Artificial Intelligence in Agriculture最新文献

{"title":"Inversion of plant functional traits from hyperspectral imagery enhances the distinction of wheat stripe rust severity","authors":"Kehui Ren ,&nbsp;Anting Guo ,&nbsp;Binxiang Qian ,&nbsp;Chao Ruan ,&nbsp;Wenjiang Huang ,&nbsp;Yingying Dong ,&nbsp;Xia Jing ,&nbsp;Kun Wang ,&nbsp;Tiecheng Huang ,&nbsp;Huiqin Ma","doi":"10.1016/j.aiia.2025.10.006","DOIUrl":"10.1016/j.aiia.2025.10.006","url":null,"abstract":"<div><div>Wheat stripe rust can cause yield losses of up to 40 % during severe outbreaks, underscoring the importance of timely and accurate detection for effective management. Traditional hyperspectral methods relying on vegetation indices (VIs) and texture features (TFs) offer indirect assessments prone to environmental interference. In contrast, plant functional traits (PTs) supply a more consistent and informative reflection of stress progression. Accurately quantifying PTs, alongside traditional VIs and TFs, is therefore essential for constructing a comprehensive and robust disease monitoring framework. First, this study utilized correlation coefficient analysis and variance inflation factor (VIF) analysis to filter conventional variables (VIs and TFs) from candidate feature sets. The hybrid inversion model (HIM) was then employed to retrieve critical PTs—CCC, Car, Anth, CBC, and LAI—from UAV hyperspectral data, while canopy temperature (T_c) was derived from thermal imagery. Subsequently, 28 disease monitoring models were developed using machine learning algorithms (RF, AdaBoost, GBRT, and LASSO), incorporating both individual and combined features. Model performance was rigorously assessed through 6-fold cross-validation. The results demonstrated that PTs significantly responded to wheat rust disease (<em>p-value &lt; 0.001</em>), manifesting as reductions in pigment content (CCC, Car, Anth) and structural parameters (LAI), along with increases in CBC and T_c. During model development, PTs exhibited superior performance over VIs and TFs, given their strong association with plant health and sensitivity to biotic stress. Moreover, the sparsity of LASSO, combined with synergistic integration of multiple feature types, substantially enhanced model accuracy. The optimal model, integrating all three feature categories via LASSO regression, yielded R², RMSE, and MAE values of 0.628, 8.03 %, and 6.57 %, respectively. Overall, this study advances the accuracy of wheat stripe rust monitoring by integrating PTs, VIs, and TFs to create a physiological-spectral–morphological synergy, providing valuable insights for large-scale disease detection on airborne and satellite platforms.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 1","pages":"Pages 206-223"},"PeriodicalIF":12.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416195","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":"Two-year remote sensing and ground verification: Estimating chlorophyll content in winter wheat using UAV multi-spectral imagery","authors":"Wenjie Ai ,&nbsp;Guofeng Yang ,&nbsp;Zhongren Li ,&nbsp;Jiawei Du ,&nbsp;Lingzhen Ye ,&nbsp;Xuping Feng ,&nbsp;Xiangping Jin ,&nbsp;Yong He","doi":"10.1016/j.aiia.2025.10.017","DOIUrl":"10.1016/j.aiia.2025.10.017","url":null,"abstract":"<div><div>Leaf chlorophyll content serves as a critical biophysical indicator for characterizing wheat growth status. Traditional measurement using a SPAD meter, while convenient, is hampered by its localized sampling, low efficiency, and destructive nature, making it unsuitable for high-throughput field applications. To overcome these constraints, this research developed a novel approach for assessing canopy SPAD values in winter wheat by leveraging multispectral imagery obtained from an unmanned aerial vehicle (UAV). The generalizability of this methodology was rigorously evaluated through a replication experiment conducted in a subsequent growing season. Throughout the study, canopy reflectance data were acquired across key phenological stages and paired with synchronized ground-based SPAD measurements to construct stage-specific estimation models. The acquired multispectral images were processed to remove soil background interference, from which 17 distinct vegetation indices and 8 texture features were subsequently extracted. An in-depth examination followed, aiming to clarify the evolving interplay of these features with SPAD values throughout growth phases. Among the vegetation indices, the Modified Climate Change Canopy Vegetation Index (MCCCI) displayed a “rise-and-decline” pattern across the season, aligning with the crop's intrinsic growth dynamics and establishing it as a robust and phonologically interpretable proxy. Texture features, particularly contrast and entropy, demonstrated notable associations with SPAD values, reaching their peak strength during the booting stage. Comparative evaluation of various predictive modeling techniques revealed that a Support Vector Regression (SVR) model integrating both vegetation indices and texture features yielded the highest estimation accuracy. This integrated model outperformed models based solely on spectral or textural data, improving estimation accuracy by 23.81 % and 22.48 %, respectively. The model's strong generalization capability was further confirmed on the independent validation dataset from the second year (RMSE = 2.54, R² = 0.748). In summary, this study establishes an effective and transferable framework for non-destructively monitoring chlorophyll content in winter wheat canopies using UAV data.