[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1273":3,"related-1273":103},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":23,"tags":25,"primary_topic":30,"secondary_topics":36,"classification":37,"search_phrases":62,"slug":65,"view_count":66,"doi":67,"paper":68,"created_at":102},1273,"OurMED D6.3 GoAigua Digital Twins Tool at the Farm, Basin and MED Scale. Version 1.2","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.22142119","This deliverable, D6.3 GoAigua Digital Twins Tool at the plot, basin and MED scale, presents the development and integration of the OurMED Digital Twin framework implemented within WP6 - Digital Twins and Decision Support Systems. The framework combines EO products, in situ observations, modelling outputs, citizen-science contributions, and smart agriculture services into interoperable digital platforms supporting near real-time monitoring, forecasting, environmental assessment, and decision-making.",null,"Zenodo (CERN European Organization for Nuclear Research)","2026-08-28T00:00:00Z","论文",10,false,69,{"impact":16,"substance":17,"depth":18,"authority":19,"freshness":20,"relevant":21,"comment":22},15,20,18,12,4,1,"欧盟项目交付物，整合多源数据构建农业数字孪生框架，具行业参考价值，但时效性较低。",[24],{"name":9,"url":6},[26,27,28,29],"智慧农业","农业遥感","数字孪生","决策支持系统",{"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":46},[43,52,57],{"code":44,"score":45,"supported":46,"title":47,"abstract":48,"tags":51},"I1",8,true,[],[49,50],"smart agriculture","digital twin",[28,26],{"code":31,"score":20,"supported":46,"title":53,"abstract":54,"tags":56},[],[55],"decision support",[29],{"code":58,"score":21,"supported":13,"title":59,"abstract":60,"tags":61},"I3",[],[],[27],[63,64],"决策支持系统 农业遥感 数字孪生 智慧农业","决策支持系统 农业遥感","决策支持系统农业遥感数字孪生智慧农业-1273",2,"10.5281\u002Fzenodo.22142119",{"doi":67,"openalex_id":69,"authors":70,"venue":9,"cited_by_count":94,"oa_url":6,"card":95,"direction":99,"ingested_from":101},"W7204518076",[71,73,76,79,82,85,88,91],{"name":72,"orcid":8},"Isabel Augscheller",{"name":74,"orcid":75},"Maximilian Schwarz","https:\u002F\u002Forcid.org\u002F0000-0001-8059-5697",{"name":77,"orcid":78},"Patrícia Lourenço","https:\u002F\u002Forcid.org\u002F0000-0002-4901-0934",{"name":80,"orcid":81},"Ainhoa Maté Marín","https:\u002F\u002Forcid.org\u002F0000-0002-1125-6381",{"name":83,"orcid":84},"Anis Guelmami","https:\u002F\u002Forcid.org\u002F0000-0002-3906-6424",{"name":86,"orcid":87},"Nina Bègue","https:\u002F\u002Forcid.org\u002F0009-0000-1749-2177",{"name":89,"orcid":90},"Amir Rouhani","https:\u002F\u002Forcid.org\u002F0000-0002-7976-3558",{"name":92,"orcid":93},"Seifeddine Jomaa","https:\u002F\u002Forcid.org\u002F0000-0003-4782-9468",0,{"tldr":96,"method":97,"finding":98,"direction":99,"opportunity":100},"开发了跨地块、流域和地中海尺度的农业数字孪生工具，集成多源数据支持实时监测与决策。","集成EO产品、实地观测、模型输出、公民科学和智慧农业服务，构建互操作数字平台。","数字孪生框架支持近实时监测、预报、环境评估和决策，覆盖多尺度。","智慧农业 \u002F 