[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4177":3,"related-4177":116},{"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":86,"slug":89,"view_count":75,"doi":90,"paper":91,"created_at":115},4177,"The Changing Landscape of Plant Disease Detection: The Role of Artificial Intelligence","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fjsrr\u002F2026\u002Fv32i104569","Plant diseases constrain agricultural productivity and food security, while conventional diagnosis depends heavily on visible symptoms and specialist expertise, limiting rapid and scalable assessment across heterogeneous agricultural environments. Artificial intelligence (AI), particularly machine learning, deep learning, and computer vision, offers complementary approaches for automated and non-invasive disease detection. This review examines AI-based methods for image classification, object detection, image segmentation, severity assessment, and early or pre-symptomatic disease detection. It also considers the integration of AI with hyperspectral, multispectral, and thermal imaging and unmanned aerial vehicles for crop-scale monitoring and precision management. Reported studies demonstrate high performance under many experimental conditions; however, reliable field deployment remains constrained by limited or imbalanced datasets, symptom similarity, environmental variability, domain shift, computational requirements, and insufficient independent validation. The review further considers explainable artificial intelligence, edge-based computing, multimodal sensing, foundation models, vision-language models, and federated learning as emerging directions. These approaches may improve robustness, interpretability, accessibility, and scalability, but their value depends on representative field data, biological validity, rigorous external validation, and integration with plant-pathology expertise across diverse crops, cultivars, locations, seasons, and imaging conditions. AI should therefore be regarded as a complementary decision-support tool whose practical contribution depends on combining computational methods with biological knowledge and real-world agricultural observations.","植物病害制约农业生产力和粮食安全，而传统诊断高度依赖可见症状和专家经验，限制了在异质性农业环境中进行快速、可扩展的评估。人工智能（AI），特别是机器学习、深度学习和计算机视觉，为自动化、非侵入式病害检测提供了互补性方法。本文综述了基于AI的图像分类、目标检测、图像分割、严重度评估以及早期或症状前病害检测方法，并探讨了AI与高光谱、多光谱和热成像及无人机相结合用于作物尺度监测和精准管理的集成应用。已报道的研究在许多实验条件下表现出高性能；然而，可靠的田间部署仍受限于数据集有限或不平衡、症状相似性、环境变异性、域偏移、计算需求以及独立验证不足等问题。本文进一步探讨了可解释人工智能、边缘计算、多模态传感、基础模型、视觉语言模型和联邦学习等新兴方向。这些方法有望提升鲁棒性、可解释性、可及性和可扩展性，但其价值取决于具有代表性的田间数据、生物学有效性、严格的外部验证，以及与植物病理学专业知识在多样化作物、品种、地点、季节和成像条件下的整合。因此，AI应被视为一种互补性决策支持工具，其实际贡献取决于将计算方法与生物学知识和真实农业观测相结合。",null,"Journal of Scientific Research and Reports","2026-10-03T00:00:00Z","论文",10,false,78,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,21,13,8,1,"系统综述AI植物病害检测的方法、瓶颈与前沿方向，学术价值高但属综述性论文，产业落地影响有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","精准农业","遥感监测","植物病害检测",{"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":45},[44,57,65,73,80],{"code":32,"score":17,"supported":45,"title":46,"abstract":48,"tags":56},true,[47],"artificial intelligence",[49,50,51,52,53,54,47,55],"目标检测","深度学习","机器学习","计算机视觉","machine learning","deep learning","computer vision",[27],{"code":58,"score":59,"supported":45,"title":60,"abstract":61,"tags":64},"I3",7,[],[62,63],"multispectral","hyperspectral",[29],{"code":66,"score":67,"supported":45,"title":68,"abstract":69,"tags":72},"A8",6,[],[70,71],"粮食安全","food security",[],{"code":74,"score":75,"supported":45,"title":76,"abstract":77,"tags":79},"I5",3,[],[78],"边缘计算",[],{"code":81,"score":82,"supported":14,"title":83,"abstract":84,"tags":85},"I1",2,[],[],[26,28],[87,88],"AI 植物病害检测","无人机 高光谱 作物监测","AI植物病害检测-4177","10.9734\u002Fjsrr\u002F2026\u002Fv32i104569",{"doi":90,"openalex_id":92,"authors":93,"venue":10,"cited_by_count":107,"oa_url":6,"card":108,"direction":112,"ingested_from":114},"W7218426608",[94,96,98,101,103,105],{"name":95,"orcid":9},"Aksa Anna Shajan",{"name":97,"orcid":9},"Sumiya K. V.",{"name":99,"orcid":100},"Sainamole Kurian P.","https:\u002F\u002Forcid.org\u002F0009-0004-1992-3582",{"name":102,"orcid":9},"Pooja Naduvalath",{"name":104,"orcid":9},"Athira Suresh",{"name":106,"orcid":9},"Aswathi