[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4965":3,"related-4965":75},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":60,"slug":63,"view_count":21,"doi":8,"paper":64,"created_at":74},4965,"Uncertainty-Aware Semi-Supervised Framework with Supervised Contrastive Learning for Sugarcane Leaf Disease Detection [预印本]","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.17996","Shalini Dangi等提出基于Mean-Teacher半监督学习的不确定性感知病害检测网络U-SSDNet,用于甘蔗叶病检测。论文针对(i)同一病害在不同生育期表现差异大,(ii)病健组织类间相似,(iii)标签噪声等问题,先以离散小波变换增强病斑并降低类内差异,再用KL散度优化伪标签减轻确认偏差,同时引入监督对比学习提升类间区分度。甘蔗叶病数据集上的实验显示,U-SSDNet仅需少量标注即可接近全监督模型精度,显著优于当前主流监督与半监督方法。",null,"arXiv 2610.17996 2026 年 10 月 13 日","2026-10-13T00:00:00Z","论文",10,false,69,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},12,20,17,11,9,1,"预印本提出不确定性感知半监督框架，方法新颖且少标注下接近全监督精度，对作物病害智能识别有参考价值，但尚属实验室阶段成果。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","甘蔗","病害检测","半监督学习",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[],{"status":39,"locked":13,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":45},[44,50,55],{"code":32,"score":16,"supported":45,"title":46,"abstract":48,"tags":49},true,[47],"sugarcane",[28],[28],{"code":51,"score":21,"supported":13,"title":52,"abstract":53,"tags":54},"I1",[],[],[26],{"code":56,"score":21,"supported":13,"title":57,"abstract":58,"tags":59},"I2",[],[],[27],[61,62],"甘蔗叶病 检测 半监督学习","U-SSDNet 甘蔗","甘蔗叶病检测半监督学习-4965",{"doi":8,"openalex_id":8,"authors":65,"venue":8,"cited_by_count":66,"oa_url":8,"card":67,"direction":71,"ingested_from":73},[],0,{"tldr":68,"method":69,"finding":70,"direction":71,"opportunity":72},"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。","Mean-Teacher半监督、离散小波变换、KL散度伪标签优化、监督对比学习。","仅需少量标注即接近全监督精度，显著优于主流监督与半监督方法。","农业人工智能与决策模型","可将该不确定性半监督框架迁移至其他作物病害，并探索田间复杂光照与多生育期泛化。","agent","2026-10-15T00:06:08.498104Z",{"total":76,"page":21,"page_size":76,"items":77},6,[78,136,196,273,325,406],{"id":79,"title":80,"url":81,"summary":82,"summary_zh":83,"content":8,"source_name":84,"source_url":81,"published_at":85,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":86,"sources":89,"tags":91,"primary_topic":94,"secondary_topics":95,"classification":96,"search_phrases":114,"slug":117,"view_count":21,"doi":118,"paper":119,"created_at":135},5234,"A smart agriculture-oriented cross-attention multiscale CNN for sugarcane disease diagnosis","https:\u002F\u002Fdoi.org\u002F10.1080\u002F23080477.2026.2749739","Sugarcane leaf diseases can adversely affect crop health and yield, making timely disease identification important for smart agriculture. Manual inspection of leaf diseases is time-consuming and requires expert knowledge, particularly when large cultivation areas need to be monitored. To tackle this problem, this study proposes a Cross-Attention Multiscale Parallel Convolutional Neural Network for automatic classification of sugarcane leaf diseases. This framework comprises a Parallel Feature Extraction Stem, cross-attention mechanism, Dense Connection Blocks, and Inception Blocks to learn disease features at various spatial scales. The PFES extracts fine and broad disease patterns using parallel convolutional branches, while the cross-attention mechanism refines the extracted features by emphasizing disease-related information. The DCB supports feature propagation and reuse, and the