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 1","pages":"Pages 480-494"},"PeriodicalIF":12.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145579035","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":"YOLO-light-pruned: A lightweight model for monitoring maize seedling count and leaf age using near-ground and UAV RGB images","authors":"Tiantian Jiang ,&nbsp;Liang Li ,&nbsp;Zhen Zhang ,&nbsp;Xun Yu ,&nbsp;Yanqin Zhu ,&nbsp;Liming Li ,&nbsp;Yadong Liu ,&nbsp;Yali Bai ,&nbsp;Ziqian Tang ,&nbsp;Shuaibing Liu ,&nbsp;Yan Zhang ,&nbsp;Zheng Duan ,&nbsp;Dameng Yin ,&nbsp;Xiuliang Jin","doi":"10.1016/j.aiia.2025.10.002","DOIUrl":"10.1016/j.aiia.2025.10.002","url":null,"abstract":"<div><div>Maize seedling count and leaf age are critical indicators of early growth status, essential for effective field management and breeding variety selection. Traditional field monitoring methods are time-consuming, labor-intensive, and prone to subjective errors. Recently, deep learning-based object detection models have gained attention in crop seedling counting. However, many of these models exhibit high computational complexity and implementation costs, making field deployment challenging. Moreover, maize leaf age monitoring in field environments is barely investigated. Therefore, this study proposes two lightweight models, YOLOv8n-Light-Pruned (YOLOv8n-LP) and YOLOv11n-Light-Pruned (YOLOv11n-LP), for monitoring maize seedling count and leaf age in field RGB images. Our proposed models are improved from YOLOv8n and YOLOv11n by incorporating the DAttention mechanism, an improved BiFPN, an EfficientHead, and layer-adaptive magnitude-based pruning. The improvement in model complexity and model efficiency was significant, with the number of parameters reduced by over 73 % and model efficiency upgraded by up to 42.9 % depending on the device computation power. High accuracy was achieved in seedling counting (YOLOv8n-LP/ YOLOv11n-LP: AP = 0.968/0.969, R² = 0.91/0.94, rRMSE = 6.73 %/5.59 %), with significantly reduced model size (YOLOv8n-LP/ YOLOv11n-LP: parameters = 0.8 M/0.7 M, trained model size = 1.8 MB/1.7 MB). The robustness was validated across datasets with varying leaf ages (rRMSE = 4.07 % – 7.27 %), resolutions (rRMSE = 3.06 % – 6.28 %), seedling compositions (rRMSE = 1.09 % – 9.29 %), and planting densities (rRMSE = 3.38 % – 10.82 %). Finally, by integrating plant counting and leaf age estimation, the proposed models demonstrated high accuracy in leaf age detection using near-ground images (YOLOv8n-LP/ YOLOv11n-LP: rRMSE = 5.73 %/7.54 %) and UAV images (rRMSE = 9.24 %/14.44 %). The results demonstrate that the proposed models excel in detection accuracy, deployment efficiency, and adaptability to complex field environments, providing robust support for practical applications in precision agriculture.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 1","pages":"Pages 164-186"},"PeriodicalIF":12.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145324222","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":"Early detection of wheat powdery mildew: A multi-source in situ remote sensing approach enabled by stacked ensemble learning","authors":"Li Song ,&nbsp;Jiliang Zhao ,&nbsp;Yahui Li ,&nbsp;Linru Liu ,&nbsp;Jianzhao Duan ,&nbsp;Li He ,&nbsp;Yonghua Wang ,&nbsp;Tiancai Guo ,&nbsp;Wei Feng","doi":"10.1016/j.aiia.2025.10.004","DOIUrl":"10.1016/j.aiia.2025.10.004","url":null,"abstract":"<div><div>Powdery mildew seriously hinders photosynthesis and nutrient accumulation in wheat, and its early detection holds the key to enhancing control efficacy. In this research, solar-induced chlorophyll fluorescence (SIF) parameters were derived from radiance and reflectance data, while vegetation indices (VI) were computed using reflectance. A suite of feature selection methods, including shadow feature (Boruta), feature selection (ReliefF), minimum redundancy maximum correlation (mRMR), and random forest (RF). Models were developed on the back propagation (BP) neural network, support vector regression (SVR), and partial least squares regression (PLSR). Furthermore, a stacking ensemble strategy was adopted, utilizing RF and decision tree (DT) algorithms as meta-models to integrate the predictions from base models. The findings revealed that the Boruta method selected a well-balanced number of feature parameters with