots. However, existing methods are prone to positioning errors when dealing with complex scenarios such as short peduncles, partial occlusion, or complete misidentification, which can affect the actual work efficiency of the fruit picking robot. This study proposes an enhanced picking point localization method based on semantic reasoning for complex picking scenarios in vineyard. It innovatively designs three modules: the semantic reasoning module (SRM), the ROI threshold adjustment strategy (RTAS), and the picking point location optimization module (PPOM). The SRM is applied to handle the scenarios of grape peduncles being obstructed by obstacles, partial misidentification of peduncles, and complete misidentification of peduncles. The RTAS addresses the issue of low and short peduncles during the picking process. Finally, the PPOM optimizes the final position of the picking point, allowing the robotic arm to perform the picking operation with greater flexibility. Experimental results show that SegFormer achieves an mIoU (mean Intersection over Union) of 84.54 %, with B_IoU and P_IoU reaching 73.90 % and 75.63 %, respectively. Additionally, the success rate of the improved fruit picking point localization algorithm reached 94.96 %, surpassing the baseline algorithm by 8.12 %. The algorithm's average processing time is 0.5428 ± 0.0063 s, meeting the practical requirements for real-time picking.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 4","pages":"Pages 744-756"},"PeriodicalIF":8.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144263500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-camera fusion and bird-eye view location mapping for deep learning-based cattle behavior monitoring","authors":"Muhammad Fahad Nasir ,&nbsp;Alvaro Fuentes ,&nbsp;Shujie Han ,&nbsp;Jiaqi Liu ,&nbsp;Yongchae Jeong ,&nbsp;Sook Yoon ,&nbsp;Dong Sun Park","doi":"10.1016/j.aiia.2025.06.001","DOIUrl":"10.1016/j.aiia.2025.06.001","url":null,"abstract":"<div><div>Cattle behavioral monitoring is an integral component of the modern infrastructure of the livestock industry. Ensuring cattle well-being requires precise observation, typically using wearable devices or surveillance cameras. Integrating deep learning into these systems enhances the monitoring of cattle behavior. However, challenges remain, such as occlusions, pose variations, and limited camera viewpoints, which hinder accurate detection and location mapping of individual cattle. To address these challenges, this paper proposes a multi-viewpoint surveillance system for indoor cattle barns, using footage from four cameras and deep learning-based models including action detection and pose estimation for behavior monitoring. The system accurately detects hierarchical behaviors across camera viewpoints. These results are fed into a Bird's Eye View (BEV) algorithm, producing precise cattle position maps in the barn. Despite complexities like overlapping and non-overlapping camera regions, our system, implemented on a real farm, ensures accurate cattle detection and BEV-based projections in real-time. Detailed experiments validate the system's efficiency, offering an end-to-end methodology for accurate behavior detection and location mapping of individual cattle using multi-camera data.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 4","pages":"Pages 724-743"},"PeriodicalIF":8.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144263581","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-scale cross-modal feature fusion and cost-sensitive loss function for differential detection of occluded bagging pears in practical orchards","authors":"Shengli Yan ,&nbsp;Wenhui Hou ,&nbsp;Yuan Rao ,&nbsp;Dan Jiang ,&nbsp;Xiu Jin ,&nbsp;Tan Wang ,&nbsp;Yuwei Wang ,&nbsp;Lu Liu ,&nbsp;Tong Zhang ,&nbsp;Arthur Genis","doi":"10.1016/j.aiia.2025.05.002","DOIUrl":"10.1016/j.aiia.2025.05.002","url":null,"abstract":"<div><div>In practical orchards, the challenges posed by fruit overlapping, branch and leaf occlusion, significantly impede the successful implementation of automated picking, particularly for bagging pears. To address this issue, this paper introduces the multi-scale cross-modal feature fusion and cost-sensitive classification loss function network (MCCNet), specifically designed to accurately detect bagging pears with various occlusion categories. The network designs a dual-stream convolutional neural network as its backbone, enabling