[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4423":3,"related-4423":106},{"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":24,"tags":26,"primary_topic":32,"secondary_topics":38,"classification":39,"search_phrases":67,"slug":70,"view_count":71,"doi":72,"paper":73,"created_at":105},4423,"A stage-weighted environmental risk and visual severity integration model for climate-smart management of cassava anthracnose in humid tropical systems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffphgy.2026.1898224","Colletotrichum anthracnose is a persistent constraint on cassava root yield and planting material quality in humid tropical systems, where calendar-based management often leads to poorly timed or excessive fungicide use. This study developed and pilot-tested the Cassava Anthracnose Risk Index (CARI), a stage-sensitive, persistence-adjusted early-warning framework integrating low-cost environmental sensing with structured visual severity scoring for climate-smart, input-reduced disease management. Microclimate sensors were deployed at CSIR-CRI Fumesua, Ghana, across a 13-month rainfed crop cycle (March 2025 – April 2026) using the susceptible landrace Debo . A stage-weighted Environmental Risk Subscore (ERS; 0–600 points, ERS%) was constructed from six environmental components, moderated by a persistence factor requiring sustained infection-favorable conditions, and stratified across three growth stages. A Visual Symptom Subscore (VSS; 0–40) was derived from standardised weekly severity assessments of 15 sentinel plants, integrated into a composite CARI (0–100) with stage-specific alert thresholds. Across 73 valid Stage 3 monitoring days, CARI classified all observations as Low Risk and issued no Amber or Red alerts, including on 37 of 73 days with sustained nocturnal high-humidity conditions that would have triggered a precautionary alert under a simple threshold approach. In the single confirmed disease episode observed, an approximately one-day interval separated the highest environmental risk signal from confirmed symptom onset; this is not presented as a validated lag value, a preceding 25-day sensor outage means antecedent conditions cannot be characterised. CARI did not issue an intervention recommendation during this episode (11–24 February 2026) because concurrent environmental risk remained below the Amber threshold. Published decision support systems for Colletotrichum anthracnose in other pathosystems report fungicide reductions of 30–70% relative to calendar-based approaches; these figures are cited only for benchmarking and were not measured here, as no comparative fungicide trial was conducted. These results provide proof of concept for the technical operability of CARI and identify the dataset and analyses required for formal predictive evaluation in a planned multi-season study; they should not be interpreted as evidence of CARI’s predictive accuracy or input-reduction performance.","炭疽病（Colletotrichum anthracnose）是湿润热带地区木薯块根产量和种植材料质量的持续制约因素，当地基于日历的防治方式常导致杀菌剂施用时机不当或过量。本研究开发并初步测试了木薯炭疽病风险指数（Cassava Anthracnose Risk Index, CARI），这是一个阶段敏感、持续性校正的预警框架，将低成本环境传感与结构化视觉严重度评分相结合，服务于气候智慧型、减投入的病害管理。微气候传感器部署于加纳CSIR-CRI Fumesua试验站，覆盖13个月雨养作物周期（2025年3月–2026年4月），供试品种为感病地方品种Debo。研究构建了阶段加权环境风险子评分（Environmental Risk Subscore, ERS；0–600分，ERS%），由六个环境组分构成，并受持续性因子调节——该因子要求感染有利条件持续存在——同时按三个生长阶段进行分层。视觉症状子评分（Visual Symptom Subscore, VSS；0–40）来源于对15株哨兵植株的标准化每周严重度评估，并与ERS整合为综合CARI（0–100），设定阶段特异性警报阈值。在73个有效的第3阶段监测日中，CARI将所有观测归类为低风险，未发出琥珀色或红色警报，其中包括73天中的37天存在持续性夜间高湿条件——若采用简单阈值方法，这些条件本会触发预防性警报。在观察到的唯一一次确诊病害事件中，最高环境风险信号与确诊症状出现之间相隔约一天；此值不作为经验证的滞后值呈现，因为此前25天的传感器中断意味着无法表征先行条件。CARI在该事件期间（2026年2月11–24日）未发出干预建议，因为同期环境风险仍低于琥珀色阈值。其他病理系统中针对炭疽病（Colletotrichum anthracnose）已发表的决策支持系统报告，相对于基于日历的方法，杀菌剂用量减少30–70%；这些数据仅用于基准参照，本研究并未测定，因为未开展比较性杀菌剂试验。