P. R.",0,{"tldr":109,"method":110,"finding":111,"direction":112,"opportunity":113},"综述AI在植物病害检测中的应用，涵盖分类、检测、分割及早期诊断，并指出田间部署的瓶颈与新兴方向。","综述机器学习、深度学习、计算机视觉与高光谱\u002F多光谱\u002F热成像及无人机结合的研究。","AI实验表现优异，但受数据、环境变异和验证不足限制，田间可靠部署仍困难。","农业人工智能与决策模型","可探索多模态基础模型与联邦学习在跨域田间病害检测中的鲁棒性和可解释性验证。","openalex","2026-10-04T23:30:40.159648Z",{"total":67,"page":21,"page_size":67,"items":117},[118,177,296,362,433,519],{"id":119,"title":120,"url":121,"summary":122,"summary_zh":123,"content":9,"source_name":124,"source_url":121,"published_at":125,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":126,"score_detail":127,"sources":132,"tags":134,"primary_topic":135,"secondary_topics":136,"classification":137,"search_phrases":160,"slug":163,"view_count":82,"doi":164,"paper":165,"created_at":176},4912,"Deep Learning Approaches for Plant Disease Detection: A Review of Convolutional Neural Networks and Emerging Techniques","https:\u002F\u002Fdoi.org\u002F10.56201\u002Fijcsmt.vol.11.no12.2025.pg220.231","Plant diseases pose a significant threat to global food security, crop yield, and agricultural sustainability. Traditional disease diagnosis methods, relying on expert visual inspection and laboratory analyses, are often time-consuming, labor-intensive, and costly, limiting their scalability. Recent advances in artificial intelligence, particularly deep learning, offer automated and accurate solutions for plant disease detection. This review synthesizes current research on deep learning–based approaches, focusing on convolutional neural networks (CNNs), hybrid CNN-transformer models, attention mechanisms, and generative data augmentation techniques. CNNs have demonstrated strong capabilities in extracting hierarchical features from leaf images, capturing complex patterns such as lesion shapes, color variations, and textures, thereby outperforming conventional machine learning and rule-based methods. Hybrid architectures and transformer-based models enhance feature extraction, particularly for subtle or visually similar disease symptoms. Data augmentation strategies, including generative adversarial networks (GANs), help address class imbalance and improve model generalization. Despite high reported accuracies, challenges remain, including limited dataset diversity, environmental variability, overfitting, lack of explainable AI, and computational constraints for mobile deployment. Future research should prioritize explainable models, multi-crop and multi-disease detection, lightweight architectures suitable for field applications, and integration with precision agriculture systems. Overall, deep learning–based plant disease detection presents a transformative approach for early diagnosis, timely intervention, and sustainable agricultural practices, offering the potential to reduce crop losses, enhance productivity, and contribute to global food security.","植物病害对全球粮食安全、作物产量和农业可持续性构成重大威胁。传统的病害诊断方法依赖专家视觉检查和实验室分析，往往耗时、费力且成本高昂，限制了其可扩展性。人工智能，特别是深度学习的最新进展，为植物病害检测提供了自动化且准确的解决方案。本文综述了基于深度学习方法的最新研究，重点关注卷积神经网络（CNN）、CNN-Transformer混合模型、注意力机制和生成式数据增强技术。CNN在从叶片图像中提取层次化特征方面展现出强大能力，能够捕捉病变形状、颜色变化和纹理等复杂模式，从而优于传统机器学习和基于规则的方法。混合架构和基于Transformer的模型增强了特征提取能力，尤其针对细微或视觉上相似的病害症状。数据增强策略，包括生成对抗网络（GAN），有助于解决类别不平衡问题并提高模型泛化能力。尽管已有研究报告了较高的准确率，但仍存在诸多挑战，包括数据集多样性有限、环境变异性、过拟合、缺乏可解释的人工智能以及移动端部署的计算约束。未来研究应优先关注可解释模型、多作物和多病害检测、适用于田间应用的轻量级架构以及与精准农业系统的集成。总体而言，基于深度学习的植物病害检测为早期诊断、及时干预和可持续农业实践提供了一种变革性方法，具有减少作物损失、提高生产力并促进全球粮食安全的潜力。","INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY E-ISSN","2026-10-12T00:00:00Z",75,{"impact":17,"substance":128,"depth":129,"authority":130,"freshness":20,"relevant":21,"comment":131},20,17,12,"系统综述深度学习植物病害检测的方法进展与挑战，学术价值较高但偏理论，对产业落地有参考意义。",[133],{"name":124,"url":121},[26,27,50,28,30],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":138},{"candidates":139,"agricultural_evidence":45},[140,145,150,154],{"code":32,"score":141,"supported":45,"title":142,"abstract":143,"tags":144},19,[54],[50,51,53,54,47],[27,50],{"code":81,"score":20,"supported":45,"title":146,"abstract":147,"tags":149},[],[28,148],"precision agriculture",[26,28],{"code":66,"score":67,"supported":45,"title":151,"abstract":152,"tags":153},[],[70,71],[],{"code":155,"score":75,"supported":45,"title":156,"abstract":157,"tags":159},"H8",[],[158],"可持续农业",[],[161,162],"植物病害检测 