农业物联网","可探索数字孪生与具体作物模型耦合，提升地块尺度精准管理，并验证跨尺度可扩展性。","openalex","2026-09-01T04:03:13.243484Z",{"total":104,"page":21,"page_size":104,"items":105},6,[106,164,232,306,383,461],{"id":107,"title":108,"url":109,"summary":110,"summary_zh":111,"content":8,"source_name":9,"source_url":109,"published_at":112,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":113,"score_detail":114,"sources":118,"tags":120,"primary_topic":124,"secondary_topics":125,"classification":126,"search_phrases":147,"slug":150,"view_count":94,"doi":151,"paper":152,"created_at":163},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。","2026-10-19T00:00:00Z",81,{"impact":18,"substance":115,"depth":18,"authority":116,"freshness":12,"relevant":21,"comment":117},22,13,"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。",[119],{"name":9,"url":109},[26,121,28,122,123],"农业人工智能","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":127},{"candidates":128,"agricultural_evidence":46},[129,133,140],{"code":44,"score":45,"supported":46,"title":130,"abstract":131,"tags":132},[],[28,50],[28,26],{"code":31,"score":134,"supported":46,"title":135,"abstract":136,"tags":139},7,[],[137,138],"计算机视觉","computer vision",[121],{"code":141,"score":104,"supported":46,"title":142,"abstract":143,"tags":146},"A2",[],[144,145],"小麦","wheat",[],[148,149],"PLANT-GPK 植物表型","小麦 收获期 预测","PLANT-GPK植物表型-5315","10.5281\u002Fzenodo.23461121",{"doi":151,"openalex_id":153,"authors":154,"venue":9,"cited_by_count":94,"oa_url":109,"card":157,"direction":161,"ingested_from":101},"W7222875391",[155],{"name":156,"orcid":8},"MOHAMMED TAHAR SAIM",{"tldr":158,"method":159,"finding":160,"direction":161,"opportunity":162},"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。","2026-10-19T23:30:19.267920Z",{"id":165,"title":166,"url":167,"summary":168,"summary_zh":169,"content":8,"source_name":170,"source_url":167,"published_at":171,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":172,"score_detail":173,"sources":177,"tags":179,"primary_topic":182,"secondary_topics":183,"classification":184,"search_phrases":217,"slug":220,"view_count":21,"doi":221,"paper":222,"created_at":231},5311,"AI SMART AGRO SYSTEM: A WEB-BASED AI-ASSISTED DECISION SUPPORT PLATFORM FOR FARMERS","https:\u002F\u002Fdoi.org\u002F10.56726\u002Firjmets103514","Farmers often have to decide on disease control, crop selection, soil management and irrigation without timely expert advice, which leads to yield loss and wastage of water and fertiliser.This paper presents AI Smart Agro System, a web-based decision-support platform that brings five services into one dashboard: leaf disease detection from an uploaded photograph, crop recommendation, soil health analysis, irrigation advisory and weather-based farming advice.The system is built with PHP, MySQL and JavaScript, while disease detection and crop recommendation are handled by an external Python Flask AI service that is called through a secured REST API.Soil, irrigation and weather advice are generated by transparent rule-based logic using soil pH, N-P-K values, soil moisture, rainfall, temperature and humidity.Every result is stored in the user's history, and notifications and an administrator panel support monitoring.The prototype is designed to return the disease name, confidence, treatment and prevention tips for an uploaded leaf image, to recommend the three most suitable crops, and to give clear advice for soil and water management.Quantitative evaluation of the AI models is left for future work.","农民常常需要在没有及时专家建议的情况下决定病害防治、作物选择、土壤管理和灌溉，这会导致产量损失以及水和肥料的浪费。