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。
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农业人工智能 智慧农业 可解释AI
同时包含 农业人工智能、智慧农业、可解释AI 的农业资讯，共 28 条。
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。
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 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%。
Farmer-Friendly Decision Support System Using Explainable Orangutan Artificial Intelligence for Soil Fertility
Farmer-Friendly Decision Support System Using Explainable Orangutan Artificial Intelligence for Soil Fertility。SN Computer Science
A multi-agent multi-modal LLM with Monte Carlo tree search for interpretable plant disease classification
提出多智能体多模态LLM结合蒙特卡洛树搜索，实现可解释的植物病害分类。
Explainable optimized deep learning and generative AI based framework for finger millet disease detection in smart agriculture
提出融合深度学习、可解释AI与生成式AI的指状粟病害检测框架，实现高精度识别与决策支持。
From patterns to prediction: detecting crop sequence and interpreting crop selection with explainable AI at field level in Denmark
用丹麦十年田块数据识别轮作模式并预测年度作物选择。
DAC-Grad-CAM: Defect-aware explainable AI for automated mango quality classification
提出DAC-Grad-CAM可解释框架，实现芒果缺陷感知分类与定量验证。
AI-Driven Transformation in Sustainable Agriculture: A Systematic Review
系统综述AI在可持续农业中的应用，分析生产力与可持续性影响及推广障碍。
Artificial Intelligence-Based Intelligent Fertigation Recommendation Systems for Precision Agriculture
综述AI施肥推荐研究，并用马哈拉施特拉数据集比较七种分类器，提出可审计智能施肥推荐框架。
DASO-RiceNet: 细粒度水稻病害与损伤分类的双注意力序列优化网络(准确率 96.5%)
提出双注意力序列优化网络DASO-RiceNet，实现10类水稻病害与损伤的高精度分类。
Hybrid explainable DeiT-based framework for plant disease classification and severity estimation
提出基于DeiT的混合可解释框架，实现植物病害分类与严重度估计。
中国农科院资划所基于可解释AI构建农产品产地溯源模型 破解光谱判断产地的"黑箱"难题
中国农科院农业资源与农业区划研究所智慧农业创新团队基于可解释人工智能构建农产品产地溯源模型,以中国六个地区的猕猴桃为对象,融合光谱成像与多模型组合策略,并运用可解释性方法对最优组合模型进行分层和整体解读,在提升溯源准确率的同时揭示光谱数据判断产地的内在逻辑,为光谱技术在农业生产与品质监管中应用提供系统解决思路,成果发表于《农业人工智能》(Artificial Intelligence in Agriculture)。
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
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。
Explainable AI for Precision Agriculture: Fine-Grained Plant Pathology Localization Using ConvNeXt-Tiny and Residual Spatial Attention Module Palvi Sharma
提出结合ConvNeXt-Tiny与残差空间注意力模块的框架，实现多作物病害细粒度分类与可解释定位。
Agentic, Autonomous and Explainable AI in Smart Agriculture: A Systematic Literature Review（MDPI Agronomy 2026, 16(17), 1690）
系统综述2016-2026年智慧农业中代理式、自主与可解释AI研究，揭示可解释性主导、自主性不足及验