normalized weights. The multi-source model (SIF + VI) is superior to the single-source model (SIF or VI). The BP model exhibited high accuracy in wheat disease monitoring, particularly during the initial infection phases. The multi-regressor stacked with RF ensemble model (MRSRF) generally surpassed the multi-regressor stacked with DT ensemble model (MRSDT), especially in the initial infection stage, where the MRSRF model's average R² was 13.03 % higher than that of the BP model. To validate these conclusions, reflectance data simulated by the PROSAIL model (PROSPECT and SAIL) were utilized. The Boruta-MRSRF model demonstrated exceptional advantages in early detection, achieving an R² greater than 0.90 at all infection stages. This study provides effective ideas and methods for the active prevention and control of crop diseases, which are of great significance for ensuring agricultural production.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 1","pages":"Pages 124-138"},"PeriodicalIF":12.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145264763","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":"SCLFormer: A synergistic convolution-linear attention transformer for hyperspectral image classification of mechanical damage in maize kernels","authors":"Yiqiang Zheng ,&nbsp;Jun Fu ,&nbsp;Fengshuang Liu ,&nbsp;Haiming Zhao ,&nbsp;Jindai Liu","doi":"10.1016/j.aiia.2025.11.010","DOIUrl":"10.1016/j.aiia.2025.11.010","url":null,"abstract":"<div><div>Classifying mechanical damage in maize kernels using hyperspectral imaging is crucial for food security and loss reduction. Existing methods are constrained by high computational complexity and limited precision in detecting subtle damages, such as pericarp damage and kernel cracks. To address these challenges, we introduce a novel algorithm, the Synergistic Convolution and Linear Attention Transformer (SCLFormer). By replacing traditional softmax attention with linear attention, we reduce computational complexity from quadratic to linear, thereby enhancing efficiency. Integrating convolutional operations into the encoder enriches local prior information for global feature modeling, improving classification accuracy. SCLFormer achieves an overall accuracy of 97.08 % in classifying maize kernel damage, with over 85 % accuracy for cracked kernel and pericarp damage. Compared to softmax attention, SCLFormer reduces training and testing times by 355.27 s (16.86 %) and 0.71 s (23.67 %), respectively. Additionally, we propose a modular hyperspectral image-level classification framework that can integrate existing pixel-level feature extraction networks to achieve classification accuracies exceeding 80 %, demonstrating the framework's scalability. SCLFormer, serving as the framework's dedicated feature extraction component, provides a robust solution for maize kernel damage classification and exhibits substantial potential for broader spatial-scale applications. This framework establishes a novel technical paradigm for hyperspectral image-wise classification of other agricultural products.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 1","pages":"Pages 565-577"},"PeriodicalIF":12.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145693322","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":"Development of an enhanced hybrid attention YOLOv8s small object detection method for phenotypic analysis of root nodules","authors":"Ya Zhao ,&nbsp;Wen Zhang ,&nbsp;Liangxiao Zhang ,&nbsp;Xiaoqian Tang ,&nbsp;Du Wang ,&nbsp;Qi Zhang ,&nbsp;Peiwu Li","doi":"10.1016/j.aiia.2025.07.001","DOIUrl":"10.1016/j.aiia.2025.07.001","url":null,"abstract":"<div><div>Nodule formation and their involvement in biological nitrogen fixation are critical features of leguminous plants, with phenotypic characteristics closely linked to plant growth and nitrogen fixation efficiency. However, the phenotypic analysis of root nodules remains technically challenging due to their small size, weak texture, dense clustering, and occlusion. To address these challenges, this study constructed a scanner-based imaging platform and optimized data acquisition conditions for high-resolution, high-consistency root nodule images under field conditions. In addition, A hybrid small-object detection method, SCO-YOLOv8s, was proposed, integrating Swin Transformer and CBAM attention mechanisms into the YOLOv8s framework to enhance global and local feature representation. Furthermore, an Otsu segmentation-based post-processing module was incorporated to validate and refine detection results based on geometric features, boundary sharpness, and image entropy, effectively reducing false positives and enhancing robustness in complex scenes. Using this integrated approach, over 3375 nodules were identified from a single plant sample in under 1 min, with extracted phenotypic features such as diameter, color, and texture. A total of 10,879 high-quality annotated images were collected from 39 peanut varieties across 14 provinces and 31 soybean varieties across 12 provinces in China, addressing the current lack of large-scale datasets for legume root nodules. The SCO-YOLOv8s model achieved a precision of 97.29 %, a mAP of 98.23 %, and an overall identification accuracy of 95.83 %. This integrated approach provides a practical and scalable solution for high-throughput nodule phenotyping, and may contribute to a deeper understanding of nitrogen fixation mechanisms.