Artificial Intelligence in Agriculture最新文献

{"title":"A lightweight keypoint detection model-based method for strawberry recognition and picking point localization in multi-occlusion scenes","authors":"Dezhi Wang,&nbsp;Xiaochan Wang,&nbsp;Yinyan Shi,&nbsp;Xiaolei Zhang,&nbsp;Yanyu Chen,&nbsp;Jinming Zheng,&nbsp;Nan Liu","doi":"10.1016/j.aiia.2025.10.009","DOIUrl":"10.1016/j.aiia.2025.10.009","url":null,"abstract":"<div><div>Strawberries grown on elevated stands usually suffer from fruit occlusion issues, which severely limit the implementation of strawberry recognition and picking point localization, and the embedded devices carried by strawberry picking robots have high requirements for model lightweighting, posing a dual challenge to the efficient execution of automated picking tasks by robots. To address this issue, this study proposes a method for strawberry recognition and picking point localization in multi-occlusion scenes based on a lightweight keypoint detection model. Firstly, a strawberry dataset covering no, slight, moderate, and heavy occlusion scenes is constructed. Then, a lightweight strawberry recognition and keypoint detection network, LS-net, is proposed. LS-net improves the spatial relationship modelling capability between strawberries and stems by integrating the lightweight MobileNetv4 backbone with the Mobile Grouped-Query Attention mechanism; improves the feature pyramid network using depthwise separable convolutions and incorporates an anchor-free decoupled head network to reduce computational complexity while maintaining detection accuracy; and introduces the Matrix Non-Maximum Suppression to optimize the processing of overlapping strawberries, which effectively reduces the false negative detections. Based on the keypoint detection results from LS-net, the picking point coordinates and stem pose are calculated after a series of processes such as region-of-interest extraction, binarization, and depth data alignment. The experimental results show that the accuracy of LS-net is 91.07 %, the mean average precision is 93.93 %, and the average pixel Euclidean distance is 4.79. By deploying LS-net to the embedded device, its frames per second reaches 78.2, and the success rates of 3D picking point localization and stem pose estimation are 84.07 % and 81.32 %, respectively. LS-net and related methods provide a visual recognition solution adapted to embedded devices for strawberry picking robots.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 1","pages":"Pages 316-341"},"PeriodicalIF":12.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145465549","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-arm robotic system and strategy for the automatic packaging of apples","authors":"Yizhi Zhang ,&nbsp;Liping Chen ,&nbsp;Xin Li ,&nbsp;Qicheng Li ,&nbsp;Jiangbo Li","doi":"10.1016/j.aiia.2025.11.006","DOIUrl":"10.1016/j.aiia.2025.11.006","url":null,"abstract":"<div><div>Packaging is a crucial step in the commercial distribution of apples after harvest. However, achieving fast and accurate automated packaging remains a challenge. This study proposed for the first time a highly integrated four-arm robotic system and an effective implementation strategy specifically designed for automatic packaging of apples. In order to achieve accurate and simultaneous detection of multi-channel apples in complex visual conditions, an enhanced target detection model (i.e. YOLOv8s-GUC) based on the YOLOv8s architecture was developed by combining with a global attention mechanism (GAM), UniRepLKNet large kernel convolution structure, and CARAFE lightweight upsampling module. The results demonstrated that the YOLOv8s-GUC model can significantly improve detection accuracy and generalization for apples and related small features (such as stems and calyxes), achieving an [email protected] of 99.5 %. Furthermore, considering the complexity and real-time constraints of task planning in multi-arm robotic operations, this study further proposed an intelligent scheduling algorithm based on a Deep Q-Network (DQN), which enables efficient collaboration and collision-free operation among the robotic arms through real-time state perception and online decision-making. The results of simulations and real-world experiments indicated that the developed multi-arm robotic packaging system and scheduling strategy had high operational stability and efficiency in apple packaging, with a success rate of 100 % and an average packaging time of less than one second per apple. This study provides an effective and reliable solution for automated apple packaging.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 1","pages":"Pages 578-591"},"PeriodicalIF":12.