深度学习","卷积神经网络 作物病害识别","植物病害检测深度学习-4912","10.56201\u002Fijcsmt.vol.11.no12.2025.pg220.231",{"doi":164,"openalex_id":166,"authors":167,"venue":124,"cited_by_count":107,"oa_url":170,"card":171,"direction":112,"ingested_from":114},"W7221637766",[168],{"name":169,"orcid":9},"NUHU ABDULLAHI","https:\u002F\u002Fiiardjournals.org\u002Fget\u002FIJCSMT\u002FVOL. 11 NO. 12 2025\u002FDeep Learning Approaches for Plant 220-231.pdf",{"tldr":172,"method":173,"finding":174,"direction":112,"opportunity":175},"综述深度学习在植物病害检测中的应用，涵盖CNN、混合Transformer、注意力机制与生成式数据增","综述CNN、CNN-Transformer混合模型、注意力机制及GAN数据增强在","深度学习检测精度高，但仍受数据集多样性、环境变异、可解释性和移动部署算力限制。","可探索轻量化可解释模型、多作物多病害检测及与精准农业系统集成，解决田间部署难题。","2026-10-14T23:30:54.687367Z",{"id":178,"title":179,"url":180,"summary":181,"summary_zh":182,"content":9,"source_name":183,"source_url":180,"published_at":184,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":185,"score_detail":186,"sources":188,"tags":190,"primary_topic":192,"secondary_topics":193,"classification":203,"search_phrases":271,"slug":274,"view_count":107,"doi":275,"paper":276,"created_at":295},4302,"BIG DATA IN AGRONOMY: TECHNOLOGIES, APPLICATIONS, BENEFITS AND CHALLENGES","https:\u002F\u002Fdoi.org\u002F10.4238\u002Fk6hcgw77","Big Data is becoming an important component of modern agronomy by enabling the integration, processing and analysis of large, diverse and continuously generated datasets for improved agricultural decision-making. Agricultural Big Data is generated from multiple sources, including remote sensing satellites, Unmanned Aerial Vehicles (UAVs), Internet of Things (IoT) sensors, weather stations, farm machinery, field observations and historical databases. The integration of these data sources with Geographic Information Systems (GIS), cloud computing, Artificial Intelligence (AI) and Machine Learning (ML) enables the identification of spatial and temporal patterns that are difficult to detect using conventional approaches. Big Data applications in agronomy include climate and weather prediction, pest and disease management, soil health and nutrient management, irrigation scheduling, crop yield prediction, precision agriculture, crop insurance and weed management. Examples from India and other regions demonstrate the application of satellite imagery, UAV-based sensing, IoT systems, crop-growth models and machine-learning algorithms for field-scale and regional agricultural decision making. Big Data analytics can improve resource-use efficiency, support predictive and real-time monitoring, strengthen agricultural risk assessment and facilitate site-specific management. However, effective implementation is constrained by challenges such as high infrastructure and operational costs, data quality and interoperability issues, data privacy and ownership concerns, limited rural digital infrastructure, inadequate technical skills and increasing requirements for data storage and computing. Therefore, the effective integration of Big Data with agronomic knowledge and decision-support systems can contribute to more precise, efficient and sustainable agricultural production while addressing emerging challenges related to climate variability, resource scarcity and food security.","大数据正成为现代农学的重要组成部分，它能够整合、处理和分析大规模、多样化且持续生成的数据集，从而改进农业决策。农业大数据来源于多种渠道，包括遥感卫星、无人机（UAV）、物联网（IoT）传感器、气象站、农业机械、田间观测和历史数据库。将这些数据源与地理信息系统（GIS）、云计算、人工智能（AI）和机器学习（ML）相结合，能够识别传统方法难以发现的空间和时间模式。大数据在农学中的应用包括气候与天气预测、病虫害管理、土壤健康与养分管理、灌溉调度、作物产量预测、精准农业、作物保险和杂草管理。来自印度及其他地区的案例展示了卫星影像、基于无人机的遥感、物联网系统、作物生长模型和机器学习算法在田块尺度和区域农业决策中的应用。大数据分析可以提高资源利用效率，支持预测性和实时监测，加强农业风险评估，并促进因地制宜的管理。然而，有效实施受到诸多挑战的制约，如高昂的基础设施和运营成本、数据质量和互操作性问题、数据隐私和所有权担忧、农村数字基础设施有限、技术技能不足以及数据存储和计算需求不断增加。