[预印本] Enhancing Deep Learning Model Performance through Data Generation Strategies: Classification of Olive Tree Diseases
结合传统增强、CutMix与DCGAN生成数据，提升橄榄树病害分类精度与类别平衡。
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Hybrid local–global vision transformer framework for robust plant disease detection in precision agriculture
提出混合局部-全局视觉Transformer模型ViT-LGFNet用于植物病害检测。
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[预印本]GANet 面向嵌入式系统的轻量化植物病害识别网络
提出轻量化植物病害识别网络GANet，在嵌入式设备上实现高精度实时识别。
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Evaluation of Concept-Rank Collapse Audits for Computer Vision Agriculture Disease Classifiers Under Low-Label Regimes: An Interpretability Capacity Bound for Smallholder Fungicide Selectivity
提出可解释性容量上界，评估低标签下概念秩崩溃审计的可靠性。
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A Comparative Study of Client-Level Stabilization Techniques in Federated Learning for Robust Plant Disease Recognition
比较联邦学习客户端级稳定化技术，提升植物病害识别的鲁棒性。
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A Nightshade-SWIN Transformer-Based Framework for Enhanced Multi-Crop Plant Disease Classification
提出Nightshade-SWIN框架，用Swin Transformer提升茄科多作物叶片病害分类
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PEAS-DeiT: Multi-Scale Spatial Attention Enhanced DeiT for Pea Leaf Disease Classification
提出PEAS-DeiT模型，将多尺度空间注意力融入DeiT，实现豌豆叶片病害分类。
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A global–local momentum contrastive framework with critical feature calibration for field plant disease identification in precision agriculture
提出GLC-Net，用全局-局部动量对比学习与关键特征校准提升田间植物病害识别。
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Image Processing-Based Efficient Hybrid Feature Extraction Approach for Plant Leaf Disease Recognition
提出融合CNN深度特征与手工特征的方法，用于植物叶片病害识别。
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Fuzzy-guided hyperparameter optimization for efficient CNN-based plant disease classification
提出模糊推理系统引导的CNN超参数优化方法，用于植物病害分类，兼顾精度与效率。
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CuFormer U-Net: a transformer-enhanced multi-task network for cucumber leaf disease detection and segmentation
提出CuFormer U-Net多任务网络，同时实现黄瓜叶病分类与病斑分割。
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A Graph Attention–Mamba Framework Utilizing Chinese Pangolin Optimization for Cross-Domain Plant Disease Recognition and Severity Estimation
提出图注意力-Mamba框架并用中华穿山甲算法优化，实现跨域植物病害识别与严重度估计。
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Plant disease classification using lightweight deep learning architectures
比较六种轻量神经网络在三种植物病害数据集上的分类性能，面向边缘设备部署。
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InResKolArno_SCO: An Efficient Plant Disease Classification Framework for IoT-Enabled Smart Agriculture
提出InResKolArno_SCO混合框架，用于物联网智慧农业中的植物病害分类。
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Fine-Tuned Hybrid Deep Learning Framework for Plant Disease Classification Using MobileNetV2 and EfficientNetB0 Feature Fusion
融合MobileNetV2与EfficientNetB0特征并微调，实现植物病害高精度分类。
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Segmentation of Cactus Diseases Using Machine and Deep Learning)
该研究用随机森林和多种深度学习模型对仙人掌病害进行分割诊断，并比较性能。
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A multi-agent multi-modal LLM with Monte Carlo tree search for interpretable plant disease classification
提出多智能体多模态LLM结合蒙特卡洛树搜索，实现可解释的植物病害分类。
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A Multi-Modal Generative Model for Tomato Disease Leaves Understanding
提出多模态生成模型SOLAR，统一理解番茄病害叶片的六类问答任务。
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A CNN-Based Approach for Leaf Disease Prediction in Smart Agriculture
用CNN对植物叶片病害进行自动识别，在基准数据集上取得92%准确率。
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AgriScope:面向农业图像的像素级多模态理解统一框架,arXiv 2609.20325(预印本)
提出AgriScope统一框架，实现农业图像像素级多模态理解与多任务交互。
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