[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1208":3,"related-1208":103},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"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":65,"slug":68,"view_count":69,"doi":70,"paper":71,"created_at":102},1208,"Restoration of occlusion in residual film images and curling degree prediction based on UAV in pre-sowing cotton fields","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112335","Restoration of occlusion in residual film images and curling degree prediction based on UAV in pre-sowing cotton fields。Computers and Electronics in Agriculture",null,"Computers and Electronics in Agriculture","2026-08-25T00:00:00Z","论文",10,false,62,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},12,18,16,14,2,1,"基于无人机影像的残膜污染检测与卷曲度预测研究，方法新颖，对农业环保有实际价值，但时效性较低。",[24],{"name":9,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","棉花","遥感","残膜污染",{"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},[44,50,55,60],{"code":32,"score":16,"supported":45,"title":46,"abstract":48,"tags":49},true,[47],"cotton",[47],[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],{"code":61,"score":21,"supported":13,"title":62,"abstract":63,"tags":64},"I3",[],[],[29],[66,67],"农业人工智能 智慧农业 残膜污染 棉花","农业人工智能 智慧农业","农业人工智能智慧农业残膜污染棉花-1208",3,"10.1016\u002Fj.compag.2026.112335",{"doi":70,"openalex_id":72,"authors":73,"venue":9,"cited_by_count":94,"oa_url":8,"card":95,"direction":99,"ingested_from":101},"W7204210197",[74,77,79,82,85,88,91],{"name":75,"orcid":76},"Zhiqiang Zhai","https:\u002F\u002Forcid.org\u002F0000-0002-5242-9779",{"name":78,"orcid":8},"Lupeng Miao",{"name":80,"orcid":81},"Huting Wang","https:\u002F\u002Forcid.org\u002F0009-0008-3605-2685",{"name":83,"orcid":84},"Jiangbo Li","https:\u002F\u002Forcid.org\u002F0000-0001-7856-9131",{"name":86,"orcid":87},"Yue Chen","https:\u002F\u002Forcid.org\u002F0009-0004-5110-6400",{"name":89,"orcid":90},"Songxin Ye","https:\u002F\u002Forcid.org\u002F0000-0002-8895-8525",{"name":92,"orcid":93},"Ruoyu Zhang","https:\u002F\u002Forcid.org\u002F0000-0003-0370-3249",0,{"tldr":96,"method":97,"finding":98,"direction":99,"opportunity":100},"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。","无人机图像、遮挡修复、卷曲度预测模型。","成功修复遮挡并预测卷曲度，支持残膜污染评估。","农业遥感与作物表型","可扩展至不同作物、不同残膜类型，结合深度学习提升修复精度和预测鲁棒性。","openalex","2026-09-01T04:03:02.336224Z",{"total":104,"page":21,"page_size":104,"items":105},6,[106,165,224,280,364,411],{"id":107,"title":108,"url":109,"summary":110,"summary_zh":8,"content":8,"source_name":111,"source_url":8,"published_at":8,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":112,"score_detail":113,"sources":116,"tags":118,"primary_topic":121,"secondary_topics":122,"classification":128,"search_phrases":151,"slug":154,"view_count":94,"doi":8,"paper":155,"created_at":164},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":18,"substance":114,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":115},21,"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。",[117],{"name":111,"url":109},[26,27,28,119,120],"障碍物检测","采棉机",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[123],{"code":56,"slug":124,"name":125,"path":126,"parent_code":127},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":39,"locked":13,"version":40,"method":41,"evidence":129},{"candidates":130,"agricultural_evidence":45},[131,135,140,147],{"code":32,"score":16,"supported":45,"title":132,"abstract":133,"tags":134},[28],[28],[28],{"code":56,"score":16,"supported":45,"title":136,"abstract":138,"tags":139},[137],"YOLO",[137],[27],{"code":141,"score":104,"supported":45,"title":142,"abstract":143,"tags":146},"F5",[],[144,145],"农业机械","机械化",[],{"code":51,"score":21,"supported":13,"title":148,"abstract":149,"tags":150},[],[],[26],[152,153],"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":156,"venue":8,"cited_by_count":94,"oa_url":8,"card":157,"direction":161,"ingested_from":163},[],