IB further captures multi-scale feature representations. The proposed model was tested on two benchmark datasets of leaf diseases for sugarcane plants. The proposed framework obtained an accuracy (98.79%), precision (98.31%), recall (97.81%), specificity (98.60%), and F1-Score (98.05%). For assessing the classification accuracy and consistency of the framework, the comparative study, ablation studies, and five-fold cross-validation are carried out. The results show that CA-MPCNN provides accurate sugarcane leaf disease classification and can support automated disease diagnosis in smart agriculture applications.","甘蔗叶片病害会对作物健康与产量造成不利影响，因此及时识别病害对智慧农业具有重要意义。人工检查叶片病害耗时且需要专业知识，尤其是在需要监测大面积种植区域时。为解决该问题，本研究提出一种用于甘蔗叶片病害自动分类的交叉注意力多尺度并行卷积神经网络（Cross-Attention Multiscale Parallel Convolutional Neural Network）。该框架由并行特征提取主干（Parallel Feature Extraction Stem）、交叉注意力机制、密集连接块（Dense Connection Blocks）和Inception块组成，用于学习不同空间尺度上的病害特征。PFES通过并行卷积分支提取细粒度与广域病害模式，而交叉注意力机制通过强调病害相关信息对提取的特征进行细化。DCB支持特征传播与复用，IB进一步捕获多尺度特征表示。所提出的模型在两个甘蔗植株叶片病害基准数据集上进行了测试。该框架取得了准确率（98.79%）、精确率（98.31%）、召回率（97.81%）、特异性（98.60%）和F1分数（98.05%）。为评估该框架的分类准确性和一致性，开展了对比研究、消融研究和五折交叉验证。结果表明，CA-MPCNN能够实现准确的甘蔗叶片病害分类，并可支持智慧农业应用中的自动化病害诊断。","Smart Science","2026-10-17T00:00:00Z",{"impact":16,"substance":17,"depth":87,"authority":16,"freshness":20,"relevant":21,"comment":88},16,"该研究提出一种面向甘蔗叶部病害自动分类的交叉注意力多尺度CNN，在两个基准数据集上准确率达98.79%，方法新颖、实验充分，但属于细分作物应用研究，产业影响有限，时效性强，值得进入每日精选。",[90],{"name":84,"url":81},[26,27,28,92,93],"深度学习","病害诊断",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":97},{"candidates":98,"agricultural_evidence":45},[99,104,109],{"code":32,"score":100,"supported":45,"title":101,"abstract":102,"tags":103},15,[47],[28,47],[28],{"code":51,"score":100,"supported":45,"title":105,"abstract":107,"tags":108},[106],"smart agriculture",[26,106],[26],{"code":56,"score":110,"supported":13,"title":111,"abstract":112,"tags":113},2,[],[],[27,92],[115,116],"甘蔗 叶部病害 智能诊断","CA-MPCNN 甘蔗 病害","甘蔗叶部病害智能诊断-5234","10.1080\u002F23080477.2026.2749739",{"doi":118,"openalex_id":120,"authors":121,"venue":84,"cited_by_count":66,"oa_url":8,"card":128,"direction":133,"ingested_from":134},"W7222832245",[122,124,126],{"name":123,"orcid":8},"Bala Subramanian Chokkalingam",{"name":125,"orcid":8},"Uvarani Meenrajan",{"name":127,"orcid":8},"Shanmugapriya Sankaran",{"tldr":129,"method":130,"finding":131,"direction":71,"opportunity":132},"提出交叉注意力多尺度并行CNN，实现甘蔗叶病自动分类。","并行卷积分支、交叉注意力、密集连接与Inception块，两个基准数据集验证。","模型准确率达98.79%，消融与五折交叉验证证明其有效稳定。","可探索田间复杂背景与轻量化部署，并扩展到多作物病害实时诊断。","智慧农业 \u002F 农业物联网","openalex","2026-10-18T23:30:07.777036Z",{"id":137,"title":138,"url":139,"summary":140,"summary_zh":8,"content":8,"source_name":141,"source_url":8,"published_at":142,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":143,"sources":146,"tags":148,"primary_topic":152,"secondary_topics":153,"classification":159,"search_phrases":183,"slug":186,"view_count":66,"doi":8,"paper":187,"created_at":195},5532,"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","https:\u002F\u002Farxiv.org\u002Fabs\u002F2610.24182","研究提出单阶段全卷积检测器Cotton-Vision，融合幽灵瓶颈通道注意力与跨阶段部分融合三项轻量化设计，参数减少20%。在巴基斯坦旁遮普省7个棉田采集的2700余幅高分辨率无人机影像上测试，精确率97.7%、召回率77.7%、mAP@50为85.6%，较主流基线提升10至20个百分点。