the parallel extraction of multi-modal features. Meanwhile, we propose a novel lightweight cross-modal feature fusion method, inspired by enhancing shared features between modalities while extracting specific features from RGB and depth modalities. The cross-modal method enhances the perceptual capabilities of the model by facilitating the fusion of complementary information from multimodal bagging pear image pairs. Furthermore, we optimize the classification loss function by transforming it into a cost-sensitive loss function, aiming to improve detection classification efficiency and reduce instances of missing and false detections during the picking process. Experimental results on a bagging pear dataset demonstrate that our MCCNet achieves mAP0.5 and mAP0.5:0.95 values of 97.3 % and 80.3 %, respectively, representing improvements of 3.6 % and 6.3 % over the classical YOLOv10m model. When benchmarked against several state-of-the-art detection models, our MCCNet network has only 19.5 million parameters while maintaining superior inference speed.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 4","pages":"Pages 573-589"},"PeriodicalIF":8.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144139789","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decoding canola and oat crop health and productivity under drought and heat stress using bioelectrical signals and machine learning","authors":"Guoqi Wen,&nbsp;Bao-Luo Ma","doi":"10.1016/j.aiia.2025.04.006","DOIUrl":"10.1016/j.aiia.2025.04.006","url":null,"abstract":"<div><div>Abiotic stresses, such as heat and drought, often reduce crop yields by harming plant health. Plants have evolved complex signaling networks to mitigate environmental impacts, making monitoring in-situ biosignals a promising tool for assessing plant health in real time. In this study, needle-like sensors were used to measure electrical potential changes in oat and canola plants under heat and drought stress conditions. Signals were recorded over a 30-min period and segmented into time intervals of 1-, 5-, 10-, 20-, and 30-min. Machine learning algorithms, including Random Forest, K-Nearest Neighbors, and Support Vector Machines, were applied to classify stress conditions and estimate biomass based on 14 extracted bioelectrical features, such as signal amplitude and entropy. Results showed that heat stress primarily altered signal patterns, whereas drought stress affected the signal intensity, possibly due to a reduction in the flow rate of charged ions. Random Forest classifier successfully identified over 85 % of stressed crops within 30 min of signal recording. These signals also explained 58–95 % of the variation in plant aboveground and root biomass, depending on stress intensity and crop genotype. This study demonstrates the potential of using bioelectrical sensing as a rapid and efficient tool for stress detection and biomass estimation. Future research should explore the ability to use biosensors to capture genetic variability to mitigate abiotic stresses and combine this with remote sensing and other emerging precision agriculture technologies.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 4","pages":"Pages 696-706"},"PeriodicalIF":8.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144254491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"VMGP: A unified variational auto-encoder based multi-task model for multi-phenotype, multi-environment, and cross-population genomic selection in plants","authors":"Xiangyu Zhao ,&nbsp;Fuzhen Sun ,&nbsp;Jinlong Li ,&nbsp;Dongfeng Zhang ,&nbsp;Qiusi Zhang ,&nbsp;Zhongqiang Liu ,&nbsp;Changwei Tan ,&nbsp;Hongxiang Ma ,&nbsp;Kaiyi Wang","doi":"10.1016/j.aiia.2025.06.007","DOIUrl":"10.1016/j.aiia.2025.06.007","url":null,"abstract":"<div><div>Plant breeding stands as a cornerstone for agricultural productivity and the safeguarding of food security. The advent of Genomic Selection heralds a new epoch in breeding, characterized by its capacity to harness whole-genome variation for genomic prediction. This approach transcends the need for prior knowledge of genes associated with specific traits. Nonetheless, the vast dimensionality of genomic data juxtaposed with the relatively limited number of phenotypic samples often leads to the “curse of dimensionality”, where traditional statistical, machine learning, and deep learning methods are prone to overfitting and suboptimal predictive performance. To