上述结果为CARI的技术可操作性提供了概念验证，并明确了在计划中的多季节研究中开展正式预测评估所需的数据集和分析；不应将其解读为CARI预测准确性或减投入性能的证据。",null,"Frontiers in Plant Physiology","2026-10-06T00:00:00Z","论文",10,false,67,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,20,17,13,9,1,"方法新颖、数据翔实的木薯炭疽病早期预警概念验证研究，但仅为单点单季试点、无预测精度与减药效果验证，公共影响有限，适合作为技术前沿条目而非每日精选头条。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","木薯","气候智慧农业","病害预警","环境传感",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":47},[45,52,57,62],{"code":33,"score":46,"supported":47,"title":48,"abstract":49,"tags":51},3,true,[],[50],"decision support",[],{"code":53,"score":22,"supported":14,"title":54,"abstract":55,"tags":56},"A5",[],[],[28],{"code":58,"score":22,"supported":14,"title":59,"abstract":60,"tags":61},"H7",[],[],[29],{"code":63,"score":22,"supported":14,"title":64,"abstract":65,"tags":66},"I1",[],[],[27],[68,69],"CSIR-CRI 木薯 炭疽病","木薯炭疽病 风险指数 CARI","CSIR-CRI木薯炭疽病-4423",0,"10.3389\u002Ffphgy.2026.1898224",{"doi":72,"openalex_id":74,"authors":75,"venue":10,"cited_by_count":71,"oa_url":97,"card":98,"direction":102,"ingested_from":104},"W7220664728",[76,79,82,84,86,89,91,93,95],{"name":77,"orcid":78},"Cevdet Bulut","https:\u002F\u002Forcid.org\u002F0000-0003-1614-7990",{"name":80,"orcid":81},"Kwaku Onwona-Hwesofour Asante","https:\u002F\u002Forcid.org\u002F0000-0003-3539-3505",{"name":83,"orcid":9},"Gordon Akon-Yamga",{"name":85,"orcid":9},"Monica Ode Adu-Gyamfi",{"name":87,"orcid":88},"Zippora Appiah-Kubi","https:\u002F\u002Forcid.org\u002F0000-0001-5653-5707",{"name":90,"orcid":9},"Sarah Odame Afari",{"name":92,"orcid":9},"Josua Brett",{"name":94,"orcid":9},"Kwame Onwona Simpe",{"name":96,"orcid":9},"Reuben Nutefe Kuavedzi","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-physiology\u002Farticles\u002F10.3389\u002Ffphgy.2026.1898224\u002Fpdf",{"tldr":99,"method":100,"finding":101,"direction":102,"opportunity":103},"构建木薯炭疽病阶段加权风险指数CARI，融合环境传感与目测病情，实现气候智慧型预警。","13个月微气候传感监测加15株每周病情目测，构建ERS与VSS复合指数。","CARI全程判为低风险、未发预警，仅一次发病且环境风险信号领先症状约一天。","智慧农业 \u002F 农业物联网","需多季节数据验证预警阈值与滞后关系，并开展减药对比试验评估实际投入削减效果。","openalex","2026-10-08T23:30:11.101422Z",{"total":107,"page":22,"page_size":107,"items":108},6,[109,196,283,347,434,474],{"id":110,"title":111,"url":112,"summary":113,"summary_zh":114,"content":9,"source_name":115,"source_url":112,"published_at":116,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":117,"score_detail":118,"sources":123,"tags":125,"primary_topic":129,"secondary_topics":130,"classification":131,"search_phrases":178,"slug":181,"view_count":71,"doi":182,"paper":183,"created_at":195},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":119,"substance":119,"depth":120,"authority":121,"freshness":21,"relevant":22,"comment":122},22,18,12,"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。",[124],{"name":115,"url":112},[27,126,127,128,29],"农业人工智能","大语言模型","数字孪生",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":132},{"candidates":133,"agricultural_evidence":47},[134,143,150,157,162,168,174],{"code":33,"score":135,"supported":47,"title":136,"abstract":138,"tags":142},19,[137],"artificial intelligence",[139,137,140,141,50],"计算机视觉","computer vision","large language model",[126,127],{"code":144,"score":107,"supported":47,"title":145,"abstract":146,"tags":149},"H2",[],[147,148],"土壤","soil",[],{"code":151,"score":107,"supported":47,"title":152,"abstract":153,"tags":156},"H3",[],[154,155],"灌溉","irrigation",[],{"code":63,"score":158,"supported":47,"title":159,"abstract":160,"tags":161},5,[],[128],[128,27],{"code":163,"score":46,"supported":47,"title":164,"abstract":165,"tags":167},"F6",[],[166],"农业机器人",[],{"code":169,"score":46,"supported":47,"title":170,"abstract":171,"tags":173},"G2",[],[172],"smallholder",[],{"code":58,"score":22,"supported":14,"title":175,"abstract":176,"tags":177},[],[],[29],[179,180],"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":182,"openalex_id":184,"authors":185,"venue":115,"cited_by_count":71,"oa_url":112,"card":189,"direction":102,"ingested_from":104},"W7222823256",[186],{"name":187,"orcid":188},"Mostafa