预印本，仅在巴基斯坦单一区域验证。","arXiv 预印本 2026-10-16","2026-10-16T00:00:00Z",{"impact":87,"substance":144,"depth":18,"authority":20,"freshness":76,"relevant":21,"comment":145},21,"方法轻量化与实测数据有增量，但仅单区域预印本验证，属细分技术进展，可入精选备选。",[147],{"name":141,"url":139},[26,27,149,150,151],"目标检测","无人机遥感","棉花病害",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[154],{"code":56,"slug":155,"name":156,"path":157,"parent_code":158},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":39,"locked":13,"version":40,"method":41,"evidence":160},{"candidates":161,"agricultural_evidence":45},[162,168,174,178],{"code":56,"score":163,"supported":45,"title":164,"abstract":166,"tags":167},18,[92,165],"YOLO",[],[27,149],{"code":32,"score":19,"supported":45,"title":169,"abstract":171,"tags":173},[170],"棉花",[172],"cotton",[],{"code":51,"score":21,"supported":13,"title":175,"abstract":176,"tags":177},[],[],[26],{"code":179,"score":21,"supported":13,"title":180,"abstract":181,"tags":182},"I3",[],[],[150],[184,185],"Cotton-Vision 棉花病害","无人机影像 棉花 检测","Cotton-Vision棉花病害-5532",{"doi":8,"openalex_id":8,"authors":188,"venue":8,"cited_by_count":66,"oa_url":8,"card":189,"direction":193,"ingested_from":73},[],{"tldr":190,"method":191,"finding":192,"direction":193,"opportunity":194},"提出轻量化检测器Cotton-Vision，用无人机影像精准识别棉花病害。","基于YOLO改进，融合幽灵瓶颈通道注意力与跨阶段部分融合，用2700余幅无人机影","精确率97.7%、mAP@50达85.6%，较主流基线提升10至20个百分点。","农业遥感与作物表型","仅在巴基斯坦单一区域验证，可扩展至多区域多品种，并探索边缘设备实时部署。","2026-10-22T00:07:09.816790Z",{"id":197,"title":198,"url":199,"summary":200,"summary_zh":201,"content":8,"source_name":202,"source_url":199,"published_at":142,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":203,"score_detail":204,"sources":207,"tags":209,"primary_topic":212,"secondary_topics":213,"classification":215,"search_phrases":233,"slug":236,"view_count":21,"doi":237,"paper":238,"created_at":272},5150,"MSFANet: a multi-scale feature aggregation network for tobacco pest recognition","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1871587","Introduction Tobacco is a globally significant economic crop, and pest infestation represents one of the primary factors contributing to declines in both yield and quality. However, tobacco pests encompass a wide variety of species that differ substantially in morphology and scale. Field-captured images are frequently subject to complex background interference and class imbalance, presenting considerable challenges for automated recognition. Existing deep learning approaches remain limited in their capacity for multi-scale feature extraction and discriminative feature representation under such conditions. To address these challenges, this study proposes a Multi-Scale Feature Aggregation Network (MSFANet) for tobacco pest recognition, formulated here as a single-label image classification task. Methods To address scale variation and complex field backgrounds, two task-oriented modules were developed. The Multi-Scale Pyramid Pooling (MSPP) module aggregates contextual information across multiple receptive fields through parallel feature extraction and adaptive cross-branch fusion. The Hierarchical Feature Fusion Block (HFFB) subsequently refines the feature representations through spatial gating and partial convolution. An auxiliary classification head and Focal Loss were incorporated during training to provide intermediate supervision and address class imbalance, respectively. Results The effectiveness of MSFANet was evaluated