因此，将大数据与农学知识和决策支持系统有效整合，有助于实现更精准、高效和可持续的农业生产，同时应对气候变异性、资源稀缺和粮食安全等方面的新兴挑战。","Genetics and Molecular Research","2026-10-05T00:00:00Z",76,{"impact":17,"substance":128,"depth":129,"authority":19,"freshness":20,"relevant":21,"comment":187},"系统综述农业大数据技术、应用与挑战，信息密度高，对智慧农业决策有参考价值，但属综述性论文，非突破性成果。",[189],{"name":183,"url":180},[26,27,191,28,29],"农业大数据",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[194,199],{"code":195,"slug":196,"name":197,"path":198,"parent_code":36},"I4","gis","GIS与空间分析","\u002Ftopic\u002Fdigital-agriculture\u002Fgis",{"code":74,"slug":200,"name":201,"path":202,"parent_code":36},"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":204},{"candidates":205,"agricultural_evidence":45},[206,210,218,225,229,235,239,246,253,259,265],{"code":32,"score":13,"supported":45,"title":207,"abstract":208,"tags":209},[],[51,53,47],[27],{"code":195,"score":211,"supported":45,"title":212,"abstract":213,"tags":217},9,[],[214,215,216],"地理信息","GIS","geographic information",[],{"code":74,"score":211,"supported":45,"title":219,"abstract":220,"tags":224},[],[221,222,223],"物联网","internet of things","IoT",[],{"code":81,"score":20,"supported":45,"title":226,"abstract":227,"tags":228},[],[28,148],[26,28],{"code":58,"score":59,"supported":45,"title":230,"abstract":231,"tags":234},[],[232,233],"遥感","remote sensing",[29],{"code":66,"score":67,"supported":45,"title":236,"abstract":237,"tags":238},[],[70,71],[],{"code":240,"score":67,"supported":45,"title":241,"abstract":242,"tags":245},"H2",[],[243,244],"土壤","soil",[],{"code":247,"score":67,"supported":45,"title":248,"abstract":249,"tags":252},"H3",[],[250,251],"灌溉","irrigation",[],{"code":254,"score":255,"supported":45,"title":256,"abstract":257,"tags":258},"I6",4,[],[191],[191],{"code":260,"score":75,"supported":45,"title":261,"abstract":262,"tags":264},"F3",[],[263],"养分管理",[],{"code":266,"score":75,"supported":45,"title":267,"abstract":268,"tags":270},"F5",[],[269],"农业机械",[],[272,273],"农业大数据 遥感 物联网","机器学习 作物产量预测","农业大数据遥感物联网-4302","10.4238\u002Fk6hcgw77",{"doi":275,"openalex_id":277,"authors":278,"venue":183,"cited_by_count":107,"oa_url":180,"card":289,"direction":294,"ingested_from":114},"W7220374836",[279,281,283,285,287],{"name":280,"orcid":9},"Kodavali Aswini",{"name":282,"orcid":9},"Raju Marimuthu",{"name":284,"orcid":9},"Guru Gurusamy",{"name":286,"orcid":9},"Kannan Pandian",{"name":288,"orcid":9},"Anandham Rangasamy",{"tldr":290,"method":291,"finding":292,"direction":112,"opportunity":293},"综述农学大数据的技术、应用、效益与挑战，强调多源数据融合支撑精准农业决策。","整合遥感、无人机、物联网、GIS、云计算与AI\u002FML的多源农业数据分析。","大数据可提升资源效率与实时监测，但受成本、数据质量、隐私和农村数字基建制约。","可研究低成本边缘计算与数据互操作标准，破解小农户场景下大数据落地瓶颈。","智慧农业 \u002F 农业物联网","2026-10-06T23:30:11.880721Z",{"id":297,"title":298,"url":299,"summary":300,"summary_zh":301,"content":9,"source_name":302,"source_url":299,"published_at":303,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":304,"score_detail":305,"sources":309,"tags":311,"primary_topic":313,"secondary_topics":314,"classification":315,"search_phrases":347,"slug":349,"view_count":75,"doi":350,"paper":351,"created_at":361},3161,"Artificial Intelligence in Plant Disease Detection: An Introduction to Intelligent and Automated Crop Health Monitoring","https:\u002F\u002Fdoi.org\u002F10.59256\u002Fijire.20260705005","Plant diseases are a major challenge in modern agriculture, as they can significantly reduce crop yield, crop quality, and economic productivity. Traditional plant disease detection methods mainly depend on visual inspection and expert knowledge, which can be time-consuming, subjective, and difficult to apply across large agricultural fields. The rapid advancement of Artificial Intelligence (AI), particularly Machine Learning (ML), Deep Learning (DL), and Computer Vision, has created new opportunities for automated and efficient crop disease detection and crop health monitoring. AI-based plant disease detection systems can analyze plant and leaf images to identify disease-related characteristics such as leaf discoloration, spots, lesions, texture variations, and abnormal growth patterns. Advanced techniques, including Convolutional Neural Networks (CNNs), transfer learning, image processing, image segmentation, and object detection, can be used for plant disease classification and identification of affected regions with high accuracy. This chapter introduces the fundamental concepts of AI-based plant disease detection, covering image acquisition, image preprocessing, feature extraction, model development, disease classification, and performance evaluation. It also examines the applications of AI in precision agriculture, smart agriculture, mobile-based plant disease diagnosis, drone-assisted crop monitoring, IoT-enabled farming, and edge-based agricultural systems. Furthermore, the chapter discusses important challenges such as limited and imbalanced