本文提出AI Smart Agro System，这是一个基于Web的决策支持平台，将五项服务整合到一个仪表板中：根据上传照片进行叶片病害检测、作物推荐、土壤健康分析、灌溉建议以及基于天气的农事建议。该系统使用PHP、MySQL和JavaScript构建，而病害检测和作物推荐由外部Python Flask AI服务处理，并通过安全的REST API调用。土壤、灌溉和天气建议由透明的基于规则的逻辑生成，使用土壤pH、N-P-K值、土壤湿度、降雨量、温度和湿度。每个结果都存储在用户历史记录中，通知和管理员面板支持监控。该原型旨在为上传的叶片图像返回病害名称、置信度、治疗和预防提示，推荐三种最合适的作物，并为土壤和水管理提供明确建议。AI模型的定量评估留待未来工作。","International Research Journal of Modernization in Engineering Technology and Science","2026-10-17T00:00:00Z",49,{"impact":45,"substance":174,"depth":19,"authority":104,"freshness":175,"relevant":21,"comment":176},14,9,"原型系统整合病害识别、作物推荐与灌溉建议，思路清晰但缺乏定量评估，属细分技术进展，公共影响有限。",[178],{"name":170,"url":167},[26,121,29,180,181],"病害识别","灌溉管理",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":185},{"candidates":186,"agricultural_evidence":46},[187,192,199,206,213],{"code":31,"score":188,"supported":46,"title":189,"abstract":190,"tags":191},11,[55],[],[121,29,180],{"code":193,"score":134,"supported":46,"title":194,"abstract":195,"tags":198},"H3",[],[196,197],"灌溉","irrigation",[181],{"code":200,"score":104,"supported":46,"title":201,"abstract":202,"tags":205},"H2",[],[203,204],"土壤","soil",[],{"code":207,"score":208,"supported":46,"title":209,"abstract":210,"tags":212},"F3",3,[],[211],"肥料",[],{"code":44,"score":21,"supported":13,"title":214,"abstract":215,"tags":216},[],[],[26],[218,219],"Web 农业决策支持平台","农业人工智能 决策支持系统 智慧农业 灌溉管理","Web农业决策支持平台-5311","10.56726\u002Firjmets103514",{"doi":221,"openalex_id":223,"authors":224,"venue":170,"cited_by_count":94,"oa_url":167,"card":225,"direction":99,"ingested_from":101},"W7222785888",[],{"tldr":226,"method":227,"finding":228,"direction":229,"opportunity":230},"构建一个面向农民的网页版AI决策支持平台，集成病害识别、选种、土壤、灌溉与天气建议。","PHP\u002FMySQL\u002FJS前端，Python Flask提供病害与选种AI服务，规","原型可输出病害名称、置信度与防治建议，推荐三种适宜作物并给出水土管理建议。","农业人工智能与决策模型","AI模型尚未定量评估，可补充精度验证与田间实测，并扩展多语言、离线与本地化作物模型。","2026-10-19T23:30:08.043132Z",{"id":233,"title":234,"url":235,"summary":236,"summary_zh":237,"content":8,"source_name":238,"source_url":235,"published_at":171,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":239,"score_detail":240,"sources":242,"tags":244,"primary_topic":247,"secondary_topics":248,"classification":249,"search_phrases":289,"slug":292,"view_count":94,"doi":293,"paper":294,"created_at":305},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",83,{"impact":115,"substance":115,"depth":18,"authority":19,"freshness":175,"relevant":21,"comment":241},"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。",[243],{"name":238,"url":235},[26,121,245,28,246],"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":250},{"candidates":251,"agricultural_evidence":46},[252,259,263,267,272,278,284],{"code":31,"score":253,"supported":46,"title":254,"abstract":256,"tags":258},19,[255],"artificial intelligence",[137,255,138,257,55],"large language