[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4990":3,"related-4990":100},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":6,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":72,"slug":75,"view_count":76,"doi":77,"paper":78,"created_at":99},4990,"Smart Crop Advisory System","https:\u002F\u002Fdoi.org\u002F10.55041\u002Fijcope.v2i10.081","The Smart Crop Advisory System is a web-based agricultural decision-support platform that uses Artificial Intelligence (AI) and Machine Learning (ML) technologies to assist farmers in making informed farming decisions. Traditional agriculture faces several challenges, including unsuitable crop selection, unpredictable weather conditions, crop diseases, and fluctuations in market prices. The proposed system integrates essential agricultural services, including crop recommendation, weather forecasting, crop disease detection, agricultural market price information, and an AI-based multilingual voice assistant. It also provides farmer profile management, crop price trend analysis, and rainfall trend visualization through an interactive dashboard. The crop recommendation module helps farmers identify suitable crops based on relevant agricultural parameters, while the disease detection module supports the identification of potential crop diseases. Weather and market information help farmers plan agricultural activities and make informed selling decisions. The multilingual voice assistant aims to improve accessibility by enabling farmers to interact with the system through voice-based communication. Overall, the Smart Crop Advisory System aims to improve access to agricultural information, support effective decision-making, promote efficient resource utilization, and encourage sustainable farming practices. Keywords: Smart Agriculture, Crop Recommendation, Machine Learning, Crop Disease Detection, Weather Forecasting, Market Price Analysis, Voice Assistant, Sustainable Agriculture.","智能作物咨询系统是一个基于网络的农业决策支持平台，利用人工智能（AI）和机器学习（ML）技术帮助农民做出明智的农作决策。传统农业面临诸多挑战，包括作物选择不当、天气条件不可预测、作物病害以及市场价格波动。所提出的系统整合了基本农业服务，包括作物推荐、天气预报、作物病害检测、农产品市场价格信息以及基于AI的多语言语音助手。它还通过交互式仪表板提供农民档案管理、作物价格趋势分析和降雨趋势可视化。作物推荐模块帮助农民根据相关农业参数识别适宜作物，而病害检测模块支持识别潜在作物病害。天气和市场信息帮助农民规划农业活动并做出明智的销售决策。多语言语音助手旨在通过使农民能够以语音方式与系统交互来提高可及性。总体而言，智能作物咨询系统旨在改善农业信息的获取，支持有效决策，促进资源高效利用，并鼓励可持续农业实践。关键词：智慧农业、作物推荐、机器学习、作物病害检测、天气预报、市场价格分析、语音助手、可持续农业。",null,"International Journal of Creative and Open Research in Engineering and Management","2026-10-14T00:00:00Z","论文",10,false,48,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":21,"comment":22},8,14,12,6,1,"面向农户的AI农业决策支持系统论文，功能集成度较高但方法新颖性与数据规模有限，属细分领域进展，可作为智慧农业主题聚合素材。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","病虫害识别","农业气象","作物推荐",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,53,60,67],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":52},11,true,[],[49,50,51],"机器学习","machine learning","artificial intelligence",[27,28],{"code":54,"score":55,"supported":46,"title":56,"abstract":57,"tags":59},"I1",7,[],[26,58],"smart agriculture",[26],{"code":61,"score":20,"supported":46,"title":62,"abstract":63,"tags":66},"H8",[],[64,65],"可持续农业","sustainable agriculture",[],{"code":68,"score":21,"supported":14,"title":69,"abstract":70,"tags":71},"H7",[],[],[29],[73,74],"AI 作物推荐 语音助手","农业人工智能 病虫害识别 作物推荐 农业气象","AI作物推荐语音助手-4990",2,"10.55041\u002Fijcope.v2i10.081",{"doi":77,"openalex_id":79,"authors":80,"venue":10,"cited_by_count":89,"oa_url":90,"card":91,"direction":97,"ingested_from":98},"W7221803029",[81,83,85,87],{"name":82,"orcid":9},"Bhuvana H P",{"name":84,"orcid":9},"Kavya C M",{"name":86,"orcid":9},"Manjula S",{"name":88,"orcid":9},"Priyanka