Farajpour","https:\u002F\u002Forcid.org\u002F0000-0003-4223-502X",{"tldr":190,"method":191,"finding":192,"direction":193,"opportunity":194},"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。","2026-10-18T23:30:07.666127Z",{"id":197,"title":198,"url":199,"summary":200,"summary_zh":201,"content":9,"source_name":202,"source_url":199,"published_at":203,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":204,"score_detail":205,"sources":209,"tags":211,"primary_topic":214,"secondary_topics":220,"classification":227,"search_phrases":256,"slug":259,"view_count":22,"doi":260,"paper":261,"created_at":282},4808,"Early Detection of Apple Scab in Orchards and Mildew in Vineyards Using A Novel Deep Learning Model and Multispectral Imagery","https:\u002F\u002Fdoi.org\u002F10.15832\u002Fankutbd.1898342","Agricultural diseases are a significant problem for growers as they cause substantial damage to fruits and leaves, leading to yield losses. Therefore, developing systems for early detection of plant diseases is a crucial issue. In this study, a new deep learning-based hybrid model is presented for the detection and segmentation of plant diseases, specifically apple scab disease in apple orchards and mildew disease in vineyards. Multispectral imagery data collected in agricultural areas of Tekirdag province in Turkey via drones between May 2023 and December 2024 were used in developing the early disease detection system. The datasets contain 3625 images of apple orchards and 5625 images of vineyards. DJI Phantom 4 Multispectral drone, equipped with five spectral bands (Blue, Green, Red, Red Edge, and Near Infrared), was utilized to capture both spectral richness and temporal progression. This study introduces a novel deep learning architecture developed specifically for time series-based disease detection and segmentation using multispectral drone imagery. Changes in plants over time provide important information about the occurrence of plant diseases. For this reason, time series were used in this study to extract features from the changes in plants over time. The proposed model architecture was specifically designed to leverage time series information, enabling robust tracking of crop health changes over time. The model trained with the dataset collected for apple diseases achieved an average Intersection Over Unit (IoU) value of 82.05%, while the model trained with the dataset collected for vineyard diseases reached an IoU value of 79.67%. The findings highlight the effectiveness of integrating drone-acquired multispectral time-series data with advanced deep learning models for scalable, accurate, and timely plant disease detection in precision agriculture.","农业病害是种植者面临的重大问题，会对果实和叶片造成严重损害，导致产量损失。因此，开发植物病害早期检测系统是一个至关重要的问题。本研究提出了一种基于深度学习的新型混合模型，用于植物病害的检测与分割，具体针对苹果园中的苹果黑星病和葡萄园中的霜霉病。该早期病害检测系统的开发使用了2023年5月至2024年12月期间通过无人机在土耳其泰基尔达省农业区域采集的多光谱影像数据。数据集包含3625幅苹果园图像和5625幅葡萄园图像。所用DJI Phantom 4多光谱无人机配备五个光谱波段（蓝、绿、红、红边和近红外），用于捕捉光谱丰富度和时间变化过程。本研究提出了一种专门开发的深度学习架构，用于基于时间序列的多光谱无人机影像病害检测与分割。植物随时间的变化提供了关于植物病害发生的重要信息。因此，本研究使用时间序列从植物随时间的变化中提取特征。所提出的模型架构专门设计以利用时间序列信息，从而能够稳健地跟踪作物健康状况随时间的变化。使用苹果病害数据集训练的模型达到了82.05%的平均交并比（IoU）值，而使用葡萄园病害数据集训练的模型达到了79.67%的IoU值。