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145693435","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":"Adaptive compensation method for navigation positioning errors considering the vibration characteristics of a combine harvester1","authors":"Wenbo Wei ,&nbsp;Hui Wang ,&nbsp;Maohua Xiao ,&nbsp;Yejun Zhu ,&nbsp;Dongfang Li ,&nbsp;Xiaomei Xu ,&nbsp;Peiqi Guo ,&nbsp;Chenshuo Xie ,&nbsp;He Zheng","doi":"10.1016/j.aiia.2025.12.002","DOIUrl":"10.1016/j.aiia.2025.12.002","url":null,"abstract":"<div><div>Positioning accuracy directly affects the operational performance and stability of navigation systems. However, in complex field environments, severe vibrations during combine harvester operation can significantly exacerbate positioning errors. These vibrations have become a key limiting factor for navigation accuracy improvements. This study proposes an adaptive compensation method for navigation positioning errors that considers the vibration characteristics of combine harvesters. The proposed method aims to enhance anti-interference positioning accuracy during fully autonomous harvesting operations. First, a hybrid Whale Optimization Algorithm (WOA)–Light Gradient Boosting Machine (LightGBM) model was developed to identify operational stages. This model was trained using vibration data collected at 100 Hz over 15 s, yielding 1500 time-frame samples. Then, a regression prediction model for positioning errors was established using an eXtreme Gradient Boosting (XGBoost)–Multilayer Perceptron (MLP) framework. This model was built from GNSS/INS error data recorded at 5 Hz over 175 s, resulting in 875 time-frame samples. Finally, GNSS data compensated with the predicted positioning errors were fused with Inertial Navigation System (INS) data using an error-state Kalman filter (ESKF) within the same local planar coordinate system to achieve adaptive error compensation. Experimental results showed high identification accuracy and strong error prediction performance, with a maximum lateral deviation of 0.039 m in the straight-line path tracking test. During autonomous harvesting, the system maintained strong stability with an average lateral deviation of 0.087 m. This study provides a new approach for achieving high-precision positioning of combine harvesters under vibration disturbances. It also offers valuable insights for the development of anti-interference positioning technologies in other ground-based agricultural vehicles.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 1","pages":"Pages 695-710"},"PeriodicalIF":12.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145975954","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":"Decoupling tea-bud heap structure from non-imaging hyperspectral spectra for accurate single-bud trace biochemistry retrieval","authors":"Ning Qi ,&nbsp;Hao Yang ,&nbsp;Jianbo Qi ,&nbsp;Wenjuan Li ,&nbsp;Jinpeng Cheng ,&nbsp;Xiaodong Yang ,&nbsp;Bo Xu ,&nbsp;Ze Xu ,&nbsp;Guijun Yang ,&nbsp;Chunjiang Zhao","doi":"10.1016/j.aiia.2025.11.003","DOIUrl":"10.1016/j.aiia.2025.11.003","url":null,"abstract":"<div><div>Accurate, real-time, non-destructive estimation of single-bud biochemistry is critical for managing green-tea quality, yet non-imaging hyperspectral measurements of arbitrarily stacked buds are confounded by structured interference from mixed adaxial/abaxial surfaces and their three-dimensional arrangement. We propose Bilinear Spectral Derivative Gaussian Process Regression (BSDGPR), which couples a bilinear spectral-derivative model with Gaussian process regression to minimise structured interference. First- and second-order spectral derivatives attenuate the weakly wavelength-dependent leaf-spatial-arrangement term (<em>R</em>_<em>LSA</em>), while unit-sphere normalization combined with a chord-distance radial basis function kernel eliminate the plan-view leaf-front area fraction (<em>f</em>). This effectively standardizes tea-bud heap spectra to single-bud equivalents. (i) Structure suppression and invariance: independent-contribution analysis shows that moving to the derivative domain boosts the heap term's explanatory power to 80.7–99.4 % across bands while reducing <em>R</em>_<em>LSA</em> to ≤9.3 % (TH:LSA ratio of between 4.8 and 40.9), and the sphere-normalized chord-distance kernel cancels <em>f</em>-driven gain variation across samples. (ii) Accuracy and efficiency: relative to reflectance-domain GPR, BSDGPR raises R² by 0.13 and lowers NRMSE by 3–5 percentage points for tea polyphenols (0.68 → 0.81; 15.55 % → 12.64 %), amino acids (0.56 → 0.71; 19.78 % → 15.72 %), and soluble sugars (0.86 → 0.91; 10.85 % → 9.05 %), while maintaining fast inference (10–13 s per model) suitable for near real-time use. (iii) Transferability: models trained on tea-bud heaps generalize to canopy measurements with minimal loss (ΔR² ≤ 0.05; canopy NRMSE 11–15 %), remain credible across cultivars (R² = 0.37–0.74; NRMSE ≤23 % across 15 varieties), are stable at typical sensor resolutions (≤ 10–15 nm), and achieve R² = 0.63–0.66 with NRMSE ≤22 % for wheat canopy chlorophyll. Overall, BSDGPR is a reliable, field-ready AI approach for non-destructive biochemical sensing across crops, sensors, and management scenarios, offering broad applicability and strong potential for precision agriculture.