Rice Yield Prediction Using Multi-Modal Regression Learning with Optimized Ensemble Models
构建多模态回归学习框架，融合气候、遥感与作物因子，实现水稻产量连续预测。
水稻
以水稻为对象的生产、育种、植保、机械化与产业内容
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African Journal of Science Technology Innovation and Development·10月19日 · 周一80 分 论文水稻智慧农业 / 农业物联网阅读 0
A digital twin framework for proactive disease management in paddy cultivation using spatio-temporal graph neural networks
构建水稻数字孪生框架，用时空图神经网络提前14天预测病害。
Effects of within-season temporal aggregation of weather variables on performance and interpretability in rice yield machine learning models
评估生长季内天气变量时间聚合粒度对水稻产量机器学习模型预测与可解释性的影响。
International Journal of Computational and Applied Mathematics & Computer Science·10月12日 · 周一68 分 论文水稻农业人工智能与决策模型阅读 0
Comparative Analysis of Spatial Statistical Methods for Modelling Rice Yield Variability in Uniformity Trials
比较空间统计与随机森林模型对水稻产量空间变异的建模效果。
A Privacy-Preserving Federated Learning Framework for Paddy Leaf Disease Detection Using an Enhanced Pretrained Novel EfficientNet-Lite4 Model
提出联邦学习结合改进EfficientNet-Lite4的水稻叶片病害检测框架，兼顾隐私与精度。
Integrating UAV-Based Remote Sensing and Deep Learning for Early Detection of Bacterial Leaf Blight in Rice
Integrating UAV-Based Remote Sensing and Deep Learning for Early Detection of Bacterial Leaf Blight in Rice。Research Square
A multimodal deep learning for rice crop health monitoring using vegetation index feature selection
融合Sentinel-1/2植被指数与气象数据，用多模态深度学习监测水稻健康。
Dual‐Branch Network With Enhanced Feature Fusion for Improved Rice Classification Accuracy
提出双分支深度学习网络融合时序与辅助数据，提升亚热带水稻分类精度。
Comprehensive remote sensing and machine learning framework for multi-regional rice yield forecasting under hydroclimatic variability in Sahelian Niger
结合遥感植被指数与机器学习，预测尼日尔萨赫勒四地区2010–2025年水稻产量。
International Journal of Business Arts and Scientific Study·9月30日 · 周三63 分 论文肥料与水肥一体化农业人工智能与决策模型阅读 2
Automated Leaf Color Chart (LCC)-BasedNitrogen Fertilizer Recommendation UsingPaddy Leaf Image Color Classification
用深度学习对水稻叶片颜色分级，并结合多模态大模型实时推荐氮肥用量。
Hybrid Rule-Based and Machine Learning Decision-Support Framework for Smart Paddy Farming
提出规则与机器学习混合的水稻决策支持框架，提升决策精度并节水增产。
Balancing accuracy, completeness, and efficiency for rice 3D reconstruction through CBAM-UNet-based multi-view segmentation and camera configuration optimization
提出CBAM-UNet分割与相机配置优化结合的水稻多视角三维重建流程，兼顾精度、完整性与效率。
End-edge-cloud collaborative group phenotype recognition and diagnosis for rice seedling in vertical rice seedling cultivation
提出端边云协同的水稻秧苗群体表型识别与诊断方法，用于垂直育秧场景。
Spatiotemporal Deep Learning for Rice Plant Height Estimation from Multi-Temporal UAV RGB Imagery
提出Rice-STNet时空深度学习框架，用多时相无人机RGB影像估算水稻株高。
AgriMAC: An Attention Based Multimodal Deep Clustering Framework for Rice Health Assessment
提出无监督多模态聚类框架AgriMAC，融合无人机NDVI、IoT土壤与气象数据评估水稻健康。
IoT-RiceMobileNet: An improved lightweight MobileNetV2 Model for real-time multi-class rice disease detection using IoT
提出轻量级IoT-RiceMobileNet模型，实现水稻病害实时多类检测。
Multi-agent cooperative control for unmanned distributed-drive electric agricultural vehicle in paddy fields: tracking, stability, and energy-aware torque allocation
提出多智能体协同控制框架，实现水田分布式驱动电动农机路径跟踪、稳定性与能耗统一优化。
无人机遥感在水稻高通量表型分析中的研究进展 系统综述
系统综述199项研究，梳理无人机遥感在水稻高通量表型分析中的应用进展与瓶颈。
Deep learning-based classification of wet direct seeded rice, broadcasted direct seeded rice and transplanted rice using drone imagery for precision agriculture
用无人机可见光影像和深度学习模型区分直播稻、湿直播稻与移栽稻三种水稻种植方式。
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。