model",[121,245],{"code":200,"score":104,"supported":46,"title":260,"abstract":261,"tags":262},[],[203,204],[],{"code":193,"score":104,"supported":46,"title":264,"abstract":265,"tags":266},[],[196,197],[],{"code":44,"score":268,"supported":46,"title":269,"abstract":270,"tags":271},5,[],[28],[28,26],{"code":273,"score":208,"supported":46,"title":274,"abstract":275,"tags":277},"F6",[],[276],"农业机器人",[],{"code":279,"score":208,"supported":46,"title":280,"abstract":281,"tags":283},"G2",[],[282],"smallholder",[],{"code":285,"score":21,"supported":13,"title":286,"abstract":287,"tags":288},"H7",[],[],[246],[290,291],"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":293,"openalex_id":295,"authors":296,"venue":238,"cited_by_count":94,"oa_url":235,"card":300,"direction":99,"ingested_from":101},"W7222823256",[297],{"name":298,"orcid":299},"Mostafa Farajpour","https:\u002F\u002Forcid.org\u002F0000-0003-4223-502X",{"tldr":301,"method":302,"finding":303,"direction":229,"opportunity":304},"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。","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。","2026-10-18T23:30:07.666127Z",{"id":307,"title":308,"url":309,"summary":310,"summary_zh":311,"content":8,"source_name":312,"source_url":309,"published_at":313,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":314,"sources":317,"tags":319,"primary_topic":323,"secondary_topics":327,"classification":329,"search_phrases":355,"slug":358,"view_count":21,"doi":359,"paper":360,"created_at":382},5165,"Proximal Remote Sensing of Oil Palm Basal Stem Rot Using Deep Learning Vision Algorithms and Ground-based Images","https:\u002F\u002Fdoi.org\u002F10.22266\u002Fijies2026.1130.31","Basal Stem Rot (BSR), caused by Ganoderma boninense, threatens oil palm productivity in Southeast Asia.This study develops and benchmarks a low-cost remote sensing framework for BSR severity diagnosis from smartphone-captured RGB trunk images, evaluating two pathways for future field deployment.The first pairs YOLO detection with a Random Forest classifier: YOLO26n gave the best accuracy-to-cost trade-off among three nanovariants for localizing BSR symptoms (mushroom, rot, white-button), and the end-to-end YOLO26n+RF pipeline achieved 0.83 accuracy and macro-F1 of 0.79 across Healthy, Mild, Moderate, and Severe classes.Rot was consistently the hardest symptom to localize, owing to its diffuse, boundary-ambiguous appearance.The second pathway trained four image classifiers (EfficientNet-B0, MobileNetV2, Swin-Tiny, and ConvNeXt-V2-Tiny) directly on severity labels, with ConvNeXt-V2-Tiny performing best (0.83 accuracy, 0.83 macro-F1).Both pathways performed well at severity extremes but struggled with Moderate, reflecting visual overlap between stages.SHAP analysis identified rot-area ratio and fruiting-body count as top predictors.These offline results show accurate BSR diagnosis is potentially achievable from smartphone imagery, laying groundwork for future on-device deployment.","由灵芝（Ganoderma boninense）引起的基茎腐病（Basal Stem Rot, BSR）威胁着东南亚油棕的生产力。本研究开发并基准测试了一个低成本遥感框架，用于从智能手机拍摄的RGB树干图像中诊断基茎腐病的严重程度，并评估了未来田间部署的两条技术路径。第一条路径将YOLO检测与随机森林分类器相结合：在三种纳米变体中，YOLO26n在定位基茎腐病症状（蘑菇、腐烂、白钮）方面实现了最佳的精度-成本权衡，端到端的YOLO26n+RF流程在健康、轻度、中度和重度四个类别上达到了0.83的准确率和0.79的宏F1值。腐烂始终是最难定位的症状，原因在于其弥散且边界模糊的外观。第二条路径直接在严重程度标签上训练了四个图像分类器（EfficientNet-B0、MobileNetV2、Swin-Tiny和ConvNeXt-V2-Tiny），其中ConvNeXt-V2-Tiny表现最佳（准确率0.83，宏F1值0.83）。两条路径在严重程度极端类别上表现良好，但在中度类别上表现欠佳，反映出各阶段之间存在视觉重叠。SHAP分析确定腐烂面积比和子实体数量为最重要的预测因子。