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 1","pages":"Pages 397-411"},"PeriodicalIF":12.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145527919","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-stage fusion of dual attention mask R-CNN and geometric filtering for fast and accurate localization of occluded apples","authors":"Houkang Jiang ,&nbsp;Jizhan Liu ,&nbsp;Xiaojie Lei ,&nbsp;Baocheng Xu ,&nbsp;Yucheng Jin","doi":"10.1016/j.aiia.2025.10.005","DOIUrl":"10.1016/j.aiia.2025.10.005","url":null,"abstract":"<div><div>In unstructured orchard environments, factors such as complex lighting, fruit occlusion, and fruit clustering significantly reduce the accuracy of apple detection and 3D localization in robotic harvesting systems. To enhance the perception and reconstruction of occluded fruits, this paper proposes a multi-stage fusion framework for high-precision and robust processing, from image enhancement to 3D reconstruction. First, an adaptive image enhancement algorithm based on the HSV color space is employed to effectively alleviate image degradation caused by uneven lighting. Then, an improved Mask R-CNN with a dual-attention mechanism (SE-CBAM) is introduced to achieve scale-adaptive fruit segmentation under occlusion conditions. Next, a hierarchical point cloud purification strategy combining depth clustering and geometric feature analysis is applied to remove branch and leaf interference. Finally, a two-step RANSAC-LM spherical fitting algorithm is used to quickly and accurately recover the 3D shape of the fruit from incomplete point clouds. Experimental results show that the proposed method achieves Mask IoUs of 0.89, 0.82, and 0.65 under mild, moderate, and severe occlusion, respectively, with the lowest 3D localization error of 0.42 cm and an overall processing frame rate of 20 FPS. In real orchard environments, the harvesting success rate under occlusion conditions reaches up to 82.7 %, significantly outperforming traditional point cloud centroid and 2D positioning methods. This study provides an efficient, robust, and real-time deployable visual solution for fruit localization and robotic harvesting in complex orchard environments.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 1","pages":"Pages 187-205"},"PeriodicalIF":12.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145361969","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":"ADeepWeeD: An adaptive deep learning framework for weed species classification","authors":"Md Geaur Rahman ,&nbsp;Md Anisur Rahman ,&nbsp;Mohammad Zavid Parvez ,&nbsp;Md Anwarul Kaium Patwary ,&nbsp;Tofael Ahamed ,&nbsp;David A. Fleming-Muñoz ,&nbsp;Saad Aloteibi ,&nbsp;Mohammad Ali Moni PhD","doi":"10.1016/j.aiia.2025.04.009","DOIUrl":"10.1016/j.aiia.2025.04.009","url":null,"abstract":"<div><div>Efficient weed management in agricultural fields is essential for attaining optimal crop yields and safeguarding global food security. Every year, farmers worldwide invest significant time, capital, and resources to combat yield losses, approximately USD 75.6 billion, due to weed infestations. Deep Learning (DL) methodologies have been recently implemented to revolutionise agricultural practices, particularly in weed detection and classification. Existing DL-based weed classification techniques, including VGG16 and ResNet50, initially construct a model by implementing the algorithm on a training dataset comprising weed species, subsequently employing the model to identify weed species acquired during training. Given the dynamic nature of crop fields, we argue that existing methods may exhibit suboptimal performance due to two key issues: (i) the unavailability of all training weed species initially, as these species emerge over time, resulting in a progressively expanding training dataset, and (ii) the constrained memory and computational capacity of the system utilised for model development, which hinders the retention of all weed species that manifest over an extended duration. To address the issues, this paper introduces a novel DL-based framework called ADeepWeeD for weed classification that facilitates adaptive (i.e. incremental) learning so that it can handle new weed species by keeping track of historical information. ADeepWeeD is evaluated using two criteria, namely <span><math><msub><mi>F</mi><mn>1</mn></msub></math></span>-Score and classification accuracy, by comparing its performances against four non-incremental and two incremental state-of-the-art methods on three publicly available large datasets. Our experimental results demonstrate that ADeepWeeD outperforms existing techniques used in this study. We believe that our developed model could be used to develop an automation system for weed identification. The code of the proposed method is available on GitHub: <span><span>https://github.com/grahman20/ADeepWeed</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 4","pages":"Pages 590-609"},"PeriodicalIF":8.