精准棉花病害管理：融合无人机影像与定制YOLO深度学习框架
提出轻量化检测器Cotton-Vision，用无人机影像精准识别棉花病害。
棉麻糖与特色经作
棉花、甘蔗、烟草、蚕桑等工业原料型作物
技术筛选 · 已选 1 项
21 篇内容清除全部筛选
A smart agriculture-oriented cross-attention multiscale CNN for sugarcane disease diagnosis
提出交叉注意力多尺度并行CNN，实现甘蔗叶病自动分类。
MSFANet: a multi-scale feature aggregation network for tobacco pest recognition
提出MSFANet多尺度特征聚合网络，实现复杂田间背景下烟草害虫的自动识别。
基于改进YOLO 11n模型的棉花田间复杂环境障碍物检测方法
提出改进YOLO 11n模型，实现棉花田间复杂遮挡环境下障碍物高精度检测。
Uncertainty-Aware Semi-Supervised Framework with Supervised Contrastive Learning for Sugarcane Leaf Disease Detection [预印本]
提出半监督不确定性感知网络U-SSDNet，用少量标注实现甘蔗叶病高精度检测。
IoT-Based Automatic Cotton Leaf Disease Detection, Infection Percentage Estimation, and Treatment Recommendation Using Raspberry PI
基于树莓派与物联网，实现棉花叶病自动检测、感染率估算与防治推荐。
多尺度异构特征协同的棉花叶片病害检测模型(Multi-Scale Heterogeneous Synergistic Model for Cotton Leaf Disease Detection)
提出MHSF-DETR棉花叶片病害检测模型，提升精度并降低参数量与计算量。
Severity Grading of Early Visible Symptoms of Sugarcane Pokkah Boeng Disease Based on Multi-Scale Dynamic Feature Fusion and Channel-Spatial Interaction: An On-Device Framework
提出轻量级框架，对甘蔗凤梨病早期可见症状进行四级严重度分级。
Cotton leaf disease classification using deep learning models for smart agriculture
Cotton leaf disease classification using deep learning models for smart agriculture。Agriculture & Food Security
Multi-features and Fuzzy with Residual Lenet for Cotton Classification Using Remote Sensing Image
提出多特征与模糊结合残差LeNet的遥感影像棉花分类方法。
A Hybrid CNN–Transformer–LSTM Deep Learning Framework for Automated Cotton Disease Detection
提出CNN-Transformer-LSTM混合框架，实现棉花叶片病害自动识别。
From family trials to genomic mate allocation: statistical and genomic strategies to accelerate sugarcane genetic improvement
综述甘蔗育种早期决策的统计与基因组策略，提出连接家系与无性系数据的决策框架。
Lightweight architecture optimization of YOLOv12n for improved cotton verticillium wilt detection
提出轻量模型YOLO-SCOD，实现棉花黄萎病精准检测。
Field-based deep learning classification of cotton and weeds for machine-vision-assisted intra-row weed management
用MobileNetV2和DenseNet169对田间棉花与杂草图像做二分类，验证机器视觉除草可行性
棉花所联合西部农业研究中心发布全球首个棉花领域专属大语言模型"棉芯1.0"
中国农业科学院棉花研究所联合西部农业研究中心8月下旬发布全球首个棉花领域专属知识大语言模型"CottonMind1.0",覆盖棉花种质资源、品种性状、栽培管理、加工利用等全产业链数据,标志着农业AI从通用模型向行业垂直深耕转向。
Crop Yield Estimation with MODIS Derived Normalized Difference Vegetation Index and Comparative Study on Crop Yield Prediction Among Linear Regression, Random Forest and Gradient Boosting as Well as CatBoost
用MODIS NDVI与四种回归模型预测印度各邦作物产量，并做全国尺度验证。
Decoding Electro-Priming responses in cotton using an integrated taguchi-machine learning framework for optimizing seed performance
用田口-机器学习框架优化棉花种子电引发处理，提升种子性能。
中国农大马韫韬教授团队 AI 驱动棉花高通量表型解析三项系列论文：覆盖花器官检测、单株表型、吐絮动态评估
中国农业大学土地科学与技术学院马韫韬教授团队围绕棉花关键生育时期的表型智能识别与量化分析，在 Computers and Electronics in Agriculture、Plant Phenomics 和 Precision Agriculture 等国际权威期刊上连续发表三篇研究论文，题目分别为 MS-R outperforms RGB: Lightweight CYOLO-BiCNet enables efficient detection and storage optimization of cotton flowers，Scalable phenotyping and yield estimation via stability index and single-plant variability using a vision-based large model framework 和 Assessing Cotton Boll-opening Concentration for Harvest Decision-making via Foundation Model-enhanced Cross-scale Phenotyping。
Changing pest dynamics and pesticide use in cotton: Challenges and sustainable management
综述棉花害虫动态与农药使用变化，提出可持续管理策略。
Restoration of occlusion in residual film images and curling degree prediction based on UAV in pre-sowing cotton fields
用无人机图像修复残膜遮挡并预测卷曲程度，评估棉田残膜污染。