这些离线结果表明，利用智能手机图像有望实现准确的基茎腐病诊断，为未来设备端部署奠定了基础。","International journal of intelligent engineering and systems","2026-10-16T00:00:00Z",{"impact":19,"substance":17,"depth":315,"authority":19,"freshness":175,"relevant":21,"comment":316},16,"该研究利用智能手机图像与深度学习实现油棕基腐病严重度诊断，方法新颖、结论可靠，但属细分作物研究，产业影响有限，适合作为技术类精选。",[318],{"name":312,"url":309},[26,27,320,321,322],"深度学习","油棕","病害诊断",{"code":58,"slug":324,"name":325,"path":326,"parent_code":35},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[328],{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},{"status":38,"locked":13,"version":39,"method":40,"evidence":330},{"candidates":331,"agricultural_evidence":46},[332,338,344,351],{"code":58,"score":16,"supported":46,"title":333,"abstract":335,"tags":337},[334],"remote sensing",[336,334],"遥感",[27],{"code":31,"score":19,"supported":46,"title":339,"abstract":341,"tags":343},[340],"deep learning",[342],"YOLO",[320],{"code":345,"score":104,"supported":46,"title":346,"abstract":347,"tags":350},"B5",[],[348,349],"蘑菇","mushroom",[],{"code":44,"score":21,"supported":13,"title":352,"abstract":353,"tags":354},[],[],[26],[356,357],"油棕 基腐病 遥感","Ganoderma boninense 智能手机 检测","油棕基腐病遥感-5165","10.22266\u002Fijies2026.1130.31",{"doi":359,"openalex_id":361,"authors":362,"venue":312,"cited_by_count":94,"oa_url":309,"card":377,"direction":161,"ingested_from":101},"W7222626135",[363,366,369,371,374],{"name":364,"orcid":365},"Wan Siu Hong","https:\u002F\u002Forcid.org\u002F0000-0002-8254-4962",{"name":367,"orcid":368},"Jonathan Yong Chung Ee","https:\u002F\u002Forcid.org\u002F0000-0003-4079-8005",{"name":370,"orcid":8},"Jin Yuan Chan",{"name":372,"orcid":373},"Mohamad Izzuddin Anuar","https:\u002F\u002Forcid.org\u002F0000-0002-4929-5175",{"name":375,"orcid":376},"Mahmud Iwan Solihin","https:\u002F\u002Forcid.org\u002F0000-0002-5293-7466",{"tldr":378,"method":379,"finding":380,"direction":161,"opportunity":381},"用手机拍摄油棕树干图像，结合深度学习检测与分类，实现基腐病严重度诊断。","YOLO26n+随机森林与四种图像分类器，基于手机RGB图像，SHAP解释。","两条路径准确率均约0.83，中度病级最难区分，腐区比例与子实体数为关键预测因子。","可探索轻量化模型在手机端离线部署，并扩充多地区、多品种数据以提升中度病级辨识。","2026-10-17T23:30:28.307953Z",{"id":384,"title":385,"url":386,"summary":387,"summary_zh":388,"content":8,"source_name":389,"source_url":386,"published_at":390,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":391,"score_detail":392,"sources":395,"tags":397,"primary_topic":400,"secondary_topics":401,"classification":402,"search_phrases":436,"slug":439,"view_count":94,"doi":440,"paper":441,"created_at":460},4985,"Hybrid deep ensemble learning for crop disease prediction and climate-resilient disaster management in smart agriculture","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10791-026-10579-y","Abstract Accurate crop disease prediction and climate-resilient disaster management are essential for improving agricultural productivity and ensuring food security under increasingly dynamic environmental conditions. Existing deep learning approaches typically perform crop disease detection and climate forecasting independently and often rely on deterministic CNN–LSTM architectures without effective multimodal feature fusion, predictive uncertainty estimation, or comprehensive