{"tldr":158,"method":159,"finding":160,"direction":161,"opportunity":162},"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。","农业人工智能与决策模型","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。","agent","2026-10-15T00:06:08.577388Z",{"id":166,"title":167,"url":168,"summary":169,"summary_zh":170,"content":8,"source_name":171,"source_url":168,"published_at":172,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":173,"score_detail":174,"sources":179,"tags":181,"primary_topic":184,"secondary_topics":185,"classification":190,"search_phrases":208,"slug":211,"view_count":69,"doi":212,"paper":213,"created_at":223},3838,"Forecasting Cotton Productivity in the Mil-Mugan Economic Region using Climate Variables and Remote Sensing Data","https:\u002F\u002Fdoi.org\u002F10.52171\u002Fherald.342","Satellite imagery is considered as one of the most valuable and functional data sources for evaluating and forecasting production in agricultural systems. This study aims to forecast productivity in cotton fields within the Mil-Mugan economic region by incorporating vegetation indices and climatic variables. NDVI indicators were derived from Sentinel-2 data, temperature (T) was obtained from ERA5-Land data, and precipitation (R) was collected from the CHIRPS database. The data were analyzed using Google Earth Engine, ArcGIS Pro, and Python environments, consolidating them into an integrated temporal-spatial dimension. The correlation and regression analysis assessed the link among NDVI, temperature, precipitation, and productivity, leading to the development of a multivariate model. The findings derived from the model indicate that NDVI is the primary factor influencing productivity. The model developed for one region was implemented in the other three locations, and the correlation between the actual and predicted indicators was assessed using R² and RMSE metrics. The results indicate that the model's forecast accuracy fluctuates within the 90% range, considered appropriate for predicting regional cotton yield. The study's findings are significant for enhancing operational monitoring and planning of agrotechnical decisions with satellite data.","卫星影像被认为是评估和预测农业系统产量最有价值且最实用的数据来源之一。本研究旨在通过整合植被指数与气候变量，预测米尔-穆甘经济区棉田的生产力。NDVI指标源自Sentinel-2数据，温度（T）取自ERA5-Land数据，降水（R）来自CHIRPS数据库。数据在Google Earth Engine、ArcGIS Pro和Python环境中进行分析，并将其整合为统一的时空维度。通过相关性与回归分析评估了NDVI、温度、降水与生产力之间的关系，进而构建了多变量模型。模型结果表明，NDVI是影响生产力的主要因素。将为某一区域开发的模型应用于其他三个地点，并使用R²和RMSE指标评估实际值与预测值之间的相关性。结果表明，模型的预测精度在90%范围内波动，被认为适用于预测区域棉花产量。本研究结果对于利用卫星数据加强农艺决策的业务化监测与规划具有重要意义。","Herald of the Azerbaijan Engineering Academy","2026-09-29T00:00:00Z",63,{"impact":175,"substance":176,"depth":18,"authority":12,"freshness":177,"relevant":21,"comment":178},8,20,9,"基于Sentinel-2与气候变量的棉花产量预测模型，方法扎实、精度达90%，但属区域案例研究，公共影响有限。",[180],{"name":171,"url":168},[26,182,28,29,183],"产量预测","NDVI",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[186],{"code":61,"slug":187,"name":188,"path":189,"parent_code":127},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":39,"locked":13,"version":40,"method":41,"evidence":191},{"candidates":192,"agricultural_evidence":45},[193,198,204],{"code":32,"score":194,"supported":45,"title":195,"abstract":196,"tags":197},15,[47],[28,47],[28],{"code":61,"score":199,"supported":45,"title":200,"abstract":202,"tags":203},11,[201],"remote sensing",[],[183,182,29],{"code":51,"score":21,"supported":13,"title":205,"abstract":206,"tags":207},[],[],[26],[209,210],"Mil-Mugan 棉花 遥感","Sentinel-2 NDVI 棉花产量","Mil-Mugan棉花遥感-3838","10.52171\u002Fherald.342",{"doi":212,"openalex_id":214,"authors":215,"venue":171,"cited_by_count":94,"oa_url":168,"card":218,"direction":99,"ingested_from":101},"W7214748696",[216],{"name":217,"orcid":8},"B.H. Babashli",{"tldr":219,"method":220,"finding":221,"direction":99,"opportunity":222},"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。","Sentinel-2 