surmount this challenge, we introduce a unified Variational auto-encoder based Multi-task Genomic Prediction model (VMGP) that integrates self-supervised genomic compression and reconstruction with multiple prediction tasks. This approach provides a robust solution, offering a formidable predictive framework that has been rigorously validated across public datasets for wheat, rice, and maize. Our model demonstrates exceptional capabilities in multi-phenotype and multi-environment genomic prediction, successfully navigating the complexities of cross-population genomic selection and underscoring its unique strengths and utility. Furthermore, by integrating VMGP with model interpretability, we can effectively triage relevant single nucleotide polymorphisms, thereby enhancing prediction performance and proposing potential cost-effective genotyping solutions. The VMGP framework, with its simplicity, stable predictive prowess, and open-source code, is exceptionally well-suited for broad dissemination within plant breeding programs. It is particularly advantageous for breeders who prioritize phenotype prediction yet may not possess extensive knowledge in deep learning or proficiency in parameter tuning.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 4","pages":"Pages 829-842"},"PeriodicalIF":8.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144534365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FGPointKAN++ point cloud segmentation and adaptive key cutting plane recognition for cow body size measurement","authors":"Guoyuan Zhou ,&nbsp;Wenhao Ye ,&nbsp;Sheng Li ,&nbsp;Jian Zhao ,&nbsp;Zhiwen Wang ,&nbsp;Guoliang Li ,&nbsp;Jiawei Li","doi":"10.1016/j.aiia.2025.06.003","DOIUrl":"10.1016/j.aiia.2025.06.003","url":null,"abstract":"<div><div>Accurate and efficient body size measurement is essential for health assessment and production management in modern animal husbandry. In order to realize the segmentation of the point clouds at the pixel-level and the accurate calculation of body size for the dairy cows in different postures, a segmentation model (FGPointKAN++) and an adaptive key cutting plane recognition (AKCPR) model are developed. FGPointKAN++ introduces FGE module and KAN that enhance local feature extraction and geometric consistency, significantly improving dairy cow part segmentation accuracy. The AKCPR utilizes adaptive plane fitting and dynamic orientation calibration to optimize the key body size measurement. The dairy cow body size parameters are then calculated based on the plane geometry features. The experimental results show that mIoU scores of 82.92 % and 83.24 % for the dairy cow pixel-level point cloud segmentation results. The calculated Mean Absolute Percentage Errors (MAPE) of Wither Height (WH), Body Width (BW), Chest Circumference (CC) and Abdominal Circumference (AC) are 2.07 %, 3.56 %, 2.24 % and 1.42 %, respectively. This method enables precise segmentation and automatic body size measurement of dairy cows in various walking postures, showing considerable potential for practical applications. It provides technical support for unmanned, intelligent, and precision farming, thereby enhancing animal welfare and improving economic efficiency.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 4","pages":"Pages 783-801"},"PeriodicalIF":8.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144365220","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving accuracy and generalization in single kernel oil characteristics prediction in maize using NIR-HSI and a knowledge-injected spectral tabtransformer","authors":"Anran Song ,&nbsp;Xinyu Guo ,&nbsp;Weiliang Wen ,&nbsp;Chuanyu Wang ,&nbsp;Shenghao Gu ,&nbsp;Xiaoqian Chen ,&nbsp;Juan Wang ,&nbsp;Chunjiang Zhao","doi":"10.1016/j.aiia.2025.05.007","DOIUrl":"10.1016/j.aiia.2025.05.007","url":null,"abstract":"<div><div>Near-infrared spectroscopy hyperspectral imaging (NIR-HSI) is widely used for seed component prediction due to its non-destructive and rapid nature. However, existing models often suffer from limited generalization, particularly when trained on small datasets, and there is a lack of effective deep learning (DL) models for spectral data analysis. To address these challenges, we propose the Knowledge-Injected Spectral TabTransformer (KIT-Spectral TabTransformer), an innovative adaptation of the traditional TabTransformer specifically designed for maize seeds. By integrating domain-specific