[预印本] 基于YOLO计算机视觉的越南番荔枝果实主要病害检测（Computer Vision by YOLOs for Detection of Major Diseases in Vietnamese Custard Apple Using Fruit Imagery）
在越南农业数字化转型背景下，本研究采用YOLO系列目标检测模型，从番荔枝果实图像中识别主要病害，旨在实现更早的病害干预与更高产量，展示了计算机视觉在特色作物病虫害监测中的可行应用。
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Research progress and technical pathways of deep learning in agricultural remote sensing: a bibliometric and Task–Data–Model analysis
Deep learning has rapidly expanded in agricultural remote sensing, but its thematic evolution and technical configurations remain insufficiently examined together. This study analysed 1,706 Web of Science Core Collection articles published from 2017 to 2025 using CiteSpace and VOSviewer, and applied Task–Data–Model (T–D–M) coding to a publication-year-stratified random sample of 500 articles. Publications increased markedly after 2020, with broader collaboration networks and interdisciplinary participation. Four major themes were identified: crop growth and yield prediction, land-cover classification and segmentation, object-level recognition and fine-scale monitoring, and broader deep-learning applications. Temporal analyses showed growing attention to time-series modelling and multi-source integration. In the coded sample, Task–Data association was strongest (Cramér’s V = 0.362), exceeding Task–Model (0.215) and Data–Model (0.186). These results show that data-source selection is more task-structured than broad model-family use.
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Comparative Analysis of Deep Learning Architectures for Fine-Grained Classification of Dahlia Varieties
Fine-grained visual classification (FGVC) of flowers remains a significant challenge due to subtle inter-class variations and large intra-class diversity. In precision agriculture, deploying highly accurate models on resource-constrained edge platforms is severely restricted by computational overhead. To reduce this bottleneck, we introduce a specialized 63-category dahlia dataset comprising 3,279 field-captured images and study a Cross-Architecture Knowledge Distillation (CAKD) framework that transfers soft targets from a compact Vision Transformer (Swin-Tiny) teacher to a compact CNN student (MobileNetV3). Under a strictly identical training protocol for all models, the distilled student reaches a test accuracy of 97.51% ± 0.99% (mean ± standard deviation over three seeds) with only 4.28M parameters and 0.22 GMACs, exceeding every teacher in the pool: ResNet152 (97.01%, 58.27M), Swin-Tiny (96.82%, 27.57M) and EfficientNetV2-M (96.62%, 52.94M). Homogeneous distillation from the twice-larger ResNet152 teacher reaches 97.61%, a difference within seed variance, so the value of CAKD is efficiency rather than a higher accuracy ceiling. The student runs at 17.0 ms per image (58.8 FPS) on a consumer smartphone. We additionally report balanced accuracy, macro-F1 and head/middle/tail metrics, which show that overall accuracy conceals a substantial drop on rare cultivars. The compact student was exported to ONNX and integrated into an edge-cloud WeChat Mini Program that collects hard examples from the field for periodic retraining. The results position the system as a practical accuracy-efficiency trade-off for agricultural phenotyping and variety protection, rather than as a state-of-the-art claim on a newly constructed dataset.
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ConTransformer: Integrating Local and Global Features for Accurate Identification of Rice Leaf Diseases
提出双通道融合模型ConTransformer，精准识别五种水稻叶部病害。
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Estimating and Forecasting Grapevine Phenology using Deep Learning and Mechanistic models: A comparative study
比较机理模型与深度学习RNN预测葡萄物候期，并评估其提前预报能力。
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MACAT Net a hierarchical CNN transformer framework with multi scale attention for robust cassava leaf disease detection
提出MACAT-Net混合CNN-Transformer网络，实现木薯叶病害的鲁棒检测。
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TomaNet-6H: towards transparent plant disease detection using a modified CNN with Grad-CAM for tomato leaves
提出TomaNet-6H轻量CNN，用Grad-CAM实现番茄叶片病害可解释检测。
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LIGHTWEIGHT FEATURE FUSED ATTENTION DRIVEN MULTI LEVEL HYBRID MOBILENET V2 FRAMEWORK FOR EARLY TOMATO LEAF DISEASE DETECTION
提出轻量注意力多级混合MobileNet-V2框架，融合手工纹理与深度学习特征实现番茄叶片病害早期检
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A Comprehensive Review of Deep Learning and Computer Vision Techniques for Fruit Quality Assessment and Classification
系统综述深度学习与计算机视觉在水果品质评估与分级中的全流程方法。
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Enhanced Convolutional Neural Network Architecture with Attention-Guided Feature Refinement for Robust Image Classification
提出注意力引导细化网络AGR-Net，在CIFAR-10上兼顾精度与参数效率。
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A smart agriculture-oriented cross-attention multiscale CNN for sugarcane disease diagnosis
提出交叉注意力多尺度并行CNN，实现甘蔗叶病自动分类。
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Lightweight Hybrid CNN-Transformer Network for Robust Maize Leaf Disease Detection and Edge Deployment
构建轻量MobileNetV3-Small框架实现玉米叶片病害分类并评估边缘部署。
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MSFANet: a multi-scale feature aggregation network for tobacco pest recognition
提出MSFANet多尺度特征聚合网络，实现复杂田间背景下烟草害虫的自动识别。
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Detection of yam leaf diseases and pest damage under complex field conditions using LIMN-YOLO
提出轻量级LIMN-YOLO模型，在复杂田间条件下检测山药叶部病害与虫害。
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Deep Learning Approaches for Plant Disease Detection: A Review of Convolutional Neural Networks and Emerging Techniques
综述深度学习在植物病害检测中的应用，涵盖CNN、混合Transformer、注意力机制与生成式数据增
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Explainable deep learning framework for real-time insect pest detection in smart agriculture
提出可解释深度学习框架CPSA-RIPD-CSTGCN，实现智慧农业中害虫实时检测。
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An empirical performance comparison of deep learning models for apple leaf disease recognition
用UNet++分割苹果病叶并融合多种CNN与ViT分类，实现98.85%识别准确率。
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Hybrid CNN-LSTM Techniques for Plant Leaf Disease Detection and Farmer-Based Agricultural Practice Analysis in Western Tamil Nadu
结合CNN-LSTM混合模型检测植物叶片病害，并调查印度泰米尔纳德邦农户的农业实践与技术采纳情况。
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Detection of Apple Plant Diseases Using Leaf Images through Convolutional Neural Network
提出ResNet+GA+CNN混合模型，用苹果叶片图像检测病害并计算病斑面积占比。
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A Feature Engineering Framework for High-Precision Land Use and Land Cover Classification Using Remote Sensing Images
提出EODFE-HPLULC框架，结合YOLOv12、EfficientNet-B5和深度信念网络实
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