on a dataset comprising 13 tobacco-associated pest and natural-enemy categories. The model achieved accuracy, precision, recall, and F1-score of 97.63%, 97.89%, 97.59%, and 97.71%, respectively, with a parameter count of 1.21 M. Among the models trained from random initialization, MSFANet shared the highest accuracy while using the fewest parameters. Discussion By combining multi-scale contextual aggregation with hierarchical feature refinement, MSFANet achieved accurate classification across the 13 tobacco-associated categories under complex field conditions. The proposed method provides effective technical support for automated and precision-oriented pest monitoring in tobacco fields, offering practical value for advancing precise pest management and the broader development of smart agriculture.","引言 烟草是全球重要的经济作物，虫害侵染是导致其产量和品质下降的主要因素之一。然而，烟草害虫种类繁多，形态和尺度差异显著。田间采集的图像常受到复杂背景干扰和类别不平衡的影响，给自动化识别带来了相当大的挑战。现有深度学习方法在此类条件下进行多尺度特征提取和判别性特征表示的能力仍然有限。为应对这些挑战，本研究提出了一种用于烟草害虫识别的多尺度特征聚合网络（Multi-Scale Feature Aggregation Network，MSFANet），并将其形式化为单标签图像分类任务。方法 针对尺度变化和复杂田间背景，开发了两个面向任务的模块。多尺度金字塔池化（Multi-Scale Pyramid Pooling，MSPP）模块通过并行特征提取和自适应跨分支融合，聚合多个感受野的上下文信息。分层特征融合模块（Hierarchical Feature Fusion Block，HFFB）随后通过空间门控和部分卷积对特征表示进行细化。训练过程中引入辅助分类头和Focal Loss，分别提供中间监督和处理类别不平衡。结果 在包含13个烟草相关害虫及天敌类别的数据集上评估了MSFANet的有效性。模型的准确率、精确率、召回率和F1分数分别为97.63%、97.89%、97.59%和97.71%，参数量为1.21 M。在随机初始化训练的模型中，MSFANet以最少的参数量取得了最高准确率。讨论 通过结合多尺度上下文聚合与分层特征细化，MSFANet在复杂田间条件下实现了对13个烟草相关类别的准确分类。所提方法为烟草田间害虫的自动化和精准监测提供了有效的技术支持，对推进精准害虫管理和智慧农业的更广泛发展具有实用价值。","Frontiers in Plant Science",76,{"impact":87,"substance":144,"depth":18,"authority":205,"freshness":20,"relevant":21,"comment":206},13,"该论文提出轻量化多尺度特征聚合网络，在烟草害虫识别上取得97%以上精度且参数量仅1.21M，方法新颖、数据扎实，对智慧农业虫情监测有实用价值，值得进入每日精选。",[208],{"name":202,"url":199},[26,27,92,210,211],"病虫害识别","烟草",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[214],{"code":56,"slug":155,"name":156,"path":157,"parent_code":158},{"status":39,"locked":13,"version":40,"method":41,"evidence":216},{"candidates":217,"agricultural_evidence":45},[218,223,228],{"code":32,"score":100,"supported":45,"title":219,"abstract":221,"tags":222},[220],"tobacco",[211,220],[211],{"code":56,"score":20,"supported":45,"title":224,"abstract":225,"tags":227},[],[92,226],"deep learning",[27,92,210],{"code":51,"score":229,"supported":45,"title":230,"abstract":231,"tags":232},7,[],[26,106],[26],[234,235],"MSFANet 烟草害虫识别","多尺度特征聚合 害虫识别","MSFANet烟草害虫识别-5150","10.3389\u002Ffpls.2026.1871587",{"doi":237,"openalex_id":239,"authors":240,"venue":202,"cited_by_count":66,"oa_url":266,"card":267,"direction":133,"ingested_from":134},"W7222603139",[241,243,245,247,249,251,253,255,258,261,264],{"name":242,"orcid":8},"Shun He",{"name":244,"orcid":8},"Sifan Li",{"name":246,"orcid":8},"Xinjian Li",{"name":248,"orcid":8},"Qianqian Luo",{"name":250,"orcid":8},"Dong Sufen",{"name":252,"orcid":8},"Lu Lei",{"name":254,"orcid":8},"Xingjiang Chen",{"name":256,"orcid":257},"Liuti Cai","https:\u002F\u002Forcid.org\u002F0000-0002-9151-8002",{"name":259,"orcid":260},"Jian‐Yu Meng","https:\u002F\u002Forcid.org\u002F0000-0002-5796-1086",{"name":262,"orcid":263},"Ning Lu","https:\u002F\u002Forcid.org\u002F0000-0003-0214-5038",{"name":265,"orcid":8},"Hancheng