NDVI、ERA5-Land温度、CHIRPS降水，GEE","NDVI是产量主控因子，模型跨区预测精度约90%，可用于区域估产。","可引入多时相NDVI与机器学习，提升跨区域迁移性和极端气候下的估产鲁棒性。","2026-09-30T23:30:27.786044Z",{"id":225,"title":226,"url":227,"summary":228,"summary_zh":229,"content":8,"source_name":230,"source_url":227,"published_at":231,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":232,"score_detail":233,"sources":235,"tags":237,"primary_topic":239,"secondary_topics":240,"classification":242,"search_phrases":257,"slug":260,"view_count":261,"doi":262,"paper":263,"created_at":279},3179,"Multi-features and Fuzzy with Residual Lenet for Cotton Classification Using Remote Sensing Image","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12524-026-02541-8","Multi-features and Fuzzy with Residual Lenet for Cotton Classification Using Remote Sensing Image。Journal of the Indian Society of Remote Sensing","多特征与模糊残差LeNet用于基于遥感图像的棉花分类。《印度遥感学会杂志》","Journal of the Indian Society of Remote Sensing","2026-09-21T00:00:00Z",58,{"impact":175,"substance":19,"depth":194,"authority":16,"freshness":177,"relevant":21,"comment":234},"方法类论文，提出多特征与模糊结合残差LeNet用于遥感棉花分类，专业价值尚可但影响面窄，适合作为技术参考而非每日精选头条。",[236],{"name":230,"url":227},[27,28,29,238],"作物分类",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[241],{"code":61,"slug":187,"name":188,"path":189,"parent_code":127},{"status":39,"locked":13,"version":40,"method":41,"evidence":243},{"candidates":244,"agricultural_evidence":45},[245,249,253],{"code":61,"score":18,"supported":45,"title":246,"abstract":247,"tags":248},[201],[29,201],[238,29],{"code":32,"score":194,"supported":45,"title":250,"abstract":251,"tags":252},[47],[28,47],[28],{"code":56,"score":21,"supported":13,"title":254,"abstract":255,"tags":256},[],[],[27],[258,259],"Residual LeNet 棉花 遥感分类","农业人工智能 作物分类 棉花 遥感","ResidualLeNet棉花遥感分类-3179",5,"10.1007\u002Fs12524-026-02541-8",{"doi":262,"openalex_id":264,"authors":265,"venue":230,"cited_by_count":94,"oa_url":8,"card":274,"direction":99,"ingested_from":101},"W7213944040",[266,268,270,272],{"name":267,"orcid":8},"G. Suresh",{"name":269,"orcid":8},"G. Bhuvaneswari",{"name":271,"orcid":8},"G. Manikandan",{"name":273,"orcid":8},"M. Robinson Joel",{"tldr":275,"method":276,"finding":277,"direction":99,"opportunity":278},"提出多特征与模糊结合残差LeNet的遥感影像棉花分类方法。","多特征提取、模糊逻辑与残差LeNet网络，用于遥感图像分类。","该方法能有效提升棉花分类精度，验证了深度残差网络结合模糊特征的可行性。","可探索多特征与模糊深度学习在其他作物遥感分类中的泛化能力及轻量化部署。","2026-09-22T23:30:23.811658Z",{"id":281,"title":282,"url":283,"summary":284,"summary_zh":285,"content":8,"source_name":286,"source_url":283,"published_at":287,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":288,"score_detail":289,"sources":292,"tags":294,"primary_topic":297,"secondary_topics":298,"classification":300,"search_phrases":329,"slug":331,"view_count":69,"doi":332,"paper":333,"created_at":363},2288,"Field-based deep learning classification of cotton and weeds for machine-vision-assisted intra-row weed management","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.atech.2026.102567","Timely and precise weed management is essential for reducing crop-weed competition, labour requirements, and unnecessary weed-control inputs in cotton production. Reliable discrimination between cotton plants and weeds is a prerequisite for automated intra-row weed management. This study evaluated two pretrained convolutional neural network models, MobileNetV2 and DenseNet169, for binary classification of cotton and weeds using field-acquired RGB images. Images were collected from a farmer-managed cotton field in Haryana and an experimental cotton field at ICAR-Indian Agricultural Research Institute, New Delhi, during May-September 2025 under natural field conditions. The dataset comprised 2,195 weed images representing six predominant weed species and 1,211 cotton images. Images were classified into two operational classes, cotton and weed. Training images were augmented to address the original class imbalance, whereas validation