statistical validation. Consequently, their ability to provide reliable decision support under changing climatic conditions remains limited. To address these challenges, this paper proposes a novel hybrid deep ensemble learning framework for integrated crop disease prediction and climate-resilient disaster management in smart agriculture. The proposed framework combines convolutional neural networks (CNNs) for spatial crop image feature extraction with long short-term memory (LSTM) networks for temporal climate forecasting. An attention-based multimodal feature fusion mechanism is employed to integrate heterogeneous crop and climate information, while Bayesian uncertainty estimation and deep ensemble learning are incorporated to improve prediction reliability, robustness, and confidence-aware decision support. Experimental evaluation is performed using multimodal datasets comprising crop images and climate observations collected from the PlantVillage, NOAA, and NASA EOS datasets. The dataset is divided into training, validation, and testing subsets using a 70:15:15 ratio. The proposed framework is evaluated using classification and regression metrics, including Accuracy, Precision, Recall, F1-score, RMSE, MAE, MAPE, and AUC-ROC. To ensure reproducibility, all experiments are repeated five times using different random seeds, and the results are reported with mean $$\\pm$$ standard deviation, 95% confidence intervals, and paired Student’s $$t$$ -tests. Experimental results demonstrate that the proposed framework consistently outperforms conventional CNN, LSTM, and CNN–LSTM hybrid models by achieving higher classification accuracy, lower climate forecasting error, improved robustness under varying climate conditions, and statistically significant performance improvements. The proposed framework provides a reliable, scalable, and uncertainty-aware solution for intelligent climate-resilient smart agriculture and early agricultural disaster management.","摘要：准确的作物病害预测和气候适应性灾害管理对于在日益动态的环境条件下提高农业生产力和保障粮食安全至关重要。现有深度学习方法通常独立执行作物病害检测和气候预测，且往往依赖确定性CNN–LSTM架构，缺乏有效的多模态特征融合、预测不确定性估计或全面的统计验证。因此，其在气候变化条件下提供可靠决策支持的能力仍然有限。为应对这些挑战，本文提出了一种新颖的混合深度集成学习框架，用于智慧农业中的作物病害预测与气候适应性灾害管理一体化。该框架将用于空间作物图像特征提取的卷积神经网络（CNN）与用于时间气候预测的长短期记忆（LSTM）网络相结合。采用基于注意力的多模态特征融合机制整合异构的作物与气候信息，同时引入贝叶斯不确定性估计和深度集成学习，以提高预测可靠性、鲁棒性和置信感知决策支持能力。实验评估使用包含作物图像和气候观测数据的多模态数据集，数据来源于PlantVillage、NOAA和NASA EOS数据集。数据集按70：15：15的比例划分为训练集、验证集和测试集。采用分类和回归指标对所提框架进行评估，包括准确率、精确率、召回率、F1分数、RMSE、MAE、MAPE和AUC-ROC。为确保可复现性，所有实验使用不同随机种子重复五次，结果以均值$$\\pm$$标准差、95%置信区间和配对Student's $$t$$检验报告。实验结果表明，所提框架在分类准确率、气候预测误差、不同气候条件下的鲁棒性以及统计显著性性能提升方面均持续优于传统CNN、LSTM和CNN–LSTM混合模型。该框架为智能气候适应性智慧农业和早期农业灾害管理提供了一种可靠、可扩展且具有不确定性感知能力的解决方案。","Discover Computing","2026-10-14T00:00:00Z",71,{"impact":19,"substance":17,"depth":393,"authority":116,"freshness":175,"relevant":21,"comment":394},17,"提出CNN-LSTM注意力融合与贝叶斯不确定性估计的混合深度集成框架，多源数据与统计验证扎实，但属方法类论文，产业落地影响有限。",[396],{"name":389,"url":386},[26,121,27,398,399],"防灾减灾","作物病害预测",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[],{"status":38,"locked":13,"version":39,"method":40,"evidence":403},{"candidates":404,"agricultural_evidence":46},[405,409,413,419,426,432],{"code":44,"score":16,"supported":46,"title":406,"abstract":407,"tags":408},[49],[26,49],[26],{"code":31,"score":12,"supported":46,"title":410,"abstract":411,"tags":412},[],[320,340,55],[121],{"code":285,"score":134,"supported":46,"title":414,"abstract":415,"tags":418},[],[416,417],"气候变化","气候适应",[398],{"code":420,"score":104,"supported":46,"title":421,"abstract":422,"tags":425},"A8",[],[423,424],"粮食安全","food