Wang","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1871587\u002Fpdf",{"tldr":268,"method":269,"finding":270,"direction":71,"opportunity":271},"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。","多尺度金字塔池化MSPP、分层特征融合HFFB、辅助分类头与Focal Loss","模型准确率97.63%、参数量仅1.21M，在随机初始化模型中精度最高且参数最少。","可探索轻量多尺度网络在边缘设备部署及跨作物害虫迁移识别，结合类不平衡与复杂背景的鲁棒性研究。","2026-10-17T23:30:08.695455Z",{"id":274,"title":275,"url":276,"summary":277,"summary_zh":8,"content":8,"source_name":278,"source_url":8,"published_at":8,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":279,"score_detail":280,"sources":283,"tags":285,"primary_topic":288,"secondary_topics":289,"classification":291,"search_phrases":313,"slug":316,"view_count":66,"doi":8,"paper":317,"created_at":324},4966,"基于改进YOLO 11n模型的棉花田间复杂环境障碍物检测方法","http:\u002F\u002Fwww.qikanzj.com\u002Fhek\u002Fnyjxxb\u002Fmulu\u002F455619.html","韩科立、王振坤等(中国农业机械化科学研究院集团有限公司;现代农装科技股份有限公司;农业装备技术全国重点实验室)针对棉花田间复杂环境障碍物被遮挡致准确检测难、边缘设备算力有限的问题,提出一种基于改进YOLO 11n模型的田间障碍物检测方法。采用StarNet作为主要特征提取网络并引入DBA模块增强多尺度特征交互能力;使用KAGNConv替换C3k2瓶颈结构增强灵活性与可解释性;集成分离与增强注意力模块SEAM至检测头增强遮挡场景能力。试验结果表明,改进模型YOLO 11n-SKS精确率、召回率、mAP50、mAP50-95分别提升2.3、2.1、1.3、1.4个百分点,达到91.7%、88.3%、91.9%、62.3%。","《农业机械学报》",71,{"impact":87,"substance":144,"depth":163,"authority":281,"freshness":110,"relevant":21,"comment":282},14,"改进YOLO 11n实现棉花田间遮挡障碍物检测，方法新颖、指标提升明确，属农业人工智能细分领域实质进展，但发布日期未知、时效性偏弱。",[284],{"name":278,"url":276},[26,27,170,286,287],"障碍物检测","采棉机",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[290],{"code":56,"slug":155,"name":156,"path":157,"parent_code":158},{"status":39,"locked":13,"version":40,"method":41,"evidence":292},{"candidates":293,"agricultural_evidence":45},[294,298,302,309],{"code":32,"score":16,"supported":45,"title":295,"abstract":296,"tags":297},[170],[170],[170],{"code":56,"score":16,"supported":45,"title":299,"abstract":300,"tags":301},[165],[165],[27],{"code":303,"score":76,"supported":45,"title":304,"abstract":305,"tags":308},"F5",[],[306,307],"农业机械","机械化",[],{"code":51,"score":21,"supported":13,"title":310,"abstract":311,"tags":312},[],[],[26],[314,315],"中国农业机械化科学研究院 采棉机 障碍物检测","YOLO 11n 棉花 田间障碍物","中国农业机械化科学研究院采棉机障碍物检测-4966",{"doi":8,"openalex_id":8,"authors":318,"venue":8,"cited_by_count":66,"oa_url":8,"card":319,"direction":71,"ingested_from":73},[],{"tldr":320,"method":321,"finding":322,"direction":71,"opportunity":323},"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。","以StarNet为骨干，引入DBA、KAGNConv与SEAM注意力模块，改进Y","改进模型精确率91.7%、召回率88.3%、mAP50达91.9%，各项指标均提升。","可探索轻量化模型在边缘设备实时部署，并扩展至多作物多类障碍物检测。","2026-10-15T00:06:08.577388Z",{"id":326,"title":327,"url":328,"summary":329,"summary_zh":330,"content":8,"source_name":331,"source_url":328,"published_at":332,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":333,"score_detail":334,"sources":336,"tags":338,"primary_topic":341,"secondary_topics":342,"classification":349,"search_phrases":387,"slug":390,"view_count":21,"doi":391,"paper":392,"created_at":405},4660,"IoT-Based Automatic Cotton Leaf Disease Detection, Infection Percentage Estimation, and Treatment Recommendation Using Raspberry PI","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23280692","Cotton is one of the most important commercial crops worldwide, and its productivity is significantly affected by various leaf diseases such as bacterial blight, alter aria leaf spot, Cercospora leaf spot, powdery mildew, and leaf curl disease. Early and