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178825","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":"Accurate Orah fruit detection method using lightweight improved YOLOv8n model verified by optimized deployment on edge device","authors":"Hongwei Li ,&nbsp;Yongmei Mo ,&nbsp;Jiasheng Chen ,&nbsp;Jiqing Chen ,&nbsp;Jiabao Li","doi":"10.1016/j.aiia.2025.05.001","DOIUrl":"10.1016/j.aiia.2025.05.001","url":null,"abstract":"<div><div>The replacement of personal computer terminal with edge device is recognized as a portable and cost-effective potential solution in solving equipment miniaturization and achieving high flexibility of robotic fruit harvesting at in-field scale. This study proposes a lightweight improved You Only Look Once version 8n (YOLOv8n) model for detecting Orah fruits and deploying this model on an edge device. First of all, the model size was reduced while maintaining detection accuracy via the introduction of the ADown modules. Subsequently, a Concentrated-Comprehensive Dual Convolution (C3_DualConv) module combining dual convolutional bottlenecks was proposed to enhance the model capability to capture features of Orah fruits obscured by branches and leaves; this practice further reduced the model size. Additionally, a Bidirectional Feature Pyramid Network (BiFPN) that includes a pyramid level 2 high-resolution layer was employed for more efficient multi-scale feature fusion. Besides, three Coordinate Attention (CA) mechanism modules were also added to improve the recognition and capture capability for Orah fruit features. Finally, a more focused minimum points distance intersection over union loss was adopted to boost the detection efficiency of densely occluded Orah fruits. Experimentally demonstrating that the improved YOLOv8n model accurately detected Orah fruits in complex orchard environments, achieving a 97.7 % of precision, an Average Precision at IoU threshold 0.5 ([email protected]) of 98.8 %, and a 96.69 % of F1 score, while maintaining a compact model size of 4.1 MB, under a Windows-based system terminal. This proposed model was optimally deployed on an Nvidia Jetson Orin Nano using TensorRT Python Application Programming Interface (API), the average interface speed exceeds 30 fps, indicating a real-time detection ability. This study can provide technical support for Orah fruit robotic harvesting on the basis of edge device.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 4","pages":"Pages 707-723"},"PeriodicalIF":8.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144254405","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":"A review on enhancing agricultural intelligence with large language models","authors":"Hongda Li ,&nbsp;Huarui Wu ,&nbsp;Qingxue Li ,&nbsp;Chunjiang Zhao","doi":"10.1016/j.aiia.2025.05.006","DOIUrl":"10.1016/j.aiia.2025.05.006","url":null,"abstract":"<div><div>This paper systematically explores the application potential of large language models (LLMs) in the field of agricultural intelligence, focusing on key technologies and practical pathways. The study focuses on the adaptation of LLMs to agricultural knowledge, starting with foundational concepts such as architecture design, pre-training strategies, and fine-tuning techniques, to build a technical framework for knowledge integration in the agricultural domain. Using tools such as vector databases and knowledge graphs, the study enables the structured development of professional agricultural knowledge bases. Additionally, by combining multimodal learning and intelligent question-answering (Q&amp;A) system design, it validates the application value of LLMs in agricultural knowledge services. Addressing core challenges in domain adaptation, including knowledge acquisition and integration, logical reasoning, multimodal data processing, agent collaboration, and dynamic knowledge updating, the paper proposes targeted solutions. The study further explores the innovative applications of LLMs in scenarios such as precision crop management and market dynamics analysis, providing theoretical support and technical pathways for the development of agricultural intelligence. Through the technological innovation of large language models and their deep integration with the agricultural sector, the intelligence level of agricultural production, decision-making, and services can be effectively enhanced.