Climate policy uncertainty and domestic–international grain-market risk spillovers: a TVP-VAR-BK analysis with machine learning early warning
用TVP-VAR-BK与机器学习分析气候政策不确定性对中外粮食市场风险溢出的动态影响。
农业AI与大模型
人工智能算法、大模型与决策支持系统
技术筛选
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From Handcrafted Descriptors to Deep Embeddings: A Comparative Study for Regional Hazelnut Image Classification
比较手工特征、深度迁移学习和量子兼容分类器对三个地区榛子图像的分类性能。
Hybrid Deep Feature Extraction and Ensemble Machine Learning for Potato Leaf Disease Classification in Uncontrolled Field Conditions
融合三种CNN深度特征与集成学习，实现田间马铃薯叶片七类病害分类。
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。
Enhanced crop yield estimation in smart agriculture via AI and IoT using the GODL geo-optimized algorithm
提出GODL算法，融合地理空间信息与深度学习，结合AI和IoT提升作物产量预测精度。
Early Detection of Apple Scab in Orchards and Mildew in Vineyards Using A Novel Deep Learning Model and Multispectral Imagery
提出一种基于无人机多光谱时序影像的深度学习混合模型，实现苹果黑星病和葡萄霉病的早期检测与分割。
A study on the identification of wheat powdery mildew using chlorophyll fluorescence imaging
该研究利用叶绿素荧光成像与深度学习识别小麦白粉病并分级。
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 Privacy-Preserving Federated Learning Framework for Paddy Leaf Disease Detection Using an Enhanced Pretrained Novel EfficientNet-Lite4 Model
提出联邦学习结合改进EfficientNet-Lite4的水稻叶片病害检测框架，兼顾隐私与精度。
JOURNAL OF BAKU ENGINEERING UNIVERSITY- MATHEMATICS AND COMPUTER SCIENCE·10月12日 · 周一72 分 论文水资源与灌溉智慧农业 / 农业物联网阅读 0
CROPINTEL DATASET: A COMPREHENSIVE AGROENVIRONMENTAL RESOURCE FOR CROP CLASSIFICATION, IRRIGATION PLANNING, AND YIELD ANALYSIS
基于CropIntel数据集，用15+种机器学习算法实现作物分类、灌溉需求预测与产量潜力预测。
Adaptability of Lightweight AI Models for Embedded Deployment: A Case Study of an Agricultural Monitoring Robot
综述轻量化AI方法，并以农业监测机器人为例，提出相机+轻量YOLO+Jetson+单片机的嵌入式部署
Machine Learning-based Prediction of SVI Parameter in Maize: A Comparative Study and Feature Importance Analysis
用四种机器学习模型基于39个玉米基因型盐胁迫发芽数据预测幼苗活力指数SVI。
A global–local momentum contrastive framework with critical feature calibration for field plant disease identification in precision agriculture
提出GLC-Net，用全局-局部动量对比学习与关键特征校准提升田间植物病害识别。
An empirical performance comparison of deep learning models for apple leaf disease recognition
用UNet++分割苹果病叶并融合多种CNN与ViT分类，实现98.85%识别准确率。
International Journal of Electronics and Communication Engineering·10月10日 · 周六63 分 论文农业AI与大模型智慧农业 / 农业物联网阅读 1
Image Processing-Based Efficient Hybrid Feature Extraction Approach for Plant Leaf Disease Recognition
提出融合CNN深度特征与手工特征的方法，用于植物叶片病害识别。
Hybrid CNN-LSTM Techniques for Plant Leaf Disease Detection and Farmer-Based Agricultural Practice Analysis in Western Tamil Nadu
结合CNN-LSTM混合模型检测植物叶片病害，并调查印度泰米尔纳德邦农户的农业实践与技术采纳情况。
干旱区农技社会化服务试点政策对县域粮食生产的激励效应——基于双重机器学习因果推断
评估干旱区农技社会化服务试点政策对县域粮食产量的因果效应。
AgroNLP-SimAssist: a multilingual transformer-based decision support simulator for crop disease diagnosis, advisory generation, and market intelligence
提出多语言Transformer农业决策支持模拟器，统一实现作物病害诊断、农技建议与市场情报。
Scalable, cloud-native decision support system for Variable Rate Application (VRA) based on multi-year NDRE time series in arable precision agriculture
构建云原生决策支持系统，用多年NDRE时序自动生成精准农业变量施肥/播种处方图。
MangoVQA: A Multi-Answer Visual Question Answering System for Grading SNI-Aligned Mango Quality using Deep Learning and Text Processing
提出首个多答案芒果质量分级VQA框架MangoVQA，对齐印尼SNI标准。