accurate identification of these diseases is essential to minimize crop losses and improve yield. Traditional disease diagnosis relies on manual field inspection by experts, which is time-consuming, labour-intensive, and often impractical for large agricultural fields. The proposed system integrates a Raspberry Pi5, a high-resolution camera module, environmental sensors, and wireless communication technologies to create an intelligent crop monitoring platform. Images of cotton leaves are captured using the Raspberry Pi camera and processed through computer vision techniques for leaf segmentation and feature extraction. A lightweight deep learning model based on MobileNetV2 is employed to automatically identify different cotton leaf diseases. In addition to disease classification, image segmentation techniques are used to calculate the percentage of infected leaf area, enabling the system to determine the severity level of infection. The system also collects environmental parameters such as temperature, humidity, soil moisture, and light intensity using IoT sensors. These parameters, along with disease diagnosis results, are transmitted to a cloud-based dashboard for real-time monitoring and analysis. Automated alerts can be sent to farmers through SMS or mobile notifications when severe infections are detected. Experimental evaluation is expected to achieve disease classification accuracy above 93%, infection area estimation error below 5%, and real-time inference within one second per image on the Raspberry Pi platform. The proposed solution offers a cost-effective, portable, and scalable approach for precision agriculture by combining artificial intelligence, image processing, and IoT technologies. The system enables early disease detection, accurate infection assessment, timely treatment recommendations, reduced pesticide usage, and improved cotton crop productivity, making it a valuable tool for smart farming applications.","棉花是全球最重要的经济作物之一，其产量受到多种叶部病害的显著影响，如细菌性枯萎病（bacterial blight）、链格孢叶斑病（alternaria leaf spot）、尾孢叶斑病（Cercospora leaf spot）、白粉病（powdery mildew）和卷叶病（leaf curl disease）。早期准确识别这些病害对于减少作物损失和提高产量至关重要。传统的病害诊断依赖专家进行人工田间巡检，这种方式耗时、费力，且对于大面积农田往往不切实际。所提出的系统集成了Raspberry Pi5、高分辨率摄像头模块、环境传感器和无线通信技术，构建了一个智能作物监测平台。利用Raspberry Pi摄像头采集棉花叶片图像，并通过计算机视觉技术进行叶片分割和特征提取。采用基于MobileNetV2的轻量级深度学习模型自动识别不同的棉花叶部病害。除病害分类外，还利用图像分割技术计算受感染叶面积百分比，使系统能够确定感染的严重程度。该系统还通过物联网传感器采集温度、湿度、土壤水分和光照强度等环境参数。这些参数连同病害诊断结果一起传输至基于云端的仪表板，用于实时监测和分析。当检测到严重感染时，可通过短信或移动通知向农民发送自动警报。实验评估预期在Raspberry Pi平台上实现高于93%的病害分类准确率、低于5%的感染面积估计误差，以及每张图像一秒以内的实时推理。所提出的解决方案结合了人工智能、图像处理和物联网技术，为精准农业提供了一种经济高效、便携且可扩展的方法。该系统能够实现病害早期检测、准确感染评估、及时治疗建议、减少农药使用并提高棉花作物产量，使其成为智慧农业应用中的一种有价值的工具。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-10T00:00:00Z",74,{"impact":87,"substance":17,"depth":18,"authority":16,"freshness":20,"relevant":21,"comment":335},"基于树莓派与轻量深度学习的棉花叶病检测与物联网监测方案，方法具体、指标明确，对智慧植保有参考价值。",[337],{"name":331,"url":328},[26,27,339,340,151],"物联网","精准农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[343,348],{"code":344,"slug":345,"name":346,"path":347,"parent_code":158},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"code":56,"slug":155,"name":156,"path":157,"parent_code":158},{"status":39,"locked":13,"version":40,"method":41,"evidence":350},{"candidates":351,"agricultural_evidence":45},[352,357,361,366,373,380],{"code":344,"score":100,"supported":45,"title":353,"abstract":355,"tags":356},[354],"IoT",[339,354],[339],{"code":32,"score":281,"supported":45,"title":358,"abstract":359,"tags":360},[172],[170,172],[],{"code":51,"score":19,"supported":45,"title":362,"abstract":363,"tags":365},[],[26,340,364],"precision