A",0,"https:\u002F\u002Fijcope.org\u002Fwp-content\u002Fuploads\u002F2026\u002F10\u002FSmart-Crop-Advisory-System.pdf",{"tldr":92,"method":93,"finding":94,"direction":95,"opportunity":96},"构建基于AI\u002FML的Web农业决策支持平台，为农民提供作物推荐、病害检测、天气与市场价格等服务。","集成机器学习模型、多语言语音助手与交互式仪表盘，整合天气、市场及农业参数数据。","系统可提升农业信息获取与决策效率，促进资源高效利用和可持续耕作。","农业人工智能与决策模型","可探索多源数据融合的轻量化模型与本地化语音交互，提升小农户采纳率与实时决策精度。","智慧农业 \u002F 农业物联网","openalex","2026-10-15T23:30:09.048921Z",{"total":20,"page":21,"page_size":20,"items":101},[102,184,264,338,405,459],{"id":103,"title":104,"url":105,"summary":106,"summary_zh":107,"content":9,"source_name":108,"source_url":105,"published_at":109,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":110,"score_detail":111,"sources":117,"tags":119,"primary_topic":121,"secondary_topics":122,"classification":128,"search_phrases":166,"slug":169,"view_count":21,"doi":170,"paper":171,"created_at":183},3877,"Machine learning classification techniques for smart agriculture: a review of disease detection and crop recommendation","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrai.2026.1878006","The adoption of Internet of Things (IoT) and Machine Learning (ML) in agriculture presents a revolutionary step toward efficient agricultural practices using data to address global issues surrounding food security. This review conducted a systematic search of the Scopus, IEEE Xplore, ScienceDirect, and SpringerLink databases for articles published between January 2023 and January 2026. It used keyword combinations like “IoT and machine learning and agriculture”, “crop recommendation”, “plant disease detection”, and “precision farming”. From the initial pool of articles found, 30 articles met the criteria for empirical methodology, quantitative performance reporting, and clear dataset descriptions. These were chosen for detailed analysis. It was discovered that the ensemble, as well as the traditional machine learning methods, such as Random Forest, Support Vector Machine (SVM), and Decision Trees, produce highly accurate predictions of the crop yields with an accuracy of 99.9%. Meanwhile, Convolutional Neural Networks (CNN) and ResNet-based deep learning models detect diseases with an accuracy above 98% while Gated Recurrent Units (GRU) and Long-Short Term Memory (LSTM) time-series models provide forecasts of weather conditions with an accuracy ranging from 94 to 98%, due to the complex nature of the environment dynamics. The application of IoT systems for precision agriculture produced a significant improvement in terms of resource utilization, resulting in 60–96% of water savings, 25% increased yield, and 36–38% reduced herbicide use. Although these advancements have been made, wider application is still hindered by high costs of implementation, lack of technological skills among farmers, data privacy concerns, issues of scalability within different agricultural settings, and consistent internet connection in rural areas. The current paper evaluates the use of IoT and ML in the agriculture sector from diverse data sources and studies to reveal the immense benefits offered by the two systems in enhancing farming activities.","物联网（IoT）与机器学习（ML）在农业中的应用标志着向高效农业实践迈出了革命性的一步，即利用数据来解决全球粮食安全相关问题。本综述对Scopus、IEEE Xplore、ScienceDirect和SpringerLink数据库中2023年1月至2026年1月间发表的文章进行了系统性检索，使用了“物联网与机器学习与农业”“作物推荐”“植物病害检测”和“精准农业”等关键词组合。从初步检索到的文章池中，30篇文章符合实证方法、定量性能报告和清晰数据集描述的标准，被选入进行详细分析。研究发现，集成方法以及随机森林、支持向量机（SVM）和决策树等传统机器学习方法对作物产量的预测精度高达99.9%。同时，卷积神经网络（CNN）和基于ResNet的深度学习模型检测病害的准确率超过98%，而门控循环单元（GRU）和长短期记忆（LSTM）时间序列模型由于环境动态的复杂性，对天气状况的预测准确率在94%至98%之间。物联网系统在精准农业中的应用在资源利用方面产生了显著改善，实现了60%至96%的节水、25%的产量提升以及36%至38%的除草剂用量减少。尽管取得了这些进展，更广泛的应用仍受到实施成本高昂、农民缺乏技术技能、数据隐私顾虑、不同农业场景下的可扩展性问题以及农村地区互联网连接不稳定等因素的制约。