研究结果凸显了将无人机获取的多光谱时间序列数据与先进深度学习模型相结合，在精准农业中实现可扩展、准确且及时的植物病害检测方面的有效性。","Tarım Bilimleri Dergisi","2026-10-12T00:00:00Z",77,{"impact":206,"substance":207,"depth":120,"authority":20,"freshness":21,"relevant":22,"comment":208},16,21,"该研究提出基于多光谱时序影像的新型深度学习模型，实现果园与葡萄园病害早期检测，数据规模大、方法新颖，对精准农业病害预警具有参考价值。",[210],{"name":202,"url":199},[27,212,126,213,30],"无人机","遥感监测",{"code":215,"slug":216,"name":217,"path":218,"parent_code":219},"B2","fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[221,222],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"code":223,"slug":224,"name":225,"path":226,"parent_code":37},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":40,"locked":14,"version":41,"method":42,"evidence":228},{"candidates":229,"agricultural_evidence":47},[230,237,244,249],{"code":215,"score":19,"supported":47,"title":231,"abstract":233,"tags":236},[232],"apple",[234,235,232],"苹果","葡萄",[],{"code":33,"score":238,"supported":47,"title":239,"abstract":241,"tags":243},15,[240],"deep learning",[242,240],"深度学习",[126],{"code":223,"score":121,"supported":47,"title":245,"abstract":247,"tags":248},[246],"multispectral",[246],[213],{"code":63,"score":250,"supported":47,"title":251,"abstract":252,"tags":255},7,[],[253,254],"精准农业","precision agriculture",[27],[257,258],"苹果黑星病 无人机 多光谱","葡萄园 霜霉病 深度学习","苹果黑星病无人机多光谱-4808","10.15832\u002Fankutbd.1898342",{"doi":260,"openalex_id":262,"authors":263,"venue":202,"cited_by_count":71,"oa_url":275,"card":276,"direction":102,"ingested_from":104},"W7221558726",[264,267,270,272],{"name":265,"orcid":266},"Batuhan Aşıroğlu","https:\u002F\u002Forcid.org\u002F0000-0003-0767-6348",{"name":268,"orcid":269},"Aydın Kağan Turkoglu","https:\u002F\u002Forcid.org\u002F0009-0001-8383-8807",{"name":271,"orcid":9},"Fatih Sinan Bir",{"name":273,"orcid":274},"Sibel Senan","https:\u002F\u002Forcid.org\u002F0000-0001-6773-0428","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5761152",{"tldr":277,"method":278,"finding":279,"direction":280,"opportunity":281},"提出一种基于无人机多光谱时序影像的深度学习混合模型，实现苹果黑星病和葡萄霉病的早期检测与分割。","DJI Phantom 4多光谱无人机采集时序影像，构建时序深度学习分割模型。","苹果病IoU达82.05%，葡萄病达79.67%，验证多光谱时序数据有效性。","农业遥感与作物表型","可探索多作物、多病害的时序多光谱通用模型，并融合气象与地面数据提升早期预警能力。","2026-10-13T23:30:10.242467Z",{"id":284,"title":285,"url":286,"summary":287,"summary_zh":288,"content":9,"source_name":289,"source_url":286,"published_at":290,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":291,"score_detail":292,"sources":294,"tags":296,"primary_topic":298,"secondary_topics":299,"classification":300,"search_phrases":331,"slug":334,"view_count":22,"doi":335,"paper":336,"created_at":346},3899,"Leveraging on Artificial Intelligence Technology in Boosting Cassava Production in Nigeria: The Gains and Constraints","https:\u002F\u002Fdoi.org\u002F10.64348\u002Fzije.2026560","Artificial Intelligence (AI) technology is reshaping agricultural landscape globally by employing data-driven, efficient, and scalable production methods. Nigeria, as the world’s largest producer of cassava, faces persistent challenges like low yield per hectare, pest infestations, climate variability, and inefficient farming techniques. This study focuses on the potential of AI technology to exploit and enhance the nation’s agricultural potentials and productivity. The study decisively examines the application of AI in cassava production in Nigeria, focusing on its gains and constraints. It adopted ways to addressing the critical challenges like climate variability, scarce access to digital farming techniques, and the need for sustainable practices. Using a descriptive and analytical approach based on secondary data from FAO, NBS, IITA, and recent scholarly publications, the study explores AI-driven technologies such as precision agriculture, machine learning, remote sensing, and automation. Findings from the study reveal that AI technology application in cassava production can significantly improve productivity, optimize resource utilization, and reduce post-harvest losses. However, adoption can be constrained by high costs, poor infrastructure, low digital literacy, and weak policy frameworks. This paper investigates how AI technology can be employed to achieve the mass production of cassava in Nigeria, while critically examining the associated gains or benefits and constraints.","人工智能（AI）技术正通过采用数据驱动、高效且可扩展的生产方式重塑全球农业格局。