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
30 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。
Cotton pest and disease multi-class classification with weather and temporal predictors features across Indian agroclimatic zones
用农业气象变量与时间特征构建机器学习模型，实现印度棉区棉铃虫多级发生预测。
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。
Beyond single-element toxicity: chromium-salinity interactions, food-chain transfer and mitigation in sugarcane agroecosystems
综述甘蔗农业生态系统中铬与盐分交互作用、食物链传递及缓解措施，指出单元素毒性评估不足。
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。
Breeding innovations focused on drought tolerance, and Nitrogen and phosphorus uptake, for sustainable and climate-resilient sugarcane
综述甘蔗耐旱及氮磷高效利用的育种创新与可持续养分管理策略。
Nutrient cycling, environmental outcomes and mitigation pathways in sugarcane agroecosystems: A comparative synthesis of Brazil and Australia
综述比较巴西与澳大利亚甘蔗农业生态系统的养分循环、环境效应及减排路径。
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。
Изследване на приложимостта на вегетационни индекси за диагностика на азотния статус при тютюн
评估七种植被指数诊断两种烟草氮素状况的适用性并建立回归模型。
Forecasting Cotton Productivity in the Mil-Mugan Economic Region using Climate Variables and Remote Sensing Data
结合Sentinel-2 NDVI与气候变量，构建多元模型预测米尔-穆甘地区棉花产量。
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。
Detecting diurnal dynamics of cotton leaf inclination angle under water-salt stress
提出CAPLA无人机点云融合方法，监测水盐胁迫下棉花叶倾角昼夜动态。
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。