[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1551":3,"related-1551":73},{"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":22,"tags":24,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":67,"slug":70,"view_count":71,"doi":8,"paper":8,"created_at":72},1551,"神农大模型 4.0 路演落地湘湖实验室：病虫害识别拓展至 1016 种、准确率超 93%，覆盖全国 31 省超 500 万亩农田","https:\u002F\u002Fwww.xiaoshan.gov.cn\u002Fcol\u002Fcol1302903\u002Fart\u002F2026\u002Fart_51407f23df244519a64db25c4b0ea0b5.html","近日，\"神农大模型\"路演暨场景应用交流会在杭州市萧山区湘湖实验室举办。最新发布的神农大模型 4.0 病虫害识别种类拓展至 1016 种、识别准确率超 93%，可适配大田、设施农业、畜禽养殖等多元场景；同步推出的\"神农·农业世界模型 1.0\"能搭建数字孪生仿真田块，不用下地试种便可模拟预判施肥、温控等对作物长势的影响。这套农业\"超级大脑\"已覆盖全国 31 个省份超 500 万亩农田，惠及百万农户。",null,"杭州市萧山区人民政府","2026-09-03T00:00:00Z","报道",10,false,80,{"impact":16,"substance":17,"depth":18,"authority":12,"freshness":19,"relevant":20,"comment":21},25,22,15,8,1,"神农大模型4.0覆盖全国31省500万亩农田，病虫害识别准确率超93%，具有产业级影响，信息增量大，信源为地方政府官网，时效性佳。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","农业人工智能","大模型","数字孪生","病虫害识别",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":38,"locked":13,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":44},[43,48,54,61],{"code":31,"score":16,"supported":44,"title":45,"abstract":46,"tags":47},true,[27,29],[27,29],[26,27,29],{"code":49,"score":50,"supported":44,"title":51,"abstract":52,"tags":53},"I1",5,[],[28],[28,25],{"code":55,"score":56,"supported":44,"title":57,"abstract":58,"tags":60},"C",3,[],[59],"畜禽",[],{"code":62,"score":56,"supported":44,"title":63,"abstract":64,"tags":66},"F3",[],[65],"施肥",[],[68,69],"农业人工智能 病虫害识别 数字孪生 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数字孪生智慧农业-1551",4,"2026-09-04T00:05:30.291011Z",{"total":74,"page":20,"page_size":74,"items":75},6,[76,128,188,272,355,422],{"id":77,"title":78,"url":79,"summary":80,"summary_zh":8,"content":8,"source_name":81,"source_url":8,"published_at":82,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":83,"score_detail":84,"sources":90,"tags":92,"primary_topic":95,"secondary_topics":96,"classification":97,"search_phrases":124,"slug":126,"view_count":56,"doi":8,"paper":8,"created_at":127},2815,"WAFI2026人工智能与农业论坛:中国方案助力农民种得好、种得起、种得稳、种得赚","https:\u002F\u002Fbaike.baidu.com\u002Fitem\u002F2026%E4%B8%96%E7%95%8C%E5%86%9C%E4%B8%9A%E7%A7%91%E6%8A%80%E5%88%9B%E6%96%B0%E5%A4%A7%E4%BC%9A\u002F68651190","9月16日WAFI2026举行\"人工智能与农业论坛\",中国农业大学全球食物经济与政策研究院院长樊胜根提出\"人工智能如何造福农民\"议题。论坛介绍神农大模型(2023年1.0版到2025年3.0版,3.0版为\"小麦育种智能助手\",可识别70类、600余种病虫害,已在非洲落地)、农业食物经济与政策AI模型、北大荒\"未来农场\"平台(覆盖111个农场、接入8.4万台智能装备、为60万种植户服务)、北京市\"智京园\"智慧设施管控技术体系等案例。","百度百科 \u002F 中国农业大学","2026-09-16T00:00:00Z",86,{"impact":85,"substance":86,"depth":87,"authority":88,"freshness":19,"relevant":20,"comment":89},26,21,17,14,"国际论坛上集中展示神农大模型、北大荒未来农场等中国AI农业方案，案例数据具体、信源权威，时效性强，值得进入每日精选。",[91],{"name":81,"url":79},[25,26,93,94,27,29],"未来农场","智能育种",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":98},{"candidates":99,"agricultural_evidence":44},[100,104,110,115,120],{"code":31,"score":74,"supported":44,"title":101,"abstract":102,"tags":103},[],[27],[26,27,29],{"code":105,"score":56,"supported":44,"title":106,"abstract":107,"tags":109},"A2",[],[108],"小麦",[],{"code":111,"score":20,"supported":13,"title":112,"abstract":113,"tags":114},"F2",[],[],[94],{"code":116,"score":20,"supported":13,"title":117,"abstract":118,"tags":119},"F6",[],[],[93],{"code":49,"score":20,"supported":13,"title":121,"abstract":122,"tags":123},[],[],[25],[125,69],"农业人工智能 病虫害识别 智慧农业 