datasets, environmental variations, similar disease symptoms, model generalization, computational requirements, and the need for explainable AI in agricultural applications. Finally, emerging trends and future opportunities are discussed, with emphasis on integrating AI with IoT, remote sensing, agricultural robotics, and multimodal agricultural data. The chapter provides a foundation for understanding how Artificial Intelligence for plant disease detection can support early disease identification, reduce crop losses, optimize agricultural resources, and contribute to sustainable and intelligent farming practices.","植物病害是现代农业面临的一项重大挑战，因为它们会显著降低作物产量、作物品质和经济生产力。传统的植物病害检测方法主要依赖视觉检查和专家知识，这种方式耗时、主观性强，且难以在大规模农田中应用。人工智能（AI）的快速发展，尤其是机器学习（ML）、深度学习（DL）和计算机视觉，为自动化、高效的作物病害检测和作物健康监测创造了新的机遇。基于AI的植物病害检测系统可以分析植物和叶片图像，以识别与病害相关的特征，如叶片变色、斑点、病斑、纹理变化和异常生长模式。包括卷积神经网络（CNN）、迁移学习、图像处理、图像分割和目标检测在内的先进技术，可用于植物病害分类和受影响区域的高精度识别。本章介绍了基于AI的植物病害检测的基本概念，涵盖图像采集、图像预处理、特征提取、模型开发、病害分类和性能评估。本章还探讨了AI在精准农业、智慧农业、基于移动端的植物病害诊断、无人机辅助作物监测、物联网（IoT）赋能农业和边缘农业系统中的应用。此外，本章讨论了重要挑战，如数据集有限且不平衡、环境变化、相似病害症状、模型泛化、计算需求，以及农业应用中可解释AI的需求。最后，讨论了新兴趋势和未来机遇，重点强调将AI与物联网、遥感、农业机器人和多模态农业数据相结合。本章为理解人工智能用于植物病害检测如何支持早期病害识别、减少作物损失、优化农业资源，并促进可持续和智能农业实践提供了基础。","International Journal of Innovative Research in Engineering","2026-09-21T00:00:00Z",59,{"impact":130,"substance":306,"depth":307,"authority":13,"freshness":20,"relevant":21,"comment":308},14,15,"系统综述AI在植物病害检测中的应用，内容全面但属教科书式介绍，方法新颖性与数据规模有限，可作为智慧农业主题聚合素材。",[310],{"name":302,"url":299},[26,27,312,28,30],"农业物联网",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":316},{"candidates":317,"agricultural_evidence":45},[318,322,328,332,336,342],{"code":32,"score":17,"supported":45,"title":319,"abstract":320,"tags":321},[47],[49,50,51,52,53,54,47,55],[27],{"code":81,"score":323,"supported":45,"title":324,"abstract":325,"tags":327},11,[],[26,28,326,148],"smart agriculture",[26,28],{"code":74,"score":59,"supported":45,"title":329,"abstract":330,"tags":331},[],[221,223],[312],{"code":58,"score":67,"supported":45,"title":333,"abstract":334,"tags":335},[],[232,233],[],{"code":337,"score":75,"supported":45,"title":338,"abstract":339,"tags":341},"F6",[],[340],"农业机器人",[],{"code":254,"score":75,"supported":45,"title":343,"abstract":344,"tags":346},[],[345],"agricultural data",[],[87,348],"无人机 作物健康监测","AI植物病害检测-3161","10.59256\u002Fijire.20260705005",{"doi":350,"openalex_id":352,"authors":353,"venue":302,"cited_by_count":107,"oa_url":9,"card":356,"direction":294,"ingested_from":114},"W7213950095",[354],{"name":355,"orcid":9},"Jamuna Ratcha",{"tldr":357,"method":358,"finding":359,"direction":112,"opportunity":360},"综述AI在植物病害检测中的应用，涵盖图像采集到模型评估全流程及未来趋势。","综述CNN、迁移学习、图像分割与目标检测在叶片病害识别中的应用。","AI可高精度识别病害，但受限于数据集不足、环境变化与模型泛化能力。","可探索多模态数据融合与可解释AI，提升复杂田间环境下病害检测的泛化能力。","2026-09-22T23:30:11.209653Z",{"id":363,"title":364,"url":365,"summary":366,"summary_zh":367,"content":9,"source_name":368,"source_url":365,"published_at":369,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":185,"score_detail":370,"sources":373,"tags":375,"primary_topic":377,"secondary_topics":383,"classification":385,"search_phrases":411,"slug":414,"view_count":82,"doi":415,"paper":416,"created_at":432},2151,"An adaptive deep learning framework for multi-temporal crop and drought stress monitoring in precision agriculture","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-70304-z","Precision agriculture increasingly relies on data-driven methods to address the challenges of crop stress and drought monitoring under changing climatic conditions. Hence, a deep learning (DL) based innovative framework is required for assessing crop stress dynamics, aimed at improving agricultural productivity and ensuring regional food security, because conventional models often struggle to capture subtle spatial and spectral variations, resulting in limited accuracy and generalizability. To overcome these challenges, an innovative and adaptive DL-based approach has been proposed to utilize multi-temporal Sentinel-2 satellite images collected over Haldharmau village, Gonda District, Uttar Pradesh. The model effectively integrates local spectral and spatial details with broader contextual features, enabling crop classification and stress detection to support precision