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%。
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数字农业与前沿技术 · 细分方向
农业AI与大模型
人工智能算法、大模型与决策支持系统
670 篇内容
切换同领域方向
Brazilian Insect Survey: A Platform for Pest Management
巴西昆虫调查平台整合计算机视觉、田间试验与种群建模，实现害虫实时监测与治理。
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One Toolchain, Six Domains: A Multiple-Case, Document-Based Study of Rapid IoT Prototypes Built in a One-Week Immersive Course on a Master’s Program in Applied Artificial Intelligence
基于六组一周IoT课程原型文档，分析其架构、AI集成与安全模式。
2 个信源阅读 4
AI and Sustainable Agriculture Through Cost–Benefit Analysis of Smart Irrigation Systems
通过成本效益分析评估AI智能灌溉系统的经济与环境影响。
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Integrating artificial intelligence in climate change and sustainable development: A comprehensive bibliometric review
用文献计量法梳理2007-2025年AI与气候变化及可持续发展交叉研究，揭示主题演化与地域失衡。
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Towards intelligent and scalable routing in cloud-based smart agriculture: a hybrid deep learning approach in big data era
提出AlexNet与蚁群优化混合路由框架，用于云智慧农业的智能路由与资源分配。
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The Confluence of AI and Eco-Innovation: Shaping the Future of Multidisciplinary Research
综述AI与生态创新融合的多学科研究，提出四层分析框架并指出未来方向。
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Research on the Application of Agricultural Big Data in Plant Growth Prediction——基于多源数据同化与混合智能（MDA-HI）框架
提出MDA-HI框架，融合过程模型与混合智能，用于作物生长与产量预测。
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Computer vision-based size estimation module for selective harvesting of lettuce
开发基于RGB图像处理的生菜尺寸估计软件，用于选择性采收决策。
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Design and Implementation of an Ensemble Learning Based Decision Support Model for Crop Selection in Precision Agriculture
提出SCR-XGB五层框架，用梯度提升树集成从土壤和气候参数推荐作物。
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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。
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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。
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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。
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Identifying drought-responsive candidate genes in root and shoot tissues of wheat (Triticum aestivum L.) through RNA sequencing, cluster analysis, weighted gene co-expression network analysis, and machine learning models
通过RNA-seq、WGCNA和XGBoost-SHAP分析，鉴定小麦根和茎中响应干旱的候选基因。
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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。
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Leakage-aware, calibrated, and explainable deep learning for robust almond disease classification
提出防泄漏、校准且可解释的深度学习框架，用于稳健的杏仁病害分类。
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设施番茄采摘机器人识别定位与采摘方法研究——江苏大学雷志龙等 基于改进YOLO v5-HSV融合算法识别准确率95.01%
设计设施番茄智能采摘平台，融合改进YOLO v5与HSV实现识别定位与采摘。
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Geospatial Artificial Intelligence in Precision Agriculture: A Systematic Review（精准农业中的地理空间人工智能：系统综述）
系统综述2019-2026年地理空间AI在精准农业的应用、多模态数据集成与决策支持。
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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。
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Geospatial and machine learning techniques for spatiotemporal analyses of urban dynamics in the Coimbatore city, South India
基于Landsat影像与机器学习分析印度哥印拜陀市2001-2023年城市扩张及其环境效应。
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