agriculture",[26,340],{"code":56,"score":12,"supported":45,"title":367,"abstract":368,"tags":372},[],[92,369,226,370,371],"计算机视觉","artificial intelligence","computer vision",[27],{"code":374,"score":76,"supported":45,"title":375,"abstract":376,"tags":379},"F4",[],[377,378],"农药","pesticide",[],{"code":381,"score":76,"supported":45,"title":382,"abstract":383,"tags":386},"H2",[],[384,385],"土壤","soil",[],[388,389],"Raspberry Pi 棉花病害检测","MobileNetV2 棉花叶病识别","RaspberryPi棉花病害检测-4660","10.5281\u002Fzenodo.23280692",{"doi":391,"openalex_id":393,"authors":394,"venue":331,"cited_by_count":66,"oa_url":328,"card":400,"direction":133,"ingested_from":134},"W7221340722",[395,397],{"name":396,"orcid":8},"Deepak R Khadse",{"name":398,"orcid":399},"Pankaj Hiraman Zope","https:\u002F\u002Forcid.org\u002F0000-0003-3669-1440",{"tldr":401,"method":402,"finding":403,"direction":133,"opportunity":404},"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。","树莓派5+摄像头+环境传感器，MobileNetV2分类与图像分割，云端看板。","预期分类准确率超93%，感染面积误差低于5%，单图推理1秒内。","可探索边缘端轻量模型与多病害严重度分级、精准施药决策的闭环验证。","2026-10-11T23:30:07.876353Z",{"id":407,"title":408,"url":409,"summary":410,"summary_zh":8,"content":8,"source_name":411,"source_url":8,"published_at":412,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":413,"score_detail":414,"sources":418,"tags":420,"primary_topic":422,"secondary_topics":423,"classification":424,"search_phrases":440,"slug":443,"view_count":110,"doi":444,"paper":445,"created_at":452},4643,"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)","https:\u002F\u002Fwww.smartag.net.cn\u002FCN\u002F10.12133\u002Fj.smartag.SA202601027","辽宁工程技术大学沈学利团队基于改进RT-DETR提出MHSF-DETR,通过分层上下文选择主干、可学习加权融合与边界感知重构,实现多尺度异构特征协同。相比基准模型参数量与计算量分别降低22.42%和13.29%,mAP50提升3.2个百分点,外部数据集泛化mAP分别达80.3%与86.9%。局限:模型针对特定病斑尺度设计,跨作物迁移需重新训练。","智慧农业(中英文) 2026年第4期 2026-10-08","2026-10-08T00:00:00Z",78,{"impact":87,"substance":415,"depth":163,"authority":281,"freshness":416,"relevant":21,"comment":417},22,8,"改进RT-DETR的棉花叶片病害检测模型，参数量与计算量双降且精度提升，方法新颖、数据扎实，属细分领域实质进展。",[419],{"name":411,"url":409},[26,27,369,149,421],"棉花病害检测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":425},{"candidates":426,"agricultural_evidence":45},[427,431,436],{"code":32,"score":87,"supported":45,"title":428,"abstract":429,"tags":430},[170,172],[],[],{"code":56,"score":432,"supported":13,"title":433,"abstract":434,"tags":435},3,[],[],[27,149,369],{"code":51,"score":21,"supported":13,"title":437,"abstract":438,"tags":439},[],[],[26],[441,442],"辽宁工程技术大学 棉花叶片病害检测","MHSF-DETR 棉花病害","辽宁工程技术大学棉花叶片病害检测-4643","10.12133\u002Fj.smartag.sa202601027",{"doi":444,"openalex_id":8,"authors":446,"venue":8,"cited_by_count":66,"oa_url":8,"card":447,"direction":71,"ingested_from":73},[],{"tldr":448,"method":449,"finding":450,"direction":71,"opportunity":451},"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。","改进RT-DETR，采用分层上下文选择主干、可学习加权融合与边界感知重构。","mAP50提升3.2个百分点，参数量与计算量分别降低22.42%和13.29%，外部数据集泛化良好。","可探索跨作物病害检测的轻量化迁移学习与域自适应方法，减少重新训练成本。","2026-10-11T00:09:47.309161Z"]