尼日利亚作为世界最大的木薯生产国，长期面临单位面积产量低、病虫害侵袭、气候多变以及耕作技术低效等挑战。本研究聚焦于AI技术在开发与提升该国农业潜力及生产力方面的潜力，系统考察AI在尼日利亚木薯生产中的应用，重点关注其收益与制约因素。研究探讨了应对气候多变、数字农业技术获取困难以及可持续实践需求等关键挑战的途径。基于联合国粮农组织（FAO）、尼日利亚国家统计局（NBS）、国际热带农业研究所（IITA）及近期学术文献的二手数据，采用描述性与分析性方法，探讨了精准农业、机器学习、遥感及自动化等AI驱动技术。研究发现，AI技术在木薯生产中的应用可显著提高生产力、优化资源利用并减少采后损失。然而，其推广可能受制于高成本、基础设施薄弱、数字素养偏低及政策框架不健全等因素。本文探讨如何运用AI技术实现尼日利亚木薯的大规模生产，同时批判性地审视相关收益与制约因素。","Federal University Gusau Faculty of Education Journal","2026-09-30T00:00:00Z",69,{"impact":120,"substance":206,"depth":238,"authority":121,"freshness":17,"relevant":22,"comment":293},"基于二手数据的综述性论文，系统梳理AI在尼日利亚木薯生产中的增益与制约，对非洲农业数字化有参考价值，但方法创新与一手数据有限。",[295],{"name":289,"url":286},[27,126,253,28,297],"尼日利亚",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":301},{"candidates":302,"agricultural_evidence":47},[303,309,315,321,327],{"code":33,"score":120,"supported":47,"title":304,"abstract":305,"tags":308},[137],[306,307,137],"机器学习","machine learning",[126],{"code":63,"score":310,"supported":47,"title":311,"abstract":312,"tags":314},11,[],[253,313,254],"数字农业",[27,253],{"code":223,"score":107,"supported":47,"title":316,"abstract":317,"tags":320},[],[318,319],"遥感","remote sensing",[],{"code":322,"score":46,"supported":47,"title":323,"abstract":324,"tags":326},"J3",[],[325],"全球农业",[],{"code":53,"score":22,"supported":14,"title":328,"abstract":329,"tags":330},[],[],[28],[332,333],"尼日利亚 木薯 人工智能","木薯 精准农业 遥感","尼日利亚木薯人工智能-3899","10.64348\u002Fzije.2026560",{"doi":335,"openalex_id":337,"authors":338,"venue":289,"cited_by_count":71,"oa_url":286,"card":341,"direction":280,"ingested_from":104},"W7214923259",[339],{"name":340,"orcid":9},"Richard Igbunu",{"tldr":342,"method":343,"finding":344,"direction":193,"opportunity":345},"分析AI在尼日利亚木薯生产中的应用收益与制约因素。","基于FAO、NBS、IITA等二手数据的描述性与分析性研究。","AI可提升木薯产量、优化资源利用并减少采后损失，但受成本、基础设施、数字素养和政策制约。","可针对小农户低成本AI工具、数字素养培训与政策框架开展实证研究。","2026-10-01T23:30:29.024444Z",{"id":348,"title":349,"url":350,"summary":351,"summary_zh":352,"content":9,"source_name":353,"source_url":350,"published_at":354,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":355,"score_detail":356,"sources":359,"tags":361,"primary_topic":364,"secondary_topics":365,"classification":366,"search_phrases":388,"slug":391,"view_count":392,"doi":393,"paper":394,"created_at":433},3467,"Smart agriculture using digital holography and artificial intelligence","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1871510","In viticulture, direct protection against downy and powdery mildews relies on the preventive application of fungicides, requiring growers to anticipate infection events. Decision-making is mainly supported by forecasting models driven by weather predictions. However, these decisions are inherently uncertain, as some treatments ultimately prove unnecessary, although this information only becomes available retrospectively from observed conditions. This study explores the integration of an advanced spore detection device to better target fungicide applications in vineyards, aiming to reduce the use of phytosanitary products while maintaining high grape quality. The stand-alone device uses digital holography combined with artificial intelligence (AI) for detecting and classifying airborne spores of both downy and powdery mildew. It enables the tracking of disease dynamics as well as the assessment of environmental conditions and treatment effects on spore counts. Two case studies with real-time data access in Changins, Switzerland and Château le Puy, France, are presented and revealed promising strategies for substantial reductions in fungicide use while maintaining effective disease control.","在葡萄栽培中，对霜霉病和白粉病的直接防护依赖于杀菌剂的预防性施用，这要求种植者预判侵染事件的发生。决策主要依靠由天气预报驱动的预测模型来支持。然而