agriculture. The crop stress and drought maps have been generated with the help of a CNN+ViT model (i.e., A hybrid DL-based classification model) based classified images in conjunction with NDVI and NDWI images to evaluate drought-induced crop variability across 2023, 2024, and 2025. The monthly (January to June) stress maps derived from this process provide valuable insights into temporal patterns of crop exposure. The proposed framework offers quantitative insights into temporal and spatial patterns of crop vulnerability and resilience across semi‐arid agricultural landscapes. Therefore, the proposed framework advances precision agriculture modeling by integrating satellite images, deep learning, and phenological analysis, and is readily transferable to other drought-prone regions with analogous crop systems.","精准农业日益依赖数据驱动方法来应对气候变化条件下作物胁迫与干旱监测的挑战。因此，需要一种基于深度学习（DL）的创新框架来评估作物胁迫动态，以提高农业生产力并保障区域粮食安全，因为传统模型往往难以捕捉细微的空间和光谱变化，导致精度和泛化能力有限。为克服这些挑战，提出了一种创新且自适应的基于深度学习的方法，利用在北方邦戈达县哈尔达毛村采集的多时相Sentinel-2卫星影像。该模型有效整合了局部光谱与空间细节以及更广泛的上下文特征，实现了作物分类和胁迫检测，以支持精准农业。作物胁迫和干旱地图借助CNN+ViT模型（即基于混合深度学习的分类模型）生成的分类影像，结合NDVI和NDWI影像，评估了2023年、2024年和2025年干旱引起的作物变异性。通过该过程得到的逐月（1月至6月）胁迫地图为作物暴露的时间格局提供了有价值的见解。所提出的框架为半干旱农业景观中作物脆弱性和恢复力的时空格局提供了定量认识。因此，该框架通过整合卫星影像、深度学习和物候分析，推进了精准农业建模，并可随时推广至具有类似作物系统的其他干旱易发地区。","Scientific Reports","2026-09-09T00:00:00Z",{"impact":371,"substance":18,"depth":129,"authority":306,"freshness":20,"relevant":21,"comment":372},16,"基于CNN+ViT混合模型与Sentinel-2多时相影像的作物分类与干旱胁迫监测框架，方法新颖、数据跨三年，对旱区精准农业有参考价值。",[374],{"name":368,"url":365},[26,27,28,29,376],"作物干旱胁迫",{"code":378,"slug":379,"name":380,"path":381,"parent_code":382},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate","H",[384],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":386},{"candidates":387,"agricultural_evidence":45},[388,395,399,403,407],{"code":378,"score":129,"supported":45,"title":389,"abstract":391,"tags":394},[390],"drought",[392,393,390],"气候变化","干旱",[],{"code":81,"score":371,"supported":45,"title":396,"abstract":397,"tags":398},[148],[28,148],[26,28],{"code":32,"score":307,"supported":45,"title":400,"abstract":401,"tags":402},[54],[50,54],[27],{"code":66,"score":67,"supported":45,"title":404,"abstract":405,"tags":406},[],[70,71],[],{"code":58,"score":21,"supported":14,"title":408,"abstract":409,"tags":410},[],[],[29],[412,413],"作物干旱胁迫 农业人工智能 智慧农业 精准农业","作物干旱胁迫 农业人工智能","作物干旱胁迫农业人工智能智慧农业精准农业-2151","10.1038\u002Fs41598-026-70304-z",{"doi":415,"openalex_id":417,"authors":418,"venue":368,"cited_by_count":107,"oa_url":425,"card":426,"direction":294,"ingested_from":114},"W7212034680",[419,422],{"name":420,"orcid":421},"Gausiya Yasmeen","https:\u002F\u002Forcid.org\u002F0000-0002-7853-1376",{"name":423,"orcid":424},"Tasneem Ahmed","https:\u002F\u002Forcid.org\u002F0000-0003-2702-3168","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-70304-z_reference.pdf",{"tldr":427,"method":428,"finding":429,"direction":430,"opportunity":431},"提出CNN+ViT混合深度学习框架，利用多时相Sentinel-2影像监测作物分类与干旱胁迫。","CNN+ViT混合模型结合NDVI、NDWI，基于Sentinel-2多时相影像","框架可量化半干旱区作物时空脆弱性与恢复力，生成月度胁迫图。","农业遥感与作物表型","可探索多源遥感与时序模型融合，提升跨区域干旱胁迫预警的泛化能力。","2026-09-11T23:30:13.461760Z",{"id":434,"title":435,"url":436,"summary":437,"summary_zh":438,"content":9,"source_name":439,"source_url":436,"published_at":440,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":441,"score_detail":442,"sources":445,"tags":447,"primary_topic":449,"secondary_topics":450,"classification":455,"search_phrases":495,"slug":498,"view_count":107,"doi":499,"paper":500,"created_at":518},5553,"Artificial Intelligence–Driven Precision Agriculture Systems: Engineering Frameworks for Climate-Resilient Food Production","https:\u002F\u002Fdoi.org\u002F10.53982\u002Fajerd.2026.01.14-j","Climate change, soil issues, water scarcity, and the increasing need for food worldwide mean we need smart farming systems that can manage resources flexibly. Old farming methods used the same inputs and simple rules for decisions. AI can help us manage resources better by adapting and optimizing them as we deal with complex environmental factors. This paper looks closely at AI-powered precision agriculture systems designed to make food production more resilient to changing weather. The authors reviewed research from 2021 to 