智能育种","农业人工智能病虫害识别智慧农业智能育种-2815","2026-09-18T00:03:24.639818Z",{"id":129,"title":130,"url":131,"summary":132,"summary_zh":133,"content":8,"source_name":134,"source_url":131,"published_at":135,"category":136,"cover_url":8,"hotness":12,"is_selected":13,"score":137,"score_detail":138,"sources":142,"tags":144,"primary_topic":147,"secondary_topics":148,"classification":149,"search_phrases":169,"slug":172,"view_count":173,"doi":174,"paper":175,"created_at":187},5315,"PLANT-GPK v4.0: Multi-Stage Plant Phenotyping with Time-Discovery and Harvest Prediction","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23461121","This paper presents a comprehensive extension to the PLANT-GPK framework, demonstrating its robust capability to analyze plants across entirely different developmental stages from single images. By formulating an inverse problem that simultaneously optimizes physiological parameters and the hidden time dimension (t), the model successfully discovered the exact age of two distinct wheat samples: a vegetative (green) stage and a senescent (golden) stage. The framework bridges computer vision, spatial heterogeneity analysis, and ODE-based physics to provide a complete digital twin capable of forecasting the exact day of harvest regardless of the plant's current state.","本文对PLANT-GPK框架进行了全面扩展，展示了其仅凭单张图像即可分析处于完全不同发育阶段植物的稳健能力。通过构建一个同时优化生理参数与隐藏时间维度（t）的反问题，该模型成功推断出两个不同小麦样本的精确年龄：一个处于营养期（绿色阶段），另一个处于衰老期（金色阶段）。该框架融合了计算机视觉、空间异质性分析以及基于常微分方程（ODE）的物理建模，提供了一个完整的数字孪生，无论植物当前处于何种状态，均能预测确切的收获日期。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-19T00:00:00Z","论文",81,{"impact":139,"substance":17,"depth":139,"authority":140,"freshness":12,"relevant":20,"comment":141},18,13,"该研究提出从单张图像反演植物生育阶段与收获日的数字孪生框架，方法新颖且具产业应用潜力，值得进入每日精选。",[143],{"name":134,"url":131},[25,26,28,145,146],"植物表型","收获预测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":150},{"candidates":151,"agricultural_evidence":44},[152,157,164],{"code":49,"score":19,"supported":44,"title":153,"abstract":154,"tags":156},[],[28,155],"digital twin",[28,25],{"code":31,"score":158,"supported":44,"title":159,"abstract":160,"tags":163},7,[],[161,162],"计算机视觉","computer vision",[26],{"code":105,"score":74,"supported":44,"title":165,"abstract":166,"tags":168},[],[108,167],"wheat",[],[170,171],"PLANT-GPK 植物表型","小麦 收获期 预测","PLANT-GPK植物表型-5315",0,"10.5281\u002Fzenodo.23461121",{"doi":174,"openalex_id":176,"authors":177,"venue":134,"cited_by_count":173,"oa_url":131,"card":180,"direction":184,"ingested_from":186},"W7222875391",[178],{"name":179,"orcid":8},"MOHAMMED TAHAR SAIM",{"tldr":181,"method":182,"finding":183,"direction":184,"opportunity":185},"扩展PLANT-GPK框架，从单张图像反演植物生理参数与隐藏时间维度，实现跨生育期表型分析与收获期预","基于ODE物理模型与逆问题优化，联合估计生理参数和隐藏时间t，用两个不同生育期小","模型能准确发现两个不同生育期小麦样本的精确年龄，并预测确切收获日期。","农业遥感与作物表型","可将单图时间反演扩展到多作物多生育期，结合时序遥感构建大尺度数字孪生与收获期预报。","openalex","2026-10-19T23:30:19.267920Z",{"id":189,"title":190,"url":191,"summary":192,"summary_zh":193,"content":8,"source_name":194,"source_url":191,"published_at":195,"category":136,"cover_url":8,"hotness":12,"is_selected":13,"score":196,"score_detail":197,"sources":201,"tags":203,"primary_topic":206,"secondary_topics":207,"classification":208,"search_phrases":253,"slug":256,"view_count":173,"doi":257,"paper":258,"created_at":271},5232,"Current applications of artificial intelligence for advancing agriculture, sustainability and climate mitigation","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44163-026-02540-1","Artificial intelligence (AI) is reshaping agriculture by enabling data-driven decision-making across crop production systems and supporting the transition toward more sustainable, resilient, and climate-smart farming. This review critically synthesizes advances in AI applications spanning precision irrigation, nutrient and