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 1","pages":"Pages 12-43"},"PeriodicalIF":12.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144722714","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":"Transfer learning-based soybean LAI estimations by integrating PROSAIL, UAV, and PlanetScope imagery","authors":"Qing Li ,&nbsp;Yanan Wei ,&nbsp;Dalei Hao ,&nbsp;Weijian Yu ,&nbsp;Yelu Zeng","doi":"10.1016/j.aiia.2025.10.018","DOIUrl":"10.1016/j.aiia.2025.10.018","url":null,"abstract":"<div><div>Accurate Leaf Area Index (LAI) estimations at the soybean plot scale is achievable using high-resolution Unmanned Aerial Vehicle (UAV) imagery and field measurement samples. However, the limited coverage of UAV flights restricts large-scale remote sensing monitoring in expansive soybean fields. This study leverages the broad coverage and 3-m resolution of PlanetScope satellite imagery to extend LAI prediction from UAV to satellite scales through transfer learning, using UAV-scale LAI estimates as a benchmark to validate cross-scale consistency. To address this challenge, this study proposed the LAI-TransNet, a two-stage transfer learning framework designed for precise and scalable soybean LAI prediction across large areas, demonstrating its effectiveness in cross-scale monitoring. In Stage 1, a UAV-scale benchmark is established using PROSAIL-simulated UAV reflectance data (UAV-Sim) and field-measured soybean LAI. Traditional machine learning, deep learning, and transfer learning models are trained on a hybrid UAV-Sim and field-measured dataset (UAV-Sim_Measured), with the transfer learning model CNN-TL, fine-tuned using pre-trained weights derived from UAV-Sim, achieving the highest accuracy (<em>R</em>² = 0.81, RMSE = 0.64 m²/m², rRMSE = 11.5 %). In Stage 2, LAI-TransNet is developed by fine-tuning the CNN-TL model on PlanetScope simulated data (PS-Sim), preprocessed via cross-domain mapping to align UAV and satellite spectral features. Real PlanetScope imagery is corrected for reflectance consistency with reference to UAV imagery spectral profiles. LAI-TransNet outperforms other deep learning models trained directly on PS-Sim (<em>R</em>² = 0.69 vs. 0.60–0.63), ensuring robust cross-scale consistency. By bridging UAV and satellite scales, LAI-TransNet enables large-scale soybean LAI monitoring, enhancing precision agriculture management through improved monitoring with the PlanetScope imagery.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 1","pages":"Pages 365-380"},"PeriodicalIF":12.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145527909","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 perspective analysis of imaging-based monitoring systems in precision viticulture: Technologies, intelligent data analyses and research challenges","authors":"Annaclaudia Bono ,&nbsp;Cataldo Guaragnella ,&nbsp;Tiziana D'Orazio","doi":"10.1016/j.aiia.2025.08.001","DOIUrl":"10.1016/j.aiia.2025.08.001","url":null,"abstract":"<div><div>This paper presents a comprehensive review of recent advancements in intelligent monitoring systems within the precision viticulture sector. These systems have the potential to make agricultural production more efficient and ensure the adoption of sustainable practices to increase food production and meet growing global demand while maintaining high-quality standards. The review examines core components of non-destructive imaging-based monitoring systems in vineyards, focusing on sensors, tasks, and data processing methodologies. Particular emphasis is placed on solutions designed for practical, in-field deployment. The analysis revealed that the most commonly used sensors are RGB cameras and that the most widespread analysis focuses on grape bunches, as they provide information on both the quality and quantity of the harvest. Regarding the image processing methods, it emerged that those based on deep learning are the most adopted. In addition, a detailed analysis highlights the main technical and practical limitations in real-world scenarios, such as the management of computational resources, the need for large datasets, and the difficulties in interpreting the results. The paper concludes with an in-depth discussion of the challenges and open research questions, providing insights into potential future directions for intelligent monitoring systems in precision viticulture. These include the continued exploration of sensors to balance ease of use and accuracy, the development of generalizable methods, experimentation in real-world scenarios, and collaboration between experts for practical solutions.