security",[],{"code":427,"score":208,"supported":46,"title":428,"abstract":429,"tags":431},"A7",[],[430],"间作",[],{"code":58,"score":21,"supported":13,"title":433,"abstract":434,"tags":435},[],[],[27],[437,438],"PlantVillage NOAA NASA 作物病害预测","CNN LSTM 气候韧性 智慧农业","PlantVillageNOAANASA作物病害预测-4985","10.1007\u002Fs10791-026-10579-y",{"doi":440,"openalex_id":442,"authors":443,"venue":389,"cited_by_count":94,"oa_url":386,"card":455,"direction":99,"ingested_from":101},"W7221778141",[444,447,450,452],{"name":445,"orcid":446},"Deepak Motwani","https:\u002F\u002Forcid.org\u002F0000-0002-0217-7155",{"name":448,"orcid":449},"Sellappan Palaniappan","https:\u002F\u002Forcid.org\u002F0009-0009-1168-2864",{"name":451,"orcid":8},"A. Daniel",{"name":453,"orcid":454},"Azween B. Abdullah","https:\u002F\u002Forcid.org\u002F0000-0003-4425-8604",{"tldr":456,"method":457,"finding":458,"direction":229,"opportunity":459},"提出混合深度集成框架，融合CNN与LSTM并引入注意力机制，实现作物病害预测与气候韧性灾害管理。","CNN提取图像特征、LSTM预测气候，注意力多模态融合，贝叶斯不确定性估计与深度","框架在分类精度与气候预测误差上均优于CNN、LSTM及CNN-LSTM基线，且性能提升具统计显著性。","可探索轻量化边缘部署与多作物跨区域迁移，结合实时物联网数据提升不确定性感知的灾害预警时效。","2026-10-15T23:30:08.408875Z",{"id":462,"title":463,"url":464,"summary":465,"summary_zh":466,"content":8,"source_name":467,"source_url":464,"published_at":468,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":469,"score_detail":470,"sources":473,"tags":475,"primary_topic":478,"secondary_topics":479,"classification":485,"search_phrases":509,"slug":512,"view_count":94,"doi":513,"paper":514,"created_at":537},4898,"An Interpretable Deep Learning Framework for Spatial Prediction of Soil Salinity Using Multi-Source Remote Sensing and Environmental Data: A Case Study of Cangzhou, Hebei Province, China","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fs26206465","Soil salinity poses a significant challenge to agricultural productivity and sustainable land management in coastal and semi-arid regions. Soil salinity in coastal agricultural regions is controlled by complex interactions among surface conditions, groundwater processes, soil properties, and terrain characteristics. Accurate spatial prediction requires integrating remote sensing observations with environmental information that represents these processes. This study presents a multi-source deep learning framework that integrates Sentinel-2-derived spectral indices with environmental covariates for regional soil salinity spatial prediction under the environmental conditions of the 2024 sampling period. SHapley Additive exPlanations (SHAP) is employed to quantify the contribution of environmental variables within the prediction framework and improve model interpretability. A convolutional neural network (CNN) incorporating a squeeze-and-excitation (SE) attention mechanism is developed as a