An adaptive deep learning framework for multi-temporal crop and drought stress monitoring in precision agriculture
提出CNN+ViT混合深度学习框架，利用多时相Sentinel-2影像监测作物分类与干旱胁迫。
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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Zenodo (CERN European Organization for Nuclear Research)·9月10日 · 周四76 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
AI in energy and water conservation
提出卡塔尔国家AI水智能平台NAWIP，整合多源数据实现水预测、优化与协调。
EdgeHatch: Detection of chicken hatching stages using machine learning on an edge device
提出EdgeHatch，在边缘设备上用机器学习检测鸡蛋孵化阶段。
Leakage-Proof Reliability-Aware Deep Learning Framework for Rice Leaf Disease Diagnosis
提出防泄漏、可靠性导向的水稻叶病诊断深度学习框架，并系统评估其泛化与部署表现。
Machine Learning-Based Prediction of Fall Armyworm (Spodoptera frugiperda) Outbreaks in Maize Production Systems of Northern Nigeria Using Climate and Remote Sensing Data
用气候与遥感数据构建机器学习模型预测尼日利亚北部玉米草地贪夜蛾暴发。
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。
The use of artificial intelligence in agriculture: Global practice
综述2022-2026年AI在农业的应用趋势、成效与各国推广障碍。
AI-Powered Crop Protection: Transforming Pest Surveillance and Decision-Making Through Digital Agriculture
综述AI与数字农业在害虫监测、预测及决策支持中的应用与挑战。
An ensemble deep learning approach for soil pH classification leveraging GLCM-based texture features and neural networks
提出融合GLCM纹理特征与CNN的集成深度学习框架，实现土壤pH等级图像分类。
An Adaptive AI-DSS Framework for Real-Time Image Analysis and Decision-Making in Precision Agriculture
提出自适应AI决策支持系统，用轻量网络与特征优化实现精准农业实时图像分析与决策。
Trustworthy AutoML indicators for climate-smart agriculture: A sustainability-governance framework for decision support
提出面向气候智慧型农业的可信AutoML指标体系与可持续治理框架，用于决策支持。
Smart crop recommendation: fusing nutrient and climate data with Krill Herd Optimization and explainable AI
提出融合养分与气候数据、KHO优化和可解释AI的作物推荐框架，Tab Transformer精度达0
Decision support in recirculating aquaculture systems (RAS): A case study of a human–AI interface in prawn hatchery operation
用大语言模型辅助人类专家，解决罗氏沼虾RAS育苗中幼体高死亡率问题。
Two Decades of Computer Vision in Precision Livestock Farming: an Umbrella Review of Applications, Benchmarking Evidence, and the Performance-to-Deployment Gap Across Cattle, Pig, and Poultry Systems
伞式综述112篇综述，梳理牛猪禽精准养殖计算机视觉应用与部署差距。
Mitigating artificial intelligence-related animal welfare risks in animal farming
探讨如何缓解动物养殖中人工智能应用带来的动物福利风险。
KRISHI.AI: an explainable machine learning framework for data-driven crop recommendation
提出可解释AI框架KRISHI.AI，基于土壤和气候数据推荐作物，并解释推荐原因。
An Interpretable BO-TCBDA Deep Learning Framework for Winter Wheat Yield Estimation Using Multi-Source Remote Sensing Data
提出BO-TCBDA深度学习框架，融合多源遥感数据估算冬小麦产量，兼具高精度与可解释性。
Hydrochemistry and modeling nitrate concentration in farmland groundwater under different hydrological seasons by integrating hybrid quantum-classical ML, virtual sample generation and AlphaEarth Foundation
提出集成混合量子-经典机器学习、虚拟样本生成和遥感基础模型的框架，预测雄安农田地下水硝酸盐浓度。
Green Artificial Intelligence Framework for EnergyEfficient Smart Agriculture in Tropical Environments
提出绿色AI框架，提升热带智慧农业能效与资源利用。
Advances in artificial intelligence and geomatics technology for the transformation of global agri-food systems: A review
综述AI与地理空间技术在全球农业食品系统转型中的应用，分析2004-2025年文献，识别可持续农业实