knowledge, this approach enhances model training efficiency and predictive accuracy while reducing reliance on large datasets. The generalization capability of the model was rigorously validated through ten-fold cross-validation (10-CV). Compared to traditional machine learning methods, the attention-based CNN (ACNNR), and the Oil Characteristics Predictor Transformer (OCP-Transformer), the KIT-Spectral TabTransformer demonstrated superior performance in oil mass prediction, achieving <span><math><msubsup><mi>R</mi><mi>p</mi><mn>2</mn></msubsup></math></span>= 0.9238 ± 0.0346, RMSE_p = 0.1746 ± 0.0401. For oil content, <span><math><msubsup><mi>R</mi><mi>p</mi><mn>2</mn></msubsup></math></span>= 0.9602 ± 0.0180 and RMSE_p = 0.5301 ± 0.1446 on a dataset with oil content ranging from 0.81 % to 13.07 %. On the independent validation set, our model achieved <span><math><msup><mi>R</mi><mn>2</mn></msup></math></span> values of 0.7820 and 0.7586, along with RPD values of 2.1420 and 2.0355 in the two tasks, highlighting its strong prediction capability and potential for real-world application. These findings offer a potential method and direction for single seed oil prediction and related crop component analysis.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 4","pages":"Pages 802-815"},"PeriodicalIF":8.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144470916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MSNet: A multispectral-image driven rapeseed canopy instance segmentation network","authors":"Yuang Yang,&nbsp;Xiaole Wang,&nbsp;Fugui Zhang,&nbsp;Zhenchao Wu,&nbsp;Yu Wang,&nbsp;Yujie Liu,&nbsp;Xuan Lv,&nbsp;Bowen Luo,&nbsp;Liqing Chen,&nbsp;Yang Yang","doi":"10.1016/j.aiia.2025.05.008","DOIUrl":"10.1016/j.aiia.2025.05.008","url":null,"abstract":"<div><div>Precise detection of rapeseed and the growth of its canopy area are crucial phenotypic indicators of its growth status. Achieving accurate identification of the rapeseed target and its growth region provides significant data support for phenotypic analysis and breeding research. However, in natural field environments, rapeseed detection remains a substantial challenge due to the limited feature representation capabilities of RGB-only modalities. To address this challenge, this study proposes a dual-modal instance segmentation network, MSNet, based on YOLOv11n-seg, integrating both RGB and Near-Infrared (NIR) modalities. The main improvements of this network include three different fusion location strategies (frontend fusion, mid-stage fusion, and backend fusion) and the newly introduced Hierarchical Attention Fusion Block (HAFB) for multimodal feature fusion. Comparative experiments on fusion locations indicate that the mid-stage fusion strategy achieves the best balance between detection accuracy and parameter efficiency. Compared to the baseline network, the <em>mAP50:95</em> improvement can reach up to 3.5 %. After introducing the HAFB module, the MSNet-H-HAFB model demonstrates a 6.5 % increase in <em>mAP50:95</em> relative to the baseline network, with less than a 38 % increase in parameter count. It is noteworthy that the mid-stage fusion consistently delivered the best detection performance in all experiments, providing clear design guidance for selecting fusion locations in future multimodal networks. In addition, comparisons with various RGB-only instance segmentation models show that all the proposed MSNet-HAFB fusion models significantly outperform single-modal models in rapeseed count detection tasks, confirming the potential advantages of multispectral fusion strategies in agricultural target recognition. Finally, the MSNet was applied in an agricultural case study, including vegetation index level analysis and frost damage classification. The results show that ZN6–2836 and ZS11 were predicted as potential superior varieties, and the EVI2 vegetation index achieved the best performance in rapeseed frost damage classification.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 4","pages":"Pages 642-658"},"PeriodicalIF":8.