本文通过来自不同数据来源和研究，评估了物联网与机器学习在农业领域的应用，揭示了这两个系统在提升农业活动方面所带来的巨大效益。","Frontiers in Artificial Intelligence","2026-09-30T00:00:00Z",76,{"impact":112,"substance":113,"depth":114,"authority":115,"freshness":17,"relevant":21,"comment":116},18,20,17,13,"系统综述整合30篇实证研究，量化了IoT与机器学习在作物推荐、病害检测和精准灌溉上的性能与节水增产效益，对智慧农业技术选型有参考价值。",[118],{"name":108,"url":105},[26,27,28,120,30],"精准农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[123],{"code":124,"slug":125,"name":126,"path":127,"parent_code":36},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":39,"locked":14,"version":40,"method":41,"evidence":129},{"candidates":130,"agricultural_evidence":46},[131,138,144,152,159],{"code":32,"score":132,"supported":46,"title":133,"abstract":134,"tags":137},19,[50],[135,49,50,136],"深度学习","deep learning",[27,28],{"code":54,"score":139,"supported":46,"title":140,"abstract":141,"tags":143},16,[58],[120,142],"precision agriculture",[26,120],{"code":124,"score":145,"supported":46,"title":146,"abstract":147,"tags":151},9,[],[148,149,150],"物联网","internet of things","IoT",[],{"code":153,"score":20,"supported":46,"title":154,"abstract":155,"tags":158},"A8",[],[156,157],"粮食安全","food security",[],{"code":160,"score":161,"supported":46,"title":162,"abstract":163,"tags":165},"F4",3,[],[164],"除草",[],[167,168],"农业人工智能 病虫害识别 作物推荐 智慧农业","农业人工智能 病虫害识别","农业人工智能病虫害识别作物推荐智慧农业-3877","10.3389\u002Ffrai.2026.1878006",{"doi":170,"openalex_id":172,"authors":173,"venue":108,"cited_by_count":89,"oa_url":105,"card":178,"direction":97,"ingested_from":98},"W7214889993",[174,176],{"name":175,"orcid":9},"M. Umamaheswari",{"name":177,"orcid":9},"Kavitha D",{"tldr":179,"method":180,"finding":181,"direction":95,"opportunity":182},"综述2023-2026年30篇文献，评估物联网与机器学习在作物病害检测和推荐中的应用。","系统检索Scopus等四个数据库，筛选30篇实证研究进行性能与数据集分析。","集成学习与SVM等预测产量准确率达99.9%，CNN病害检测超98%，IoT节水60-96%。","可研究低成本、低连接依赖的边缘ML模型，解决小农户部署与数据隐私问题。","2026-10-01T23:30:09.158196Z",{"id":185,"title":186,"url":187,"summary":188,"summary_zh":189,"content":9,"source_name":190,"source_url":187,"published_at":191,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":110,"score_detail":192,"sources":195,"tags":197,"primary_topic":199,"secondary_topics":205,"classification":207,"search_phrases":224,"slug":227,"view_count":21,"doi":228,"paper":229,"created_at":263},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","2026-10-16T00:00:00Z",{"impact":139,"substance":193,"depth":114,"authority":115,"freshness":145,"relevant":21,"comment":194},21,"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。",[196],{"name":190,"url":187},[26,27,135,28,198],"烟草",{"code":200,"slug":201,"name":202,"path":203,"parent_code":204},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[206],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":208},{"candidates":209,"agricultural_evidence":46},[210,216,220],{"code":200,"score":211,"supported":46,"title":212,"abstract":214,"tags":215},15,[213],"tobacco",[198,213],[198],{"code":32,"score":145,"supported":46,"title":217,"abstract":218,"tags":219},[],[135,136],[27,135,28],{"code":54,"score":55,"supported":46,"title":221,"abstract":222,"tags":223},[],[26,58],[26],[225,226],"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":228,"openalex_id":230,"authors":231,"venue":190,"cited_by_count":89,"oa_url":257,"card":258,"direction":97,"ingested_from":98},"W7222603139",[232,234,236,238,240,242,244,246,249,252,255],{"name":233,"orcid":9},"Shun He",{"name":235,"orcid":9},"Sifan Li",{"name":237,"orcid":9},"Xinjian Li",{"name":239,"orcid":9},"Qianqian Luo",{"name":241,"orcid":9},"Dong Sufen",{"name":243,"orcid":9},"Lu Lei",{"name":245,"orcid":9},"Xingjiang Chen",{"name":247,"orcid":248},"Liuti