Proximal Remote Sensing of Oil Palm Basal Stem Rot Using Deep Learning Vision Algorithms and Ground-based Images
用手机拍摄油棕树干图像，结合深度学习检测与分类，实现基腐病严重度诊断。
农业AI与大模型
人工智能算法、大模型与决策支持系统
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Hybrid deep ensemble learning for crop disease prediction and climate-resilient disaster management in smart agriculture
提出混合深度集成框架，融合CNN与LSTM并引入注意力机制，实现作物病害预测与气候韧性灾害管理。
An Interpretable Deep Learning Framework for Spatial Prediction of Soil Salinity Using Multi-Source Remote Sensing and Environmental Data: A Case Study of Cangzhou, Hebei Province, China
构建融合多源遥感与环境变量的可解释深度学习框架，预测沧州土壤盐分空间分布。
PROSAIL-DNN-TL: A Hyperspectral Inversion Framework for Winter Wheat Leaf Chlorophyll Content Based on Fine-Tuned Transfer Learning
提出PROSAIL-DNN-TL框架，用PROSAIL模拟数据预训练DNN并全网络微调，反演冬小麦叶
APPLICATION POTENTIAL OF UNMANNED AERIAL VEHICLES AND TRENDS IN THEIR DEVELOPMENT
综述无人机应用潜力与发展趋势，强调其向自主化、网络化、数据驱动系统演进。
Pixel-level high-throughput estimation of crop photosynthetic phenotyping parameters using a multi-task deep learning framework
提出像素级多任务深度学习框架，从高光谱数据高通量估算作物光合表型参数。
A remote sensing image processing method based on edge computing and deep learning
提出边缘计算与深度学习结合的遥感图像分类方法，用改进3D CNN实现低延迟高精度土地覆盖识别。
Evaluating Reference Evapotranspiration and Soil Moisture as Predictors for Machine Learning-Based Actual Evapotranspiration Estimation: Model Performance and Explainability
评估ET0与土壤湿度对机器学习估算实际蒸散的影响及可解释性。
A stocktake of opportunities and knowledge gaps in advancing digital agriculture in Quebec
系统盘点魁北克数字农业技术应用及其对可持续发展的贡献与知识空白。
Research on the optimal modeling path for inversion of Pb content in rice leaves based on hyperspectral data of ground objects and machine learning and cross-scale remote sensing monitoring
研究基于地面高光谱与机器学习反演水稻叶片铅含量，并探索跨尺度遥感监测的最优建模路径。
Rule-driven functional zoning index prediction for sustainable agricultural landscapes design based on multimodal fusion deep learning
构建规则驱动农业景观功能分区指数，并用多模态融合深度学习实现自动预测。
UAV remote sensing for crop lodging monitoring: A comprehensive review
综述无人机遥感在作物倒伏监测中的技术流程、传感器与分析模型。
Deep Learning-Based Small-Object Detection in UAV Imagery: A Symmetry-Informed Review of Scale Variation and Scenario Constraints
综述无人机图像小目标检测的深度学习研究，提出对称性视角与三层框架。
Forest encroachment prediction using multi-temporal satellite data and machine learning: a case study of Bandipur National Park, India
用多时相Sentinel-2影像和机器学习预测印度Bandipur国家公园的森林侵占。
Sustainable soil governance pathways toward zero food gap
综述土壤科学、地球观测、AI与决策支持，提出可持续土壤治理实现零食物缺口的整合框架。
Smart Technologies for Precision Nutrient Management in Smallholder Farming: A Comprehensive Review
综述小农户精准养分管理技术，分析其适用性、经济可行性与采纳障碍。
Climate change, vegetation dynamics, and maize production in Mozambique: evidence from method of moments quantile regression and machine learning
研究气候变化与植被动态对莫桑比克玉米生产的影响，采用分位数回归和机器学习方法。
Analysis of crop residue cover in the North China Plain based on multispectral remote sensing and machine learning
提出基于线性光谱混合分析和随机森林的作物残茬覆盖度遥感估算方法，应用于华北平原。
OurMED D6.3 GoAigua Digital Twins Tool at the Farm, Basin and MED Scale. Version 1.2
开发了跨地块、流域和地中海尺度的农业数字孪生工具，集成多源数据支持实时监测与决策。
卫星数据+专用视觉Transformer的甜菜早期产量预测（Sentinel-2影像应用）
基于Sentinel-2光学影像的早期甜菜产量预测研究。通过紧耦合领域知识与机器学习，使用极小Vision Transformer patch size与全部Sentinel-2谱段，提升了模型表现。研究通过调整训练设置与基于排名的低产田检测方法，能在生长周期早期识别出大量低产田，为精准农业田间管理提供早期决策依据。