soil management, yield prediction, pest and disease monitoring, computer vision, autonomous agricultural robotics, and large language model (LLM)-based decision support systems. Beyond summarizing technological developments, the review evaluates their practical deployment, emphasizing that successful agricultural AI depends not only on predictive accuracy but also on model generalizability, explainability, economic feasibility, interoperability, and integration with existing agronomic knowledge. Emerging technologies including Vision Transformers, foundation models, Digital Twins, multimodal AI, Retrieval-Augmented Generation, and federated and continual learning—are discussed as important directions for overcoming limitations related to data scarcity, environmental variability, and scalable field implementation. The review highlights that responsible AI adoption requires transparent governance frameworks addressing data ownership, privacy, algorithmic fairness, cybersecurity, and equitable access, particularly for smallholder farming systems. Although numerous AI models report outstanding performance, widespread agricultural adoption remains constrained by cross-region validation, fragmented datasets, infrastructure requirements, and insufficient real-world deployment. Future progress will depend on multidisciplinary collaboration integrating advances in artificial intelligence, sensing technologies, robotics, crop science, and agricultural engineering to develop robust, trustworthy, and farmer-centered decision-support systems. By critically evaluating technological innovations and implementation challenges, this review provides a framework for understanding the current state of AI in agriculture while identifying research priorities that will shape the next generation of sustainable and climate-resilient agricultural systems.","人工智能（AI）正在重塑农业，通过在作物生产系统中实现数据驱动的决策，并支持向更可持续、更具韧性和气候智能型农业转型。本综述批判性地综合了AI应用的进展，涵盖精准灌溉、养分与土壤管理、产量预测、病虫害监测、计算机视觉、自主农业机器人以及基于大语言模型（LLM）的决策支持系统。除总结技术发展外，本综述还评估了其实际部署情况，强调农业AI的成功不仅取决于预测准确性，还取决于模型泛化能力、可解释性、经济可行性、互操作性以及与现有农学知识的整合。文中讨论了包括视觉Transformer、基础模型、数字孪生、多模态AI、检索增强生成以及联邦学习和持续学习在内的新兴技术，将其视为克服数据稀缺、环境变异性和田间可扩展实施等局限的重要方向。本综述强调，负责任的AI采用需要透明的治理框架，以解决数据所有权、隐私、算法公平性、网络安全和公平获取等问题，尤其是对小农耕作系统而言。尽管众多AI模型报告了卓越的性能，但农业领域的广泛采用仍受限于跨区域验证、碎片化数据集、基础设施要求以及现实世界部署不足。未来的进展将取决于多学科协作，整合人工智能、传感技术、机器人技术、作物科学和农业工程领域的进展，以开发稳健、可信且以农民为中心的决策支持系统。通过批判性评估技术创新和实施挑战，本综述提供了一个理解AI在农业中现状的框架，同时确定了将塑造下一代可持续和气候韧性农业系统的研究优先事项。","Discover Artificial Intelligence","2026-10-17T00:00:00Z",83,{"impact":17,"substance":17,"depth":139,"authority":198,"freshness":199,"relevant":20,"comment":200},12,9,"该综述系统梳理了AI在农业中的应用与落地瓶颈，覆盖精准灌溉、机器人、大模型决策支持等方向，信息密度高，但属综述类论文，产业级突破性有限，可入精选。",[202],{"name":194,"url":191},[25,26,204,28,205],"大语言模型","气候智慧农业",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":209},{"candidates":210,"agricultural_evidence":44},[211,219,226,233,237,242,248],{"code":31,"score":212,"supported":44,"title":213,"abstract":215,"tags":218},19,[214],"artificial intelligence",[161,214,162,216,217],"large language model","decision support",[26,204],{"code":220,"score":74,"supported":44,"title":221,"abstract":222,"tags":225},"H2",[],[223,224],"土壤","soil",[],{"code":227,"score":74,"supported":44,"title":228,"abstract":229,"tags":232},"H3",[],[230,231],"灌溉","irrigation",[],{"code":49,"score":50,"supported":44,"title":234,"abstract":235,"tags":236},[],[28],[28,25],{"code":116,"score":56,"supported":44,"title":238,"abstract":239,"tags":241},[],[240],"农业机器人",[],{"code":243,"score":56,"supported":44,"title":244,"abstract":245,"tags":247},"G2",[],[246],"smallholder",[],{"code":249,"score":20,"supported":13,"title":250,"abstract":251,"tags":252},"H7",[],[],[205],[254,255],"气候智慧农业 