Cai","https:\u002F\u002Forcid.org\u002F0000-0002-9151-8002",{"name":250,"orcid":251},"Jian‐Yu Meng","https:\u002F\u002Forcid.org\u002F0000-0002-5796-1086",{"name":253,"orcid":254},"Ning Lu","https:\u002F\u002Forcid.org\u002F0000-0003-0214-5038",{"name":256,"orcid":9},"Hancheng Wang","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1871587\u002Fpdf",{"tldr":259,"method":260,"finding":261,"direction":95,"opportunity":262},"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":265,"title":266,"url":267,"summary":268,"summary_zh":269,"content":9,"source_name":270,"source_url":267,"published_at":271,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":272,"score_detail":273,"sources":275,"tags":277,"primary_topic":280,"secondary_topics":285,"classification":287,"search_phrases":307,"slug":310,"view_count":21,"doi":311,"paper":312,"created_at":337},4993,"Effects of within-season temporal aggregation of weather variables on performance and interpretability in rice yield machine learning models","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-75646-2","While crop responses are inherently stage-dependent, machine learning yield models often rely on weather variables aggregated over the entire growing season. This study evaluates how within-season temporal aggregation of weather variables affects both predictive performance and interpretability in machine learning-based rice yield modeling. Using CYGMA (Crop Yield Growth Model with Assumptions on Climate and Socioeconomics)-simulated potential yield as a synthetic response variable, we minimized confounding effects from observational noise and management variability, enabling a clearer evaluation of the role of within-season weather aggregation. Daily weather variables were averaged over the rice growing period after dividing each season into multiple equal-length segments, and several machine learning algorithms were trained and evaluated using temporally independent validation. Increasing within-season temporal resolution improved model performance up to a moderate level, after which gains diminished. Interpretation using SHAP (SHapley Additive exPlanations) and response diagnostics consistently indicated that yield sensitivity concentrates in specific windows of the season, with temperature-related effects emerging most strongly early in the season and humidity-related effects later. Finer temporal aggregation sharpened the identification of window-specific nonlinear and threshold-like responses that were obscured under coarse seasonal averages. Overall, moderate within-season aggregation provides a practical balance between predictive skill and mechanistic interpretability, supporting more reliable identification of phenology-relevant meteorological risks for climate-impact assessment and decision support.","尽管作物响应本质上具有阶段依赖性，但机器学习产量模型往往依赖于整个生长季聚合的天气变量。本研究评估了天气变量的季节内时间聚合如何影响基于机器学习的水稻产量建模的预测性能与可解释性。以CYGMA（Crop Yield Growth Model with Assumptions on Climate and Socioeconomics，气候与社会经济假设下的作物产量生长模型）模拟的潜在产量作为合成响应变量，我们最大限度地减少了观测噪声和管理变异性带来的混杂效应，从而能够更清晰地评估季节内天气聚合的作用。将每个生长季划分为多个等长时段后，对水稻生长期内的逐日天气变量进行平均，并采用时间独立验证对多种机器学习算法进行训练和评估。提高季节内时间分辨率可提升模型性能，直至达到中等水平，此后增益递减。利用SHAP（SHapley Additive exPlanations，沙普利加性解释）和响应诊断进行的解释一致表明，产量敏感性集中在季节的特定窗口，温度相关效应在季节早期最为显著，湿度相关效应则在后期出现。更精细的时间聚合增强了对窗口特异性非线性和阈值型响应的识别，而这些响应在粗粒度季节平均下被掩盖。