and test images were retained without augmentation. Transfer learning was used to fine-tune both models, and performance was evaluated using accuracy, precision, recall, F1-score, confusion matrices, and inference time. Both models achieved high classification performance. DenseNet169 attained a test accuracy of 99.47%, compared with 98.27% for MobileNetV2. The results indicate that pretrained CNNs can provide accurate image-level discrimination between cotton and weed vegetation under the investigated field conditions. DenseNet169 therefore shows promise as a visual-perception component for machine-vision-assisted intra-row weed management. However, the present study is limited to image-level classification and does not demonstrate plant localization, continuous machine operation, actuator coordination, or field-scale weed removal. Further validation under independent locations, varying weed densities, illumination conditions, machine travel speeds, and complete perception-decision-actuation pipelines is required.","及时、精准的杂草管理对于减少棉花生产中的棉草竞争、劳动力需求和不必要的除草投入至关重要。可靠地区分棉花植株与杂草是实现行内自动化杂草管理的前提。本研究评估了两种预训练卷积神经网络模型——MobileNetV2和DenseNet169，利用田间采集的RGB图像对棉花与杂草进行二分类。图像于2025年5月至9月期间，在自然田间条件下，采集自哈里亚纳邦一处农民管理的棉田以及新德里ICAR-印度农业研究所的实验棉田。数据集包含2，195幅杂草图像，涵盖六种主要杂草种类，以及1，211幅棉花图像。图像被分为棉花和杂草两个操作类别。训练图像经过增强处理以解决原始类别不平衡问题，而验证和测试图像则保留未增强状态。采用迁移学习对两种模型进行微调，并使用准确率、精确率、召回率、F1分数、混淆矩阵和推理时间评估性能。两种模型均取得了较高的分类性能。DenseNet169的测试准确率达到99.47%，而MobileNetV2为98.27%。结果表明，预训练卷积神经网络能够在所研究的田间条件下实现棉花与杂草植被在图像层面的准确区分。因此，DenseNet169有望作为机器视觉辅助行内杂草管理的视觉感知组件。然而，本研究仅限于图像层面的分类，并未展示植株定位、机器连续作业、执行器协调或田间规模除草。仍需在独立地点、不同杂草密度、光照条件、机器行进速度以及完整的感知-决策-执行流程下进行进一步验证。","Smart Agricultural Technology","2026-09-11T00:00:00Z",67,{"impact":16,"substance":17,"depth":18,"authority":290,"freshness":175,"relevant":21,"comment":291},13,"基于田间RGB图像的棉花与杂草深度学习分类研究，DenseNet169测试准确率达99.47%，方法扎实但仅限图像级分类，尚缺定位与执行环节验证，属细分领域技术进展。",[293],{"name":286,"url":283},[26,27,28,295,296],"杂草识别","机器视觉",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[299],{"code":56,"slug":124,"name":125,"path":126,"parent_code":127},{"status":39,"locked":13,"version":40,"method":41,"evidence":301},{"candidates":302,"agricultural_evidence":45},[303,307,312,319,325],{"code":32,"score":194,"supported":45,"title":304,"abstract":305,"tags":306},[47],[28,47],[28],{"code":56,"score":12,"supported":45,"title":308,"abstract":310,"tags":311},[309],"deep learning",[],[27,296],{"code":313,"score":314,"supported":45,"title":315,"abstract":316,"tags":318},"F4",4,[],[317],"除草",[295],{"code":320,"score":69,"supported":45,"title":321,"abstract":322,"tags":324},"A4",[],[323],"花生",[],{"code":51,"score":21,"supported":13,"title":326,"abstract":327,"tags":328},[],[],[26],[330,67],"农业人工智能 智慧农业 机器视觉 杂草识别","农业人工智能智慧农业机器视觉杂草识别-2288","10.1016\u002Fj.atech.2026.102567",{"doi":332,"openalex_id":334,"authors":335,"venue":286,"cited_by_count":94,"oa_url":283,"card":358,"direction":161,"ingested_from":101},"W7212315230",[336,338,341,344,347,349,352,354,356],{"name":337,"orcid":8},"Shaik Nasreen",{"name":339,"orcid":340},"Roaf Ahmad Parray","https:\u002F\u002Forcid.org\u002F0000-0002-8303-1990",{"name":342,"orcid":343},"Parveen Dhanger","https:\u002F\u002Forcid.org\u002F0000-0001-5250-0275",{"name":345,"orcid":346},"Rishi Raj","https:\u002F\u002Forcid.org\u002F0009-0002-7918-1141",{"name":348,"orcid":8},"Tapan Kumar Khura",{"name":350,"orcid":351},"P. Sahoo","https:\u002F\u002Forcid.org\u002F0000-0002-3888-8506",{"name":353,"orcid":8},"Tushar Dhar",{"name":355,"orcid":8},"Prajwal R",{"name":357,"orcid":8},"Sripriyanka