nonlinear feature representation framework for integrating heterogeneous predictors, while the Crested Porcupine Optimizer (CPO) is used to optimize model hyperparameters. The proposed framework achieved the highest predictive performance among the benchmark models, with an R2 of 0.785, an RMSE of 0.928, and an MAE of 0.720. Integrating environmental covariates with spectral features further improves the prediction performance, increasing the R2 from 0.701 to 0.785. The results indicate the potential of integrating multi-source environmental information with interpretable deep learning for spatial prediction of near-surface soil salinity under similar coastal agricultural conditions.","土壤盐渍化对沿海和半干旱地区的农业生产力和可持续土地管理构成了重大挑战。沿海农业区域的土壤盐渍化受地表条件、地下水过程、土壤属性和地形特征之间复杂相互作用的控制。准确的空间预测需要将遥感观测与表征这些过程的环境信息相结合。本研究提出了一种多源深度学习框架，该框架将Sentinel-2衍生的光谱指数与环境协变量相结合，用于2024年采样期环境条件下的区域土壤盐渍化空间预测。采用SHapley加法解释（SHAP）方法量化预测框架中环境变量的贡献并提高模型可解释性。开发了一种融合压缩-激励（SE）注意力机制的卷积神经网络（CNN）作为非线性特征表示框架，用于整合异质性预测因子，同时使用冠豪猪优化器（CPO）优化模型超参数。所提出的框架在基准模型中取得了最高的预测性能，R²为0.785，RMSE为0.928，MAE为0.720。将环境协变量与光谱特征相结合进一步提高了预测性能，R²从0.701提升至0.785。结果表明，在类似的沿海农业条件下，将多源环境信息与可解释深度学习相结合用于近地表土壤盐渍化空间预测具有潜力。","Sensors","2026-10-13T00:00:00Z",75,{"impact":315,"substance":471,"depth":393,"authority":116,"freshness":45,"relevant":21,"comment":472},21,"方法新颖、指标可靠，将多源遥感与可解释深度学习结合用于盐渍化空间预测，对沿海农业区土壤治理有参考价值。",[474],{"name":467,"url":464},[26,27,476,320,477],"可解释AI","土壤盐渍化",{"code":31,"slug":32,"name":33,"path":34,"parent_code":35},[480,481],{"code":58,"slug":324,"name":325,"path":326,"parent_code":35},{"code":200,"slug":204,"name":482,"path":483,"parent_code":484},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":38,"locked":13,"version":39,"method":40,"evidence":486},{"candidates":487,"agricultural_evidence":46},[488,492,496,500,505],{"code":31,"score":315,"supported":46,"title":489,"abstract":490,"tags":491},[340],[320,340],[476,320],{"code":58,"score":16,"supported":46,"title":493,"abstract":494,"tags":495},[334],[336,334],[27],{"code":200,"score":174,"supported":46,"title":497,"abstract":498,"tags":499},[204],[203,204],[],{"code":285,"score":208,"supported":46,"title":501,"abstract":502,"tags":504},[],[503],"干旱",[],{"code":44,"score":21,"supported":13,"title":506,"abstract":507,"tags":508},[],[],[26],[510,511],"沧州 土壤盐渍化 遥感","Sentinel-2 土壤盐分 预测","沧州土壤盐渍化遥感-4898","10.3390\u002Fs26206465",{"doi":513,"openalex_id":515,"authors":516,"venue":467,"cited_by_count":94,"oa_url":464,"card":532,"direction":161,"ingested_from":101},"W7221715654",[517,520,523,525,528,530],{"name":518,"orcid":519},"Mingyuan Ye","https:\u002F\u002Forcid.org\u002F0009-0001-6434-5505",{"name":521,"orcid":522},"Peng Chen","https:\u002F\u002Forcid.org\u002F0000-0002-2549-6765",{"name":524,"orcid":8},"Junchi Ma",{"name":526,"orcid":527},"Bingxia Liu","https:\u002F\u002Forcid.org\u002F0000-0001-7958-7112",{"name":529,"orcid":8},"Jiahao Qi",{"name":531,"orcid":8},"Yuan Jing",{"tldr":533,"method":534,"finding":535,"direction":161,"opportunity":536},"构建融合多源遥感与环境变量的可解释深度学习框架，预测沧州土壤盐分空间分布。","Sentinel-2光谱指数+环境协变量，CNN-SE注意力，CPO优化超参数，","环境协变量与光谱特征结合将R²从0.701提升至0.785，模型性能最优。","可迁移至其他滨海盐渍区，并探索时序动态预测与多深度土壤盐分协同反演。","2026-10-14T23:30:27.313838Z"]