总体而言，中等程度的季节内聚合在预测能力与机制可解释性之间提供了实用平衡，有助于更可靠地识别与物候相关的气象风险，以支持气候影响评估和决策支持。","Scientific Reports","2026-10-13T00:00:00Z",72,{"impact":19,"substance":113,"depth":112,"authority":18,"freshness":17,"relevant":21,"comment":274},"该研究通过CYGMA模拟数据系统评估了生长季内气象变量时间聚合对水稻产量机器学习模型性能与可解释性的影响，方法新颖、结论可靠，对农业气象风险识别与决策支持有参考价值，但属学术论文，公共影响范围有限。",[276],{"name":270,"url":267},[26,27,278,29,279],"可解释AI","水稻产量预测",{"code":281,"slug":282,"name":283,"path":284,"parent_code":204},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice",[286],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":288},{"candidates":289,"agricultural_evidence":46},[290,295,299,303],{"code":32,"score":132,"supported":46,"title":291,"abstract":292,"tags":294},[50],[49,50,293],"decision support",[27,278],{"code":281,"score":18,"supported":46,"title":296,"abstract":297,"tags":298},[282],[283,282],[],{"code":68,"score":21,"supported":14,"title":300,"abstract":301,"tags":302},[],[],[29],{"code":54,"score":21,"supported":14,"title":304,"abstract":305,"tags":306},[],[],[26],[308,309],"水稻产量 机器学习 气象","SHAP 水稻 产量预测","水稻产量机器学习气象-4993","10.1038\u002Fs41598-026-75646-2",{"doi":311,"openalex_id":313,"authors":314,"venue":270,"cited_by_count":89,"oa_url":331,"card":332,"direction":97,"ingested_from":98},"W7221657053",[315,317,319,321,323,325,328],{"name":316,"orcid":9},"Noh-Hyun Lee",{"name":318,"orcid":9},"Wonjae Jeong",{"name":320,"orcid":9},"Hyeon-Ji Yang",{"name":322,"orcid":9},"Hana Cho",{"name":324,"orcid":9},"Hyeon-Yeong Ju",{"name":326,"orcid":327},"Toshichika Iizumi","https:\u002F\u002Forcid.org\u002F0000-0002-0611-4637",{"name":329,"orcid":330},"Ki‐Tae Kim","https:\u002F\u002Forcid.org\u002F0000-0002-8249-5283","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75646-2_reference.pdf",{"tldr":333,"method":334,"finding":335,"direction":95,"opportunity":336},"评估生长季内天气变量时间聚合粒度对水稻产量机器学习模型预测与可解释性的影响。","用CYGMA模拟潜在产量作响应变量，按等长时段聚合逐日天气，训练多种机器学习模型","适度提高季内时间分辨率可提升性能，产量敏感性集中在特定窗口，温度效应偏早、湿度效应偏晚。","可探索不同作物与气候区下最优聚合粒度，并将窗口特异性气象风险嵌入气候影响评估与决策支持系统。","2026-10-15T23:30:10.483881Z",{"id":339,"title":340,"url":341,"summary":342,"summary_zh":343,"content":9,"source_name":344,"source_url":341,"published_at":345,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":346,"score_detail":347,"sources":349,"tags":351,"primary_topic":353,"secondary_topics":358,"classification":360,"search_phrases":376,"slug":379,"view_count":89,"doi":380,"paper":381,"created_at":404},4818,"Hybrid Deep Feature Extraction and Ensemble Machine Learning for Potato Leaf Disease Classification in Uncontrolled Field Conditions","https:\u002F\u002Fdoi.org\u002F10.15832\u002Fankutbd.1837896","Early and accurate diagnosis of potato leaf diseases is critical for preventing yield losses under real field conditions. This study proposes a hybrid deep feature–based machine learning framework that combines transfer learning and ensemble classifiers to automatically identify seven potato leaf disease classes from images. A total of 4,260 images from the publicly available Potato Leaf Disease Dataset were organized into seven categories: Bacteria, Virus, Pest, Fungi, Nematode, Phytophthora, and Healthy. Real-time image augmentation was performed using Keras ImageDataGenerator to improve strength to field variability. Deep feature vectors were extracted from three pre-trained convolutional neural networks (ResNet50, EfficientNetB0, and MobileNetV2), each followed by a lightweight dense head, and concatenated into 768-dimensional feature representations. After standardization, dimensionality reduction was applied via principal component analysis, and class imbalance in the training set was addressed using the SMOTE-ENN hybrid resampling method. On these deep features, 32 classical machine learning and ensemble models including SVMs, tree-based methods, boosting algorithms, multilayer perceptrons, and four ensemble schemes, were trained. Model performance was assessed by three-fold cross-validation on the training set and final evaluation on the held-out validation set using