Functional photonic and optoelectronic materials and devices for climate-resilient smart agriculture: a review
综述光子与光电子材料器件结合物联网和机器学习，支撑气候韧性智慧农业的作物与土壤监测。
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#机器学习
Approaches to forecast soil nutrient dynamics for precision agriculture and sustainable fertiliser management: A review
综述土壤养分动态预测的统计与机器学习方法，比较精度与局限。
Tillage Practice Discrimination Using High-Resolution PlanetScope and Sentinel-2 Imagery
对比PlanetScope与Sentinel-2影像，用随机森林区分冬春两季的集约与保护性耕作。
Rice Cropping Pattern Mapping Using GEE-Based Sentinel-1/2 Time-Series Imagery and Machine Learning
基于GEE融合Sentinel-1/2时序与地形特征，用机器学习高精度制图长三角水稻种植模式。
Detection of Red Crown Rot of Soybean in Illinois Fields Using High-Resolution Satellite Imagery and Machine Learning
利用高分辨率卫星影像和机器学习在大豆田中检测红冠腐病。
Understanding Land Use/Land Cover Dynamics and Land Surface Temperature Variations Through Machine Learning and Geospatial Approach: A Case Study of Kullu District, Himachal Pradesh, India
用机器学习与遥感分析印度库鲁区1991-2022年土地利用变化及其对地表温度的影响。
Integrated Assessment of Soil Erosion Drivers Using RUSLE, Remote Sensing, and Scenario-Based Machine Learning in a Data-Scarce Watershed of the Chota Nagpur Plateau, Eastern India
结合RUSLE、遥感与情景机器学习，评估印度Chota Nagpur高原缺数据流域的土壤侵蚀驱动因素
Yield ranking of spring wheat breeding lines absent from model training within two contrasting seasons in northern Kazakhstan
评估春小麦育种品系产量模型在未参与训练品系上的跨季预测能力。
Machine Learning for Surface Water Quality Management in Malaysia: Integrating Physicochemical Parameters, Nutrient Loads, Land Use, Hydrological Conditions, and Anthropogenic Pressure
构建马来西亚地表水质机器学习框架，融合多源环境数据实现分类、预测与污染事件检测。
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。
Zenodo (CERN European Organization for Nuclear Research)·9月22日 · 周二78 分 论文农业AI与大模型智慧农业 / 农业物联网2 个信源同时报道 阅读 2
A Systematic Study of Supervised and Ensemble Learning Approaches for Crop Selection in Smart Agriculture
系统综述2016-2026年24项作物推荐研究，比较四类机器学习方法并指出基准精度已趋饱和。
Machine learning models combined with feature importance methods for honey yield classification: A replicable approach
构建可复现工作流，用气候变量与机器学习分类蜂蜜产量。
Crop recommendation in precision agriculture: a systematic literature review of methods, trends, and challenges（精准农业中的作物推荐：方法、趋势与挑战系统综述）
系统综述129篇2020-2026年文献，梳理精准农业作物推荐的方法、趋势与挑战。
YieldVision: Model-Driven Decision Support System for Crop Yield Prediction
提出YieldVision决策支持系统，用RF、LSTM及混合模型预测作物产量并部署应用。
Geostatistics and Spatial Modelling in Soil Science: A Critical Appraisal of Methods, Advantages, Inconveniences and Applications
综述地统计学与混合空间建模在土壤科学中的方法、优势、局限及应用，指出其与机器学习融合是未来方向。
Temporal trend analysis and multi-temporal satellite feature integration for mango orchard acreage estimation using machine learning algorithms approach
融合多时相Sentinel-1/2与机器学习，估算印度芒果园面积并分析23年种植趋势。
International Journal for Research in Applied Science and Engineering Technology·9月21日 · 周一63 分 论文智慧农业 / 农业物联网阅读 4
Determination of Water Stress in Okra Plants Using Canopy Temperature Measurement by Infrared Thermometer
用红外测温仪测秋葵冠层温度并结合随机森林，实现水分胁迫的早期无损分类。
International Journal of Science Engineering and Technology·9月21日 · 周一70 分 论文农业AI与大模型智慧农业 / 农业物联网2 个信源同时报道 阅读 3
Influence of Sound Frequencies on Plant Growth and Physiological Development
构建声频刺激植物生长实验框架，分析不同频率对生理指标的影响并推荐最优频率。
Non-destructive nitrogen estimation in pastures from UAV multispectral imagery using a heterogeneity-driven machine-learning framework
Non-destructive nitrogen estimation in pastures from UAV multispectral imagery using a heterogeneity-driven machine-learning framework。Precision Agriculture
Research on the Inversion Method of Total Phosphorus Concentration in Water Bodies Based on PSO-Adam-BP Neural Network Model
用PSO-Adam-BP神经网络结合Sentinel-2影像反演白洋淀总磷浓度。