2026 in agricultural engineering, computer engineering, and environmental systems. The authors examined how machine learning, deep learning, reinforcement learning, computer vision, robotics, and IoT sensors are used for predicting crop yields, spotting diseases, improving irrigation, managing nutrients, and automating field tasks. Our analysis shows that hybrid deep learning models (CNN-LSTM) are better than single models for predicting crop yields over space and time. Reinforcement learning algorithms are also better at adjusting irrigation cycles when weather is unpredictable. Using edge computing for system design reduces delays and improves real-time decision-making compared to traditional cloud-based systems. However, there are still big challenges with understanding how AI models work, having enough diverse data, ensuring cybersecurity, setting common standards, and making these systems affordable, especially in developing countries. To tackle these problems, we suggest a framework that combines AI analysis, distributed sensor networks, adaptive controls, and climate risk assessments. The authors provided critical insights into essential research gaps and outline a technological roadmap towards achieving sustainable, resilient and intelligent agricultural systems.","气候变化、土壤问题、水资源短缺以及全球日益增长的粮食需求，意味着我们需要能够灵活管理资源的智能农业系统。传统农业方法对相同的投入采用固定模式，并依赖简单规则进行决策。人工智能可以帮助我们更好地管理资源，通过适应和优化资源利用来应对复杂的环境因素。本文深入探讨了人工智能驱动的精准农业系统，旨在使粮食生产对不断变化的天气更具韧性。作者综述了2021年至2026年间农业工程、计算机工程和环境系统领域的研究。作者考察了机器学习、深度学习、强化学习、计算机视觉、机器人技术及物联网传感器如何被用于作物产量预测、病害识别、灌溉优化、养分管理和田间作业自动化。我们的分析表明，混合深度学习模型（CNN-LSTM）在时空作物产量预测方面优于单一模型。在天气不可预测的情况下，强化学习算法在调整灌溉周期方面也表现更优。与传统基于云的系统相比，采用边缘计算进行系统设计可降低延迟并改善实时决策能力。然而，在理解人工智能模型的工作原理、获取足够多样化的数据、确保网络安全、制定通用标准以及使这些系统具备可负担性方面，仍存在重大挑战，尤其是在发展中国家。为应对这些问题，我们提出了一个融合人工智能分析、分布式传感器网络、自适应控制和气候风险评估的框架。作者对关键研究空白提供了重要见解，并勾勒了实现可持续、韧性和智能农业系统的技术路线图。","ABUAD Journal of Engineering Research and Development (AJERD)","2026-10-21T00:00:00Z",80,{"impact":17,"substance":443,"depth":17,"authority":19,"freshness":211,"relevant":21,"comment":444},22,"系统综述AI驱动精准农业的工程框架与技术路线，方法梳理扎实、结论有参考价值，但属综述类论文，产业落地影响有限。",[446],{"name":439,"url":436},[26,27,221,28,448],"气候韧性",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[451,452],{"code":74,"slug":200,"name":201,"path":202,"parent_code":36},{"code":247,"slug":251,"name":453,"path":454,"parent_code":382},"水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation",{"status":39,"locked":14,"version":40,"method":41,"evidence":456},{"candidates":457,"agricultural_evidence":45},[458,462,466,471,476,481,485,491],{"code":32,"score":17,"supported":45,"title":459,"abstract":460,"tags":461},[47],[50,51,52,53,54,55],[27],{"code":81,"score":371,"supported":45,"title":463,"abstract":464,"tags":465},[148],[28,148],[26,28],{"code":74,"score":13,"supported":45,"title":467,"abstract":468,"tags":470},[],[221,78,223,469],"edge computing",[221],{"code":247,"score":211,"supported":45,"title":472,"abstract":473,"tags":475},[],[250,474,251],"水资源",[],{"code":378,"score":59,"supported":45,"title":477,"abstract":478,"tags":480},[],[392,479],"climate change",[448],{"code":240,"score":67,"supported":45,"title":482,"abstract":483,"tags":484},[],[243,244],[],{"code":486,"score":75,"supported":45,"title":487,"abstract":488,"tags":490},"A7",[],[489],"间作",[],{"code":260,"score":75,"supported":45,"title":492,"abstract":493,"tags":494},[],[263],[],[496,497],"AI 精准农业 气候韧性","CNN-LSTM 作物产量预测","AI精准农业气候韧性-5553","10.53982\u002Fajerd.2026.01.14-j",{"doi":499,"openalex_id":501,"authors":502,"venue":439,"cited_by_count":107,"oa_url":436,"card":513,"direction":294,"ingested_from":114},"W7223121456",[503,506,508,510],{"name":504,"orcid":505},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":507,"orcid":9},"Oluwadare OYEBODE",{"name":509,"orcid":9},"Iretiayo Adelaiye",{"name":511,"orcid":512},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":514,"method":515,"finding":516,"direction":112,"opportunity":517},"综述2021-2026年AI驱动精准农业系统，提出气候韧性粮食生产工程框架。","综述机器学习、深度学习、强化学习、计算机视觉、机器人与物联网在精准农业中的应用。","CNN-LSTM混合模型和强化学习在产量预测与灌溉调度上优于单一方法，边缘计算提升实时性。","可研究边缘计算与强化学习结合的轻量级灌溉决策系统，并探索发展中国家低成本部署与标准化路径。","2026-10-22T11:20:18.151376Z",{"id":520,"title":521,"url":522,"summary":523,"summary_zh":524,"content":9,"source_name":525,"source_url":522,"published_at":526,"category":12,"cover_url":9,"hotness":13,"is_selected":45,"score":527,"score_detail":528,"sources":532,"tags":534,"primary_topic":536,"secondary_topics":537,"classification":538,"search_phrases":563,"slug":566,"view_count":255,"doi":567,"paper":568,"created_at":594},5382,"Modeling