accuracy, precision, recall, and macro F1-score. Ensemble models outperformed individual classifiers. The best performance was obtained with the StackingClassifier_L2 model, which combines XGBoost, LightGBM, Random Forest, SVM with RBF kernel, and Extra Trees within a stacking framework. This model achieved an accuracy of 81.34% and a macro F1-score of 81.74% on the validation set. The proposed hybrid system, which integrates multi-backbone deep feature extraction with comprehensive classical and ensemble classifiers, provides a robust and practical solution for automatic potato leaf disease classification in uncontrolled field conditions. The results suggest that stacking-based ensembles on top of deep features can capture complex visual patterns more effectively than single models and may support decision-making in precision agriculture.","在马铃薯实际田间条件下，早期准确地诊断叶片病害对于防止产量损失至关重要。本研究提出了一种基于混合深度特征的机器学习框架，该框架结合迁移学习和集成分类器，从图像中自动识别七类马铃薯叶片病害。来自公开可用的马铃薯叶片病害数据集的4,260张图像被分为七个类别：细菌（Bacteria）、病毒（Virus）、害虫（Pest）、真菌（Fungi）、线虫（Nematode）、疫霉（Phytophthora）和健康（Healthy）。使用Keras ImageDataGenerator进行实时图像增强，以提高对田间变异的鲁棒性。从三个预训练的卷积神经网络（ResNet50、EfficientNetB0和MobileNetV2）中提取深度特征向量，每个网络后接一个轻量级密集头，并将其拼接为768维特征表示。经过标准化后，通过主成分分析进行降维，并使用SMOTE-ENN混合重采样方法解决训练集中的类别不平衡问题。在这些深度特征上，训练了32种经典机器学习和集成模型，包括支持向量机、基于树的方法、提升算法、多层感知机以及四种集成方案。通过训练集上的三折交叉验证和保留验证集上的最终评估，使用准确率、精确率、召回率和宏F1分数评估模型性能。集成模型优于单个分类器。最佳性能由StackingClassifier_L2模型获得，该模型在堆叠框架内结合了XGBoost、LightGBM、随机森林、带RBF核的支持向量机和极端随机树。该模型在验证集上达到了81.34%的准确率和81.74%的宏F1分数。所提出的混合系统将多骨干深度特征提取与全面的经典分类器和集成分类器相结合，为无控制田间条件下的马铃薯叶片病害自动分类提供了一种稳健且实用的解决方案。结果表明，基于深度特征的堆叠集成能够比单一模型更有效地捕捉复杂的视觉模式，并可能支持精准农业中的决策。","Tarım Bilimleri Dergisi","2026-10-12T00:00:00Z",65,{"impact":17,"substance":113,"depth":139,"authority":115,"freshness":17,"relevant":21,"comment":348},"方法组合较全面、数据规模明确，但验证集准确率仅81.34%，属细分领域技术进展，可作主题聚合素材而非头条精选。",[350],{"name":344,"url":341},[26,27,352,28,120],"马铃薯",{"code":354,"slug":355,"name":356,"path":357,"parent_code":204},"A5","tubers-grains","薯类与杂粮","\u002Ftopic\u002Ffield-crops\u002Ftubers-grains",[359],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":361},{"candidates":362,"agricultural_evidence":46},[363,367,372],{"code":32,"score":139,"supported":46,"title":364,"abstract":365,"tags":366},[50],[49,50],[27,28],{"code":354,"score":211,"supported":46,"title":368,"abstract":370,"tags":371},[369],"potato",[352,369],[352],{"code":54,"score":17,"supported":46,"title":373,"abstract":374,"tags":375},[],[120,142],[26,120],[377,378],"马铃薯 叶片病害 深度学习","Potato Leaf Disease Dataset 集成学习","马铃薯叶片病害深度学习-4818","10.15832\u002Fankutbd.1837896",{"doi":380,"openalex_id":382,"authors":383,"venue":344,"cited_by_count":89,"oa_url":398,"card":399,"direction":97,"ingested_from":98},"W7221546496",[384,387,389,392,395],{"name":385,"orcid":386},"Cihan Ünal","https:\u002F\u002Forcid.org\u002F0000-0002-5255-4078",{"name":388,"orcid":9},"Cengizhan Tumer",{"name":390,"orcid":391},"Elham Tahsin Yasin","https:\u002F\u002Forcid.org\u002F0000-0003-3246-6000",{"name":393,"orcid":394},"İlkay Çınar","https:\u002F\u002Forcid.org\u002F0000-0003-0611-3316",{"name":396,"orcid":397},"Murat Köklü","https:\u002F\u002Forcid.org\u002F0000-0002-2737-2360","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5487015",{"tldr":400,"method":401,"finding":402,"direction":95,"opportunity":403},"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。","ResNet50\u002FEfficientNetB0\u002FMobileNetV2特征拼接+","StackingClassifier_L2最优，验证集准确率81.34%、宏F1为81.74%。","可探索轻量化模型与多模态数据融合，提升非受控田间小样本病害识别鲁棒性。","2026-10-13T23:30:16.021066Z",{"id":406,"title":407,"url":408,"summary":409,"summary_zh":410,"content":9,"source_name":344,"source_url":408,"published_at":345,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":411,"score_detail":412,"sources":415,"tags":417,"primary_topic":419,"secondary_topics":420,"classification":422,"search_phrases":438,"slug":441,"view_count":89,"doi":442,"paper":443,"created_at":458},4802,"A