International Journal of Computational Intelligence Systems·10月22日 · 周四84 分 论文农业AI与大模型农业人工智能与决策模型阅读 0
ExplainableCropAI: Interpretable Deep Learning for Agricultural Disease Diagnosis and Decision Support
构建可解释水稻病害诊断系统，融合归因、不确定性与自然语言解释。
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Machine Learning for Drought Analysis and Forecasting: A Critical Review of Data Fusion, Extreme-Event Sensitivity, Explainability and Generalization
综述55篇文献，比较机器学习干旱监测预报在定义、数据与验证上的差异。
International Journal for Research in Applied Science and Engineering Technology·10月16日 · 周五0 分 论文智慧农业 / 农业物联网阅读 1
XAI and its Applications in Decision Support System
综述可解释AI技术及其在决策支持系统中的应用、挑战与未来方向。
Explainable deep learning for Mango leaf disease recognition using AlexNet and grad-CAM
提出可解释深度学习框架，用AlexNet结合贝叶斯调参、微调和Grad-CAM实现芒果叶病十分类。
Trustworthy Explainable AI for Lettuce Growth Stage Prediction Using Sensor Data in Smart Agriculture
提出XGB-StageVision，用传感器数据与可解释AI预测水培生菜生长阶段。
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。
PEAS-DeiT: Multi-Scale Spatial Attention Enhanced DeiT for Pea Leaf Disease Classification
提出PEAS-DeiT模型，将多尺度空间注意力融入DeiT，实现豌豆叶片病害分类。
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。
Explainable deep learning framework for real-time insect pest detection in smart agriculture
提出可解释深度学习框架CPSA-RIPD-CSTGCN，实现智慧农业中害虫实时检测。
Comparative Deep Learning and RISE-Based Explainable AI Framework for Orange Fruit Disease Detection
比较三种深度学习模型检测橙子病害，并用RISE提升可解释性。
A global-local hybrid vision-language framework for interpretable grapevine disease diagnosis
提出全局-局部混合视觉语言框架，实现可解释的葡萄病害诊断，准确率达98.7%。
Afyon Kocatepe University Journal of Sciences and Engineering·10月6日 · 周二61 分 论文农业AI与大模型农业人工智能与决策模型阅读 1
Exploratory Explainable Machine Learning for Crop-Yield Interpretation: A Comparative Analysis of XGBoost, LightGBM, and CatBoost
比较XGBoost、LightGBM和CatBoost在作物产量可解释建模中的表现，强调评估协议与数
GreenAI: An Explainable AI and IoT-Enabled Offline Framework for Precision Agriculture
GreenAI: An Explainable AI and IoT-Enabled Offline Framework for Precision Agriculture。Research Square
Multi-Scale Texture-Attention Network for Automated Arecanut Disease Classification Using Explainable Deep Learning
提出MSTANet多尺度纹理注意力网络，实现槟榔九类病害自动分类。
A Satellite-Based Framework for Flood Mapping: Enhancing Explainability Through Multispectral Indices and SHAP Analysis
构建SHAP增强的卫星洪水制图框架，用多光谱与地形变量训练LightGBM实现高精度分类。
A Hybrid Model for Crop Yield Prediction Using Recurrent Neural Networks and Explainable Artificial Intelligence
用LSTM结合可解释AI预测肯尼亚玉米产量，兼顾精度与透明度。
An Automata-Driven Cognitive Explainable Artificial Intelligence Framework for Climate-Adaptive Precision Agriculture and Environmental Sustainability
An Automata-Driven Cognitive Explainable Artificial Intelligence Framework for Climate-Adaptive Precision Agriculture and Environmental Sustainability。Cureus Journal of Computer Science.
A Study of Explainable AI In Smart Agriculture
探讨可解释人工智能在智慧农业中的应用与挑战。
A PCA-based deep feature optimization framework for explainable orange fruit disease classification
提出PCA深度特征优化框架，用ViT+SVM分类橙子病害，准确率达99.12%。
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。