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144231062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fast extraction of navigation line and crop position based on LiDAR for cabbage crops","authors":"Jiang Pin ,&nbsp;Tingfeng Guo ,&nbsp;Minzi Xv ,&nbsp;Xiangjun Zou ,&nbsp;Wenwu Hu","doi":"10.1016/j.aiia.2025.03.007","DOIUrl":"10.1016/j.aiia.2025.03.007","url":null,"abstract":"<div><div>This paper describes the design, algorithm development, and experimental verification of a precise spray perception system based on LiDAR were presented to address the issue that the navigation line extraction accuracy of self-propelled sprayers during field operations is low, resulting in wheels rolling over the ridges and excessive pesticide waste. A data processing framework was established for the precision spray perception system. Through data preprocessing, adaptive segmentation of crops and ditches, extraction of navigation lines and crop positioning, which were derived from the original LiDAR point cloud species. Data collection and analysis of the field environment of cabbages in different growth cycles were conducted to verify the stability of the precision spraying system. A controllable constant-speed experimental setup was established to compare the performance of LiDAR and depth camera in the same field environment. The experimental results show that at the self-propelled sprayer of speeds of 0.5 and 1 ms⁻¹, the maximum lateral error is 0.112 m in a cabbage ridge environment with inter-row weeds, with an mean absolute lateral error of 0.059 m. The processing speed per frame does not exceed 43 ms. Compared to the machine vision algorithm, this method reduces the average processing time by 122 ms. The proposed system demonstrates superior accuracy, processing time, and robustness in crop identification and navigation line extraction compared to the machine vision system.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 4","pages":"Pages 686-695"},"PeriodicalIF":8.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144254490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An autonomous navigation method for field phenotyping robot based on ground-air collaboration","authors":"Zikang Zhang ,&nbsp;Zhengda Li ,&nbsp;Meng Yang ,&nbsp;Jiale Cui ,&nbsp;Yang Shao ,&nbsp;Youchun Ding ,&nbsp;Wanneng Yang ,&nbsp;Wen Qiao ,&nbsp;Peng Song","doi":"10.1016/j.aiia.2025.05.005","DOIUrl":"10.1016/j.aiia.2025.05.005","url":null,"abstract":"<div><div>High-throughput phenotyping collection technology is important in affecting the efficiency of crop breeding. This study introduces a novel autonomous navigation method for phenotyping robots that leverages ground-air collaboration to meet the demands of unmanned crop phenotypic data collection. The proposed method employs a UAV equipped with a Real-Time Kinematic (RTK) module for the construction of high-precision Field maps. It utilizes SegFormor-B0 semantic segmentation models to detect crop rows, and extracts key coordinate points of these rows, and generates navigation paths for the phenotyping robots by mapping these points to actual geographic coordinates. Furthermore, an adaptive controller based on the Pure Pursuit algorithm is proposed, which dynamically adjusts the steering angle of the phenotyping robot in real-time, according to the distance (<span><math><mi>d</mi></math></span>), angular deviation (<span><math><mi>α</mi></math></span>) and the lateral deviation (<span><math><msub><mi>e</mi><mi>y</mi></msub></math></span>) between the robot's current position and its target position. This enables the robot to accurately trace paths in field environments. The results demonstrate that the mean absolute error (MAE) of the proposed method in extracting the centerline of potted plants area's rows is 2.83 cm, and the cropland's rows is 4.51 cm. The majority of global path tracking errors stay within 2 cm. In the potted plants area, 99.1 % of errors lie within this range, with a mean absolute error of 0.62 cm and a maximum error of 2.59 cm. In the cropland, 72.4 % of errors remain within this range, with a mean absolute error of 1.51 cm and a maximum error of 4.22 cm. Compared with traditional GNSS-based navigation methods and single vision methods, this method shows significant advantages in adapting to the dynamic growth of crops and complex field environments, which not only ensures that the phenotyping robot accurately travels along the crop rows during field operations to avoid damage to the crops, but also provides an efficient and accurate means of data acquisition for crop phenotyping.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 4","pages":"Pages 610-621"},"PeriodicalIF":8.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144194569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}