Privacy-Preserving Federated Learning Framework for Paddy Leaf Disease Detection Using an Enhanced Pretrained Novel EfficientNet-Lite4 Model","https:\u002F\u002Fdoi.org\u002F10.15832\u002Fankutbd.1837705","The correct and prompt detection of leaf diseases in paddy crops remains essential for protecting agricultural output and yield stability and reducing major losses that occur during severe disease outbreaks. The widespread use of conventional centralized machine learning techniques becomes impractical for real-world agricultural applications, as their effectiveness diminishes under three main conditions: data privacy limitations, delayed transmission of information, along with reduced performance accuracy from non-independent and identically distributed (non-IID) data properties across different agro-climatic regions. This research develops a decentralized diagnostic system which combines Federated Learning with an Enhanced Pretrained Novel EfficientNet-Lite4 model designed for paddy leaf disease classification. The Paddy Doctor dataset served as training material for the system which contained 16,225 RGB images that classified 12 disease types alongside healthy categories. The proposed model reached 98.7% classification accuracy and 98.5% precision and 98.6% F1-score while maintaining a final loss of 0.21 after 100 communication rounds. The system underwent field testing which demonstrated its ability to generate accurate diagnostic results in real time in various working conditions. The study demonstrates how privacy preserving AI-based paddy disease management works in practice and recommends local agricultural institutions to build decentralized learning systems. The suggested system will give farmers direct access to field level decision support and facilitate the creation of sustainable digital pest management systems required to ensure food supply.","正确且及时地检测水稻作物叶片病害，对于保护农业产出和产量稳定、减少病害严重暴发期间发生的重大损失仍然至关重要。传统集中式机器学习技术的广泛使用在现实农业应用中变得不切实际，因为在三种主要条件下其有效性会下降：数据隐私限制、信息传输延迟，以及不同农业气候区域间非独立同分布（non-IID）数据特性导致的性能准确率降低。本研究开发了一种去中心化诊断系统，将联邦学习与一种增强的预训练新型高效网络模型EfficientNet-Lite4相结合，用于水稻叶片病害分类。Paddy Doctor数据集作为该系统的训练材料，包含16,225张RGB图像，涵盖12种病害类型以及健康类别。所提出的模型在100轮通信后达到了98.7%的分类准确率、98.5%的精确率和98.6%的F1分数，同时最终损失保持在0.21。该系统经过实地测试，证明其能够在各种工作条件下实时生成准确的诊断结果。该研究展示了基于隐私保护人工智能的水稻病害管理如何在实践中运作，并建议地方农业机构构建去中心化学习系统。所建议的系统将为农民提供直接的田间决策支持，并促进建立确保粮食供应所需的可持续数字病虫害管理系统。",80,{"impact":112,"substance":413,"depth":112,"authority":115,"freshness":145,"relevant":21,"comment":414},22,"将联邦学习与轻量级EfficientNet-Lite4结合用于水稻叶病识别，兼顾隐私保护与98.7%高精度，对智慧农业落地有参考价值。",[416],{"name":344,"url":408},[26,27,283,28,418],"联邦学习",{"code":281,"slug":282,"name":283,"path":284,"parent_code":204},[421],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":423},{"candidates":424,"agricultural_evidence":46},[425,430,434],{"code":281,"score":211,"supported":46,"title":426,"abstract":428,"tags":429},[427],"paddy",[283,427],[283],{"code":32,"score":19,"supported":46,"title":431,"abstract":432,"tags":433},[],[49,50,293],[27,28,418],{"code":54,"score":21,"supported":14,"title":435,"abstract":436,"tags":437},[],[],[26],[439,440],"Paddy Doctor 数据集 水稻病害","EfficientNet-Lite4 水稻叶片病害","PaddyDoctor数据集水稻病害-4802","10.15832\u002Fankutbd.1837705",{"doi":442,"openalex_id":444,"authors":445,"venue":344,"cited_by_count":89,"oa_url":452,"card":453,"direction":97,"ingested_from":98},"W7221522859",[446,449],{"name":447,"orcid":448},"B