the global potential of smart farming innovations","https:\u002F\u002Fdoi.org\u002F10.1073\u002Fpnas.2530081123","Smart farming innovations can improve agricultural productivity, resilience, and environmental sustainability, yet their real-world impact hinges on their diffusion dynamics, which in turn depend on their economic viability. We present here a global assessment of the profitability of smart farming innovation across croplands at a spatial resolution of ~5 km from the farmers’ and the societal perspectives. We cover application mapping for precision agriculture, automated application of inputs, autonomous weeding machines, GPS steering assistance systems, and autonomous irrigation systems. For this, we compiled a globally comprehensive dataset of smart farming innovation adoption locations (N = 4,422) based on a combination of a survey (N = 2,677) and a meta-analysis (N = 1,745) and combined them with remotely sensed and statistical data to train an ensemble of five geospatial machine learning models (generalized linear models, random forests, support vector machines, maximum entropy, and boosted regression trees), which show the global distribution of the different innovations’ profitability and its overlap with major environmental issues the innovations can help to mitigate. Simulations of future socioeconomic pathways combined with assumptions about future agri-environmental policies reveal that, especially in middle-income countries, the potential of smart farming will grow over time.","智慧农业创新能够提升农业生产力、韧性和环境可持续性，然而其现实影响取决于扩散动态，而扩散动态又取决于其经济可行性。本文从农民和社会两个视角，以约5公里的空间分辨率对全球农田范围内智慧农业创新的盈利能力进行了评估。研究涵盖精准农业的应用测绘、投入品的自动化施用、自主除草机械、GPS转向辅助系统以及自主灌溉系统。为此，我们基于一项调查（N = 2,677）和一项元分析（N = 1,745）相结合，编制了全球范围内较为全面的智慧农业创新采用地点数据集（N = 4,422），并将其与遥感和统计数据相结合，用于训练由五种地理空间机器学习模型（广义线性模型、随机森林、支持向量机、最大熵和提升回归树）组成的集成模型，这些模型揭示了不同创新盈利能力的全球分布及其与这些创新有助于缓解的主要环境问题的重叠情况。对未来社会经济路径的模拟结合对未来农业环境政策的假设表明，智慧农业的潜力将随时间推移而增长，尤其是在中等收入国家。","Proceedings of the National Academy of Sciences","2026-10-19T00:00:00Z",93,{"impact":529,"substance":530,"depth":141,"authority":307,"freshness":211,"relevant":21,"comment":531},26,24,"PNAS 全球尺度智慧农业盈利性评估，数据规模与方法新颖，结论对智慧农业推广具政策参考价值，值得进入每日精选。",[533],{"name":525,"url":522},[26,27,28,232,535],"全球评估",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":539},{"candidates":540,"agricultural_evidence":45},[541,545,549,553,557],{"code":81,"score":323,"supported":45,"title":542,"abstract":543,"tags":544},[],[26,28,148],[26,28],{"code":32,"score":59,"supported":45,"title":546,"abstract":547,"tags":548},[],[51,53],[27],{"code":247,"score":67,"supported":45,"title":550,"abstract":551,"tags":552},[],[250,251],[],{"code":58,"score":255,"supported":45,"title":554,"abstract":555,"tags":556},[],[232],[232],{"code":558,"score":75,"supported":45,"title":559,"abstract":560,"tags":562},"F4",[],[561],"除草",[],[564,565],"农业人工智能 全球评估 智慧农业 精准农业","农业人工智能 全球评估","农业人工智能全球评估智慧农业精准农业-5382","10.1073\u002Fpnas.2530081123",{"doi":567,"openalex_id":569,"authors":570,"venue":525,"cited_by_count":107,"oa_url":522,"card":589,"direction":294,"ingested_from":114},"W7222974188",[571,574,577,580,583,586],{"name":572,"orcid":573},"Shahrear Roman","https:\u002F\u002Forcid.org\u002F0009-0006-5659-4292",{"name":575,"orcid":576},"Hadi Hadi","https:\u002F\u002Forcid.org\u002F0000-0002-2410-9751",{"name":578,"orcid":579},"Wyclife Agumba Oluoch","https:\u002F\u002Forcid.org\u002F0000-0003-0803-9279",{"name":581,"orcid":582},"David Zilberman","https:\u002F\u002Forcid.org\u002F0000-0001-5743-5134",{"name":584,"orcid":585},"Robert Finger","https:\u002F\u002Forcid.org\u002F0000-0002-0634-5742",{"name":587,"orcid":588},"David Wuepper","https:\u002F\u002Forcid.org\u002F0000-0002-1344-6023",{"tldr":590,"method":591,"finding":592,"direction":294,"opportunity":593},"全球5公里尺度评估五类智慧农业创新在农田的盈利性与扩散潜力。","整合4422个采用点调查与元分析数据，用五种地理空间机器学习集成建模。","智慧农业盈利性空间差异显著，中等收入国家未来潜力将随社会经济路径增长。","可结合区域政策与农户异质性，细化盈利性预测并评估环境协同效益。","2026-10-20T23:30:08.754594Z"]