AI","农业人工智能 气候智慧农业 大语言模型 数字孪生","气候智慧农业AI-5232","10.1007\u002Fs44163-026-02540-1",{"doi":257,"openalex_id":259,"authors":260,"venue":194,"cited_by_count":173,"oa_url":191,"card":264,"direction":270,"ingested_from":186},"W7222823256",[261],{"name":262,"orcid":263},"Mostafa Farajpour","https:\u002F\u002Forcid.org\u002F0000-0003-4223-502X",{"tldr":265,"method":266,"finding":267,"direction":268,"opportunity":269},"综述农业人工智能在精准灌溉、产量预测、病虫害监测等应用及落地挑战。","文献综述，覆盖视觉Transformer、基础模型、数字孪生、多模态AI与联邦学","农业AI落地受限于跨区域验证、数据碎片化与基础设施，需兼顾可解释性与公平治理。","农业人工智能与决策模型","可研究小农户场景下跨区域可泛化、可解释且低成本的农业大模型与联邦学习部署。","智慧农业 \u002F 农业物联网","2026-10-18T23:30:07.666127Z",{"id":273,"title":274,"url":275,"summary":276,"summary_zh":277,"content":8,"source_name":278,"source_url":275,"published_at":279,"category":136,"cover_url":8,"hotness":12,"is_selected":13,"score":280,"score_detail":281,"sources":284,"tags":286,"primary_topic":289,"secondary_topics":295,"classification":297,"search_phrases":315,"slug":318,"view_count":20,"doi":319,"paper":320,"created_at":354},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",76,{"impact":282,"substance":86,"depth":87,"authority":140,"freshness":199,"relevant":20,"comment":283},16,"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。",[285],{"name":278,"url":275},[25,26,287,29,288],"深度学习","烟草",{"code":290,"slug":291,"name":292,"path":293,"parent_code":294},"A6","cash-crops","棉麻糖与特色经作","\u002Ftopic\u002Ffield-crops\u002Fcash-crops","A",[296],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":298},{"candidates":299,"agricultural_evidence":44},[300,305,310],{"code":290,"score":18,"supported":44,"title":301,"abstract":303,"tags":304},[302],"tobacco",[288,302],[288],{"code":31,"score":199,"supported":44,"title":306,"abstract":307,"tags":309},[],[287,308],"deep learning",[26,287,29],{"code":49,"score":158,"supported":44,"title":311,"abstract":312,"tags":314},[],[25,313],"smart agriculture",[25],[316,317],"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":319,"openalex_id":321,"authors":322,"venue":278,"cited_by_count":173,"oa_url":348,"card":349,"direction":270,"ingested_from":186},"W7222603139",[323,325,327,329,331,333,335,337,340,343,346],{"name":324,"orcid":8},"Shun He",{"name":326,"orcid":8},"Sifan Li",{"name":328,"orcid":8},"Xinjian Li",{"name":330,"orcid":8},"Qianqian Luo",{"name":332,"orcid":8},"Dong Sufen",{"name":334,"orcid":8},"Lu Lei",{"name":336,"orcid":8},"Xingjiang Chen",{"name":338,"orcid":339},"Liuti Cai","https:\u002F\u002Forcid.org\u002F0000-0002-9151-8002",{"name":341,"orcid":342},"Jian‐Yu Meng","https:\u002F\u002Forcid.org\u002F0000-0002-5796-1086",{"name":344,"orcid":345},"Ning Lu","https:\u002F\u002Forcid.org\u002F0000-0003-0214-5038",{"name":347,"orcid":8},"Hancheng Wang","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1871587\u002Fpdf",{"tldr":350,"method":351,"finding":352,"direction":268,"opportunity":353},"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":356,"title":357,"url":358,"summary":359,"summary_zh":360,"content":8,"source_name":361,"source_url":358,"published_at":362,"category":136,"cover_url":8,"hotness":12,"is_selected":13,"score":363,"score_detail":364,"sources":366,"tags":368,"primary_topic":371,"secondary_topics":372,"classification":373,"search_phrases":398,"slug":401,"view_count":402,"doi":403,"paper":404,"created_at":421},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。","International Journal of Creative and Open Research in Engineering and