Forecasting cocoa export volumes in Indonesia using remote sensing, climate variability, and economic indicators: A comparative modeling approach
融合遥感、气候与经济指标，比较多种模型预测印尼可可出口量，XGBoost表现最佳。
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#机器学习
International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering·9月18日 · 周五72 分 论文水资源与灌溉智慧农业 / 农业物联网阅读 2
An artificial intelligence-based stacking ensemble framework for smart irrigation pump control using IoT sensor data
提出基于堆叠集成学习的灌溉泵ON/OFF预测框架，用物联网传感器数据实现智能灌溉控制。
Spatial and Temporal characteristics and driving force analysis of vegetation cover change in Shanxi Province, China based on kNDVI and XGBoost-SHAP model
基于kNDVI与XGBoost-SHAP分析山西2000-2024年植被覆盖时空变化及驱动机制。
From surveillance to intelligence: a scoping review of machine learning for antimicrobial resistance surveillance intelligence across One Health
综述机器学习在One Health抗微生物耐药监测情报中的应用与成熟度。
UAV无人机高光谱图像土壤盐度制图(湿度校正)——MDPI Agronomy
用无人机高光谱结合Stacking集成模型实现湿度干扰下的土壤盐度制图。
A Novel Data-Driven Framework for Stubble Burning Detection Using NASA FIRMS and Machine Learning
提出基于NASA FIRMS与机器学习的数据驱动框架，用于检测秸秆焚烧。
Volatile Fingerprinting Empowers Salinity Monitoring in Peppermint Using MOS Sensors and Feature-Optimized Machine Learning
用低成本MOS传感器阵列采集薄荷VOC指纹，结合特征优化机器学习实现盐胁迫检测。
Advanced olive leaf area prediction using machine learning methods
用机器学习基于叶长宽非破坏性预测30个橄榄品种叶面积，PSO优化ANN精度最高。
Integrating multi-source data and support vector machine to assess the spatio-temporal pattern of land degradation in the Eastern Cape of South Africa
用Landsat与SVM评估南非Keiskamma流域2005-2025年土地退化时空格局。
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。
Annual Gridded Anthropogenic CH4 Emissions Estimation in China (2019–2025) Integrating Multisource Data: SHAP-Based Driver Attribution and Spatio-Temporal Patterns
融合多源数据与机器学习构建中国2019-2025年0.1°人为甲烷排放网格，并用SHAP解析驱动机制
Application of Remote Sensing and Machine Learning in Sustainable Agriculture
综述遥感与机器学习在可持续农业中的应用现状与前景。
Retrieval of Optically Active and Inactive Water Quality Parameters Using Remote Sensing and Machine Learning: Evidence from Water Hyacinth-Infested Lake Tana, Ethiopia
用Sentinel-2与机器学习反演埃塞俄比亚塔纳湖四类水质参数并分析时空变化。
INTERNATIONAL JOURNAL OF HUMANITIES AND LEARNING TECHNOLOGY INNOVATION (IJHLT)·9月15日 · 周二72 分 论文农业AI与大模型智慧农业 / 农业物联网阅读 2
ARTIFICIAL INTELLIGENCE IN HIGHER EDUCATION: TRANSFORMING TEACHING, LEARNING, AND STUDENT ENGAGEMENT
提出融合物联网传感与随机森林的智能灌溉系统，实现数据驱动的自动灌溉控制。
Effects of Noise on the Accuracy of Estimating Rootzone Total Soil Moisture Using a Non‐Linear Autoregressive Exogenous Machine Learning Model for Precision Agriculture
该论文评估了测量噪声对NARX机器学习模型估算根区土壤水分动态精度的影响。
International Journal of Computer Science and Artificial Intelligence·9月14日 · 周一0 分 论文农业AI与大模型农业人工智能与决策模型阅读 3
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。
Mapping Native Grass Cover with Random Forest Models: Sentinel-2 Versus Sentinel-2 Combined with Sentinel-1 SAR-Derived GLCM Texture Metrics
用随机森林结合Sentinel-2与Sentinel-1纹理特征，绘制澳洲原生草地C3/C4草覆盖度
Mining Externalities and Climate-Smart Agriculture Portfolios: Evidence from Mining-Affected Communities in Ghana
研究加纳矿区农户气候智慧型农业组合的广度与构成，发现采矿压力与CSA组合无统一关系。
Mapping urban expansion and land transformation in Dhaka, Bangladesh by fusing night-time lights, thermal, and spectral data via machine learning approaches
融合夜间灯光、热红外与光谱数据，用机器学习精准监测达卡2016-2024年城市扩张与土地转化。
Machine Learning-Based Prediction of Soil Moisture in Sikkim's High-Rainfall Zones Using Multimodal Remote Sensing Data
用多模态遥感与机器学习预测印度锡金高降雨区表层土壤水分。