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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Multi-Target Remote Sensing Segmentation with Cross-Scale Attention for Urban-Ecological Intelligence
评估UNet、SegNet和TransU-Net在多目标遥感语义分割中的性能，用于城市与生态监测。
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Long-term dynamic root monitoring based on classification fusion and attention mechanism
Long-term dynamic root monitoring based on classification fusion and attention mechanism。Biosystems Engineering
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Artificial Intelligence-Enabled Mangrove Ecosystem Monitoring Using Remote Sensing and Environmental Data
融合遥感影像与环境数据，用深度学习构建红树林生态系统健康监测框架。
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Cotton leaf disease classification using deep learning models for smart agriculture
Cotton leaf disease classification using deep learning models for smart agriculture。Agriculture & Food Security
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Downscaling of SMAP Soil Moisture Based on the Transformer Algorithm in Anhui Province
基于Transformer及变体融合多源卫星与地下水数据，将SMAP土壤湿度降尺度至1km日尺度。
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Leakage-aware, calibrated, and explainable deep learning for robust almond disease classification
提出防泄漏、校准且可解释的深度学习框架，用于稳健的杏仁病害分类。
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Explainable growth stage classification of cacao (Theobroma cacao L.) leaves and key feature visualization using vision transformer and transfer learning
用ViT和迁移学习对可可叶D、E期进行可解释分类，准确率达97%。
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A comprehensive review of deep learning methods for weed classification in precision agriculture
综述2018-2025年深度学习杂草分类方法，分析技术瓶颈并给出改进建议。
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Deep learning-driven multisource remote sensing image fusion: Advances, challenges, and future directions
综述深度学习多源遥感图像融合方法、挑战与未来方向。
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A Hybrid CNN–Transformer–LSTM Deep Learning Framework for Automated Cotton Disease Detection
提出CNN-Transformer-LSTM混合框架，实现棉花叶片病害自动识别。
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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。
2 个信源阅读 4
Precision Diagnosis of Apple Leaf Diseases Across Infection Stages via Web-Based Analysis
提出基于YOLOv8-MHSA与SAM的网页系统，实现苹果叶片病害多感染阶段实时检测与分级。
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High-resolution land cover mapping from coarse labels via a noisy label learning-guided cross-scale framework
提出噪声标签学习引导的跨尺度框架，用低分辨率粗标签生成高分辨率土地覆盖图。
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基于深度学习数据增强技术的肉牛行为识别 与养殖状态评估研究
比较四种骨干网络并做数据增强消融，实现9类肉牛行为识别与养殖状态评估。
2 个信源阅读 3
An interpretable deep embedding framework for data-driven discovery of crop families
An interpretable deep embedding framework for data-driven discovery of crop families。Smart Agricultural Technology
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LeafTrackNet: A deep learning framework for robust leaf tracking in top-down plant phenotyping
提出LeafTrackNet深度学习框架，实现俯视植物表型中稳健的叶片追踪。
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Artificial Intelligence, Machine Learning, and Deep Learning Concepts, Methods, and Real-World Impact
系统综述AI、机器学习与深度学习的概念、方法、应用及伦理挑战。
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DASO-RiceNet: 细粒度水稻病害与损伤分类的双注意力序列优化网络(准确率 96.5%)
提出双注意力序列优化网络DASO-RiceNet，实现10类水稻病害与损伤的高精度分类。
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Deep Learning-Based Weed Classification, Detection, and Segmentation in Precision Agriculture: a Bibliometric and Comprehensive Review
对精准农业中深度学习杂草分类、检测与分割研究进行文献计量与综述分析。
阅读 3