Johnson","https:\u002F\u002Forcid.org\u002F0009-0009-8281-4051",{"name":450,"orcid":451},"Chandrakumar Thangavel","https:\u002F\u002Forcid.org\u002F0000-0002-1186-5988","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5486058",{"tldr":454,"method":455,"finding":456,"direction":95,"opportunity":457},"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。","联邦学习+预训练EfficientNet-Lite4，用Paddy Doctor","12类病害分类准确率达98.7%，100轮通信后损失0.21，支持实时诊断。","可探索非IID数据下联邦聚合优化与轻量化模型在边缘设备的部署研究。","2026-10-13T23:30:08.809781Z",{"id":460,"title":461,"url":462,"summary":463,"summary_zh":464,"content":9,"source_name":465,"source_url":462,"published_at":466,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":467,"score_detail":468,"sources":470,"tags":472,"primary_topic":474,"secondary_topics":475,"classification":476,"search_phrases":487,"slug":490,"view_count":76,"doi":491,"paper":492,"created_at":504},4741,"Hybrid CNN-LSTM Techniques for Plant Leaf Disease Detection and Farmer-Based Agricultural Practice Analysis in Western Tamil Nadu","https:\u002F\u002Fdoi.org\u002F10.55164\u002Fajstr.v29i11.262967","Plant diseases greatly affect crop productivity and agricultural sustainability, making early and accurate disease detection essential. This study investigates hybrid deep learning techniques for plant leaf disease detection and classification, focusing on combining Convolutional Neural Networks and Long Short-Term Memory networks. A review of existing studies revealed that the CNN-LSTM hybrid model achieved over 98% accuracy, outperforming several other hybrid approaches. In addition, a survey of 140 farmers from the western districts of Tamil Nadu analyzed farming practices, disease identification methods, and management techniques. The findings indicate that younger farmers are gradually adopting modern agricultural technologies, while older farmers continue to rely on traditional practices. The study highlights the effectiveness of CNN-LSTM models for smart agriculture and emphasizes the importance of farmer awareness and technology adoption for sustainable crop management.","植物病害严重影响作物生产力和农业可持续性，因此早期、准确的病害检测至关重要。本研究探讨了用于植物叶片病害检测与分类的混合深度学习方法，重点结合卷积神经网络（Convolutional Neural Network, CNN）与长短期记忆网络（Long Short-Term Memory, LSTM）。对现有研究的综述表明，CNN-LSTM混合模型达到了98%以上的准确率，优于其他多种混合方法。此外，一项针对泰米尔纳德邦西部地区140名农民的调查分析了耕作实践、病害识别方法和管理技术。研究结果表明，年轻农民正逐步采用现代农业技术，而年长农民仍继续依赖传统做法。本研究凸显了CNN-LSTM模型在智慧农业中的有效性，并强调了农民意识和技术采用对可持续作物管理的重要性。","ASEAN Journal of Scientific and Technological Reports","2026-10-11T00:00:00Z",67,{"impact":19,"substance":112,"depth":139,"authority":19,"freshness":145,"relevant":21,"comment":469},"CNN-LSTM混合模型病害识别与印度农户技术采纳调研结合，方法有参考价值但属区域性研究，影响力有限。",[471],{"name":465,"url":462},[26,27,135,28,473],"农户调研",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":477},{"candidates":478,"agricultural_evidence":46},[479,483],{"code":32,"score":145,"supported":46,"title":480,"abstract":481,"tags":482},[],[135,136],[27,135,28],{"code":54,"score":55,"supported":46,"title":484,"abstract":485,"tags":486},[],[26,58],[26],[488,489],"CNN-LSTM 植物叶片病害检测","泰米尔纳德邦 农户 农业技术采纳","CNN-LSTM植物叶片病害检测-4741","10.55164\u002Fajstr.v29i11.262967",{"doi":491,"openalex_id":493,"authors":494,"venue":465,"cited_by_count":89,"oa_url":462,"card":499,"direction":97,"ingested_from":98},"W7221445876",[495,497],{"name":496,"orcid":9},"Valarmathi Palaniappan",{"name":498,"orcid":9},"Laurence Aroquiaraj",{"tldr":500,"method":501,"finding":502,"direction":95,"opportunity":503},"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。","CNN-LSTM混合深度学习模型；对140名农户进行问卷调查与统计分析。","CNN-LSTM模型病害识别准确率超98%；年轻农户更愿采纳新技术，年长农户仍依赖传统方式。","可探索轻量化CNN-LSTM模型在移动端的部署，并结合农户技术接受度差异设计差异化推广策略。","2026-10-12T23:30:07.775839Z"]