S. Nalla",{"tldr":359,"method":360,"finding":361,"direction":161,"opportunity":362},"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","迁移学习微调两种预训练CNN，使用2025年田间RGB图像共3406张。","DenseNet169测试准确率达99.47%，优于MobileNetV2的98.27%。","可延伸至植株定位、多光照与密度条件下的感知-决策-执行全流程田间验证。","2026-09-13T23:30:04.183782Z",{"id":365,"title":366,"url":367,"summary":368,"summary_zh":8,"content":8,"source_name":369,"source_url":8,"published_at":370,"category":371,"cover_url":8,"hotness":12,"is_selected":13,"score":372,"score_detail":373,"sources":378,"tags":380,"primary_topic":383,"secondary_topics":384,"classification":385,"search_phrases":406,"slug":409,"view_count":20,"doi":8,"paper":8,"created_at":410},2228,"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\"","https:\u002F\u002Fdy.163.com\u002Farticle\u002FL6HM1VUQ05199FC8.html","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。","中国农业科学院 布瑞克农业数据","2026-09-10T16:00:00Z","报道",85,{"impact":374,"substance":375,"depth":376,"authority":19,"freshness":104,"relevant":21,"comment":377},26,22,17,"全球首个棉花领域专属大语言模型，覆盖全产业链数据，标志农业AI向垂直深耕转型，产业级突破值得入选。",[379],{"name":369,"url":367},[26,27,381,382,28],"种业振兴","大语言模型",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":13,"version":40,"method":41,"evidence":386},{"candidates":387,"agricultural_evidence":45},[388,392,398,402],{"code":32,"score":16,"supported":45,"title":389,"abstract":390,"tags":391},[28],[28],[28],{"code":393,"score":314,"supported":45,"title":394,"abstract":395,"tags":397},"F1",[],[396],"种质资源",[381],{"code":56,"score":20,"supported":13,"title":399,"abstract":400,"tags":401},[],[],[27,382],{"code":51,"score":21,"supported":13,"title":403,"abstract":404,"tags":405},[],[],[26],[407,408],"农业人工智能 大语言模型 智慧农业 种业振兴","农业人工智能 大语言模型","农业人工智能大语言模型智慧农业种业振兴-2228","2026-09-13T00:04:00.640632Z",{"id":412,"title":413,"url":414,"summary":415,"summary_zh":416,"content":8,"source_name":9,"source_url":414,"published_at":417,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":418,"sources":420,"tags":422,"primary_topic":424,"secondary_topics":425,"classification":427,"search_phrases":444,"slug":446,"view_count":69,"doi":447,"paper":448,"created_at":471},1878,"Decoding Electro-Priming responses in cotton using an integrated taguchi-machine learning framework for optimizing seed performance","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112362","Decoding Electro-Priming responses in cotton using an integrated taguchi-machine learning framework for optimizing seed performance。Computers and Electronics in Agriculture","利用集成田口-机器学习框架解析棉花电引发响应以优化种子性能。《农业中的计算机与电子》","2026-09-07T00:00:00Z",{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":419},"研究结合田口法与机器学习优化棉花电引发种子处理，方法新颖，但发表于2026年9月7日，时效性较低，影响范围限于特定作物领域。",[421],{"name":9,"url":414},[26,27,28,423],"种子处理",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[426],{"code":56,"slug":124,"name":125,"path":126,"parent_code":127},{"status":39,"locked":13,"version":40,"method":41,"evidence":428},{"candidates":429,"agricultural_evidence":45},[430,434,440],{"code":32,"score":194,"supported":45,"title":431,"abstract":432,"tags":433},[47],[28,47],[28],{"code":56,"score":194,"supported":45,"title":435,"abstract":437,"tags":439},[436],"machine learning",[438,436],"机器学习",[27],{"code":51,"score":21,"supported":13,"title":441,"abstract":442,"tags":443},[],[],[26],[445,67],"农业人工智能 智慧农业 种子处理 棉花","农业人工智能智慧农业种子处理棉花-1878","10.1016\u002Fj.compag.2026.112362",{"doi":447,"openalex_id":449,"authors":450,"venue":9,"cited_by_count":94,"oa_url":8,"card":466,"direction":161,"ingested_from":101},"W7211870713",[451,454,457,459,461,464],{"name":452,"orcid":453},"Muhammad Aasım","https:\u002F\u002Forcid.org\u002F0000-0002-8524-9029",{"name":455,"orcid":456},"Ramazan Katırcı","https:\u002F\u002Forcid.org\u002F0000-0003-2448-011X",{"name":458,"orcid":8},"Salma Naimatullah Soomro",{"name":460,"orcid":8},"Sabeen Rehman Soomro",{"name":462,"orcid":463},"Ahmet Say","https:\u002F\u002Forcid.org\u002F0000-0001-5259-7580",{"name":465,"orcid":8},"Seyid Amjad Ali",{"tldr":467,"method":468,"finding":469,"direction":161,"opportunity":470},"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。","田口实验设计与机器学习集成框架。","电引发参数可优化，显著提升棉花种子性能。","可扩展至其他作物或种子处理方式，探索机器学习在农业实验优化中的应用。","2026-09-08T23:30:01.361561Z"]