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 1","pages":"Pages 62-84"},"PeriodicalIF":12.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145048407","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":"STGMAE: A GNSS data-driven pre-training spatiotemporal graph masked autoencoder for agricultural machinery trajectory operation mode identification","authors":"Tailai Chen ,&nbsp;Weixin Zhai","doi":"10.1016/j.aiia.2025.10.007","DOIUrl":"10.1016/j.aiia.2025.10.007","url":null,"abstract":"<div><div>Utilizing spatiotemporal features in massive amounts of trajectory data to identify the operation mode of agricultural machinery trajectories is a key task in precision agriculture. Most of the previous studies focuses narrowly on single-perspective feature extraction, neglecting comprehensive spatiotemporal information in trajectory data. To improve the accuracy of the task, this paper proposes a model called STGMAE. First, we propose a multilevel feature extraction method (MFE), which extracts motion and statistical features from the initial features via a motion feature extractor and a sliding time window, and then uses the spectral feature module (SFM) to capture the spectral information, which improves the representation of trajectory data. Next, to prevent information loss in long-range encoding, we design a pre-training network with serial encoding and parallel decoding. Specifically, the data are first modeled globally interactively via a multiscale wavelet projector (WMP), and then enter an adaptive graph isomorphic neural network (AGIN). In AGIN, semi-adaptive masked Laplace operator (SAMLO) is used to capture the correlation information between trajectory points, and then a passing mechanism is used to address the homogeneous relationships between trajectory points and heterogeneous relationships between trajectory graphs. Then, the original feature and graph structure are reconstructed from the two encoding nodes to realize self-supervised training. Eventually, we use the pre-trained weights on the real trajectory samples provided by the Key Laboratory of Agricultural Machinery Monitoring and Big Data Application, Ministry of Agriculture and Rural Affairs, which contains 219 trajectory samples (2,250,693 trajectory points). The experimental results show that for the paddy, corn, and wheat harvesting trajectory datasets, our model accuracies are 95.50 %, 95.32 %, and 95.36 %, respectively, and the F1 scores are 94.54 %, 92.09 %, and 93.79 %, respectively. Compared with existing state-of-the-art methods, our method achieves accuracies of 5.75 %, 4.47 %, and 5.03 % and F1 scores of 7.26 %, 4.85 %, and 6.65 %, respectively.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 1","pages":"Pages 439-457"},"PeriodicalIF":12.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145527922","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 feature alignment network for 19-class semantic segmentation in agricultural environments","authors":"Zhi-xin Yao ,&nbsp;Hao Wang ,&nbsp;Zhi-jun Meng ,&nbsp;Liang-liang Yang ,&nbsp;Tai-hong Zhang","doi":"10.1016/j.aiia.2025.11.008","DOIUrl":"10.1016/j.aiia.2025.11.008","url":null,"abstract":"<div><div>To improve environmental perception and ensure reliable agricultural machinery navigation during field transitions under unstructured farm road conditions, this study utilizes high-resolution RGB camera vision navigation technology to propose a Multi-Scale Feature Alignment Network (MSFA-Net) for 19-class semantic segmentation of agricultural environment, which includes information such as roads, pedestrians, and vehicles. MSFA-Net introduces two key innovations: the DASP module, which integrates multi-scale feature extraction with dual attention mechanisms (spatial and channel), and the MSFA architecture, which enables robust boundary extraction and mitigates interference from lighting variations and obstacles like vegetation. Compared to existing models, MSFA-Net uniquely combines efficient multi-scale feature extraction with real-time inference capabilities, achieving an mIoU of 84.46 % and an mPA of 96.10 %. For 512 × 512 input images, the model processes an average of 26 images/s on a GTX 1650Ti, with a boundary extraction error of less than 0.47 m within 20 m. These results indicate that the proposed MSFA-Net can significantly reduce navigation errors and improve the perception stability of agricultural machinery during field operations. Furthermore, the model can be exported to ONNX or TensorFlow Lite formats, facilitating efficient deployment on embedded devices and existing farm navigation systems.</div></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"16 1","pages":"Pages 529-545"},"PeriodicalIF":12.4,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145623317","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}