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 4","pages":"Pages 671-685"},"PeriodicalIF":8.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144254489","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":"EU-GAN: A root inpainting network for improving 2D soil-cultivated root phenotyping","authors":"Shangyuan Xie ,&nbsp;Jiawei Shi ,&nbsp;Wen Li ,&nbsp;Tao Luo ,&nbsp;Weikun Li ,&nbsp;Lingfeng Duan ,&nbsp;Peng Song ,&nbsp;Xiyan Yang ,&nbsp;Baoqi Li ,&nbsp;Wanneng Yang","doi":"10.1016/j.aiia.2025.06.004","DOIUrl":"10.1016/j.aiia.2025.06.004","url":null,"abstract":"<div><div>Beyond its fundamental roles in nutrient uptake and plant anchorage, the root system critically influences crop development and stress tolerance. Rhizobox enables in situ and nondestructive phenotypic detection of roots in soil, serving as a cost-effective root imaging method. However, the opacity of the soil often results in intermittent gaps in the root images, which reduces the accuracy of the root phenotype calculations. We present a root inpainting method built upon Generative Adversarial Networks (GANs) architecture In addition, we built a hybrid root inpainting dataset (HRID) that contains 1206 cotton root images with real gaps and 7716 rice root images with generated gaps. Compared with computer simulation root images, our dataset provides real root system architecture (RSA) and root texture information. Our method avoids cropping during training by instead utilizing downsampled images to provide the overall root morphology. The model is trained using binary cross-entropy loss to distinguish between root and non-root pixels. Additionally, Dice loss is employed to mitigate the challenge of imbalanced data distribution Additionally, we remove the skip connections in U-Net and introduce an edge attention module (EAM) to capture more detailed information. Compared with other methods, our approach significantly improves the recall rate from 17.35 % to 35.75 % on the test dataset of 122 cotton root images, revealing improved inpainting capabilities. The trait error reduction rates (TERRs) for the root area, root length, convex hull area, and root depth are 76.07 %, 68.63 %, 48.64 %, and 88.28 %, respectively, enabling a substantial improvement in the accuracy of root phenotyping. The codes for the EU-GAN and the 8922 labeled images are open-access, which could be reused by researchers in other AI-related work. This method establishes a robust solution for root phenotyping, thereby increasing breeding program efficiency and advancing our understanding of root system dynamics.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 4","pages":"Pages 770-782"},"PeriodicalIF":8.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307336","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":"Rapid detection and visualization of physiological signatures in cotton leaves under Verticillium wilt stress","authors":"Na Wu ,&nbsp;Pan Gao ,&nbsp;Jie Wu ,&nbsp;Yun Zhao ,&nbsp;Xing Xu ,&nbsp;Chu Zhang ,&nbsp;Erik Alexandersson ,&nbsp;Juan Yang ,&nbsp;Qinlin Xiao ,&nbsp;Yong He","doi":"10.1016/j.aiia.2025.06.002","DOIUrl":"10.1016/j.aiia.2025.06.002","url":null,"abstract":"<div><div>Verticillium wilt poses a severe threat to cotton growth and significantly impacts cotton yield. It is of significant importance to detect Verticillium wilt stress in time. In this study, the effects of Verticillium wilt stress on the microstructure and physiological indicators (SOD, POD, CAT, MDA, Chl_a, Chl_b, Chl_ab, Car) of cotton leaves were investigated, and the feasibility of utilizing hyperspectral imaging to estimate physiological indicators of cotton leaves was explored. The results showed that Verticillium wilt stress-induced alterations in cotton leaf cell morphology, leading to the disruption and decomposition of chloroplasts and mitochondria. In addition, compared to healthy leaves, infected leaves exhibited significantly higher activities of SOD and POD, along with increased MDA amounts, while chlorophyll and carotenoid levels were notably reduced. Furthermore, rapid detection models for cotton physiological indicators were constructed, with the <em>R</em>_<em>p</em> of the optimal models ranging from 0.809 to 0.975. Based on these models, visual distribution maps of the physiological signatures across cotton leaves were created. These results indicated that the physiological phenotype of cotton leaves could be effectively detected by hyperspectral imaging, which could provide a solid theoretical basis for the rapid detection of Verticillium wilt stress.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"15 4","pages":"Pages 757-769"},"PeriodicalIF":8.2,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144263501","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}