Management","2026-10-14T00:00:00Z",48,{"impact":19,"substance":88,"depth":198,"authority":74,"freshness":19,"relevant":20,"comment":365},"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。",[367],{"name":361,"url":358},[25,26,29,369,370],"农业气象","作物推荐",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":374},{"candidates":375,"agricultural_evidence":44},[376,383,387,394],{"code":31,"score":377,"supported":44,"title":378,"abstract":379,"tags":382},11,[],[380,381,214],"机器学习","machine learning",[26,29],{"code":49,"score":158,"supported":44,"title":384,"abstract":385,"tags":386},[],[25,313],[25],{"code":388,"score":74,"supported":44,"title":389,"abstract":390,"tags":393},"H8",[],[391,392],"可持续农业","sustainable agriculture",[],{"code":249,"score":20,"supported":13,"title":395,"abstract":396,"tags":397},[],[],[369],[399,400],"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":403,"openalex_id":405,"authors":406,"venue":361,"cited_by_count":173,"oa_url":415,"card":416,"direction":270,"ingested_from":186},"W7221803029",[407,409,411,413],{"name":408,"orcid":8},"Bhuvana H P",{"name":410,"orcid":8},"Kavya C M",{"name":412,"orcid":8},"Manjula S",{"name":414,"orcid":8},"Priyanka A","https:\u002F\u002Fijcope.org\u002Fwp-content\u002Fuploads\u002F2026\u002F10\u002FSmart-Crop-Advisory-System.pdf",{"tldr":417,"method":418,"finding":419,"direction":268,"opportunity":420},"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。","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。","2026-10-15T23:30:09.048921Z",{"id":423,"title":424,"url":425,"summary":426,"summary_zh":427,"content":8,"source_name":428,"source_url":425,"published_at":429,"category":136,"cover_url":8,"hotness":12,"is_selected":13,"score":430,"score_detail":431,"sources":434,"tags":436,"primary_topic":439,"secondary_topics":444,"classification":446,"search_phrases":463,"slug":466,"view_count":173,"doi":467,"paper":468,"created_at":491},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":19,"substance":432,"depth":282,"authority":140,"freshness":19,"relevant":20,"comment":433},20,"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。",[435],{"name":428,"url":425},[25,26,437,29,438],"马铃薯","精准农业",{"code":440,"slug":441,"name":442,"path":443,"parent_code":294},"A5","tubers-grains","薯类与杂粮","\u002Ftopic\u002Ffield-crops\u002Ftubers-grains",[445],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":447},{"candidates":448,"agricultural_evidence":44},[449,453,458],{"code":31,"score":282,"supported":44,"title":450,"abstract":451,"tags":452},[381],[380,381],[26,29],{"code":440,"score":18,"supported":44,"title":454,"abstract":456,"tags":457},[455],"potato",[437,455],[437],{"code":49,"score":19,"supported":44,"title":459,"abstract":460,"tags":462},[],[438,461],"precision agriculture",[25,438],[464,465],"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":467,"openalex_id":469,"authors":470,"venue":428,"cited_by_count":173,"oa_url":485,"card":486,"direction":270,"ingested_from":186},"W7221546496",[471,474,476,479,482],{"name":472,"orcid":473},"Cihan Ünal","https:\u002F\u002Forcid.org\u002F0000-0002-5255-4078",{"name":475,"orcid":8},"Cengizhan Tumer",{"name":477,"orcid":478},"Elham Tahsin Yasin","https:\u002F\u002Forcid.org\u002F0000-0003-3246-6000",{"name":480,"orcid":481},"İlkay Çınar","https:\u002F\u002Forcid.org\u002F0000-0003-0611-3316",{"name":483,"orcid":484},"Murat Köklü","https:\u002F\u002Forcid.org\u002F0000-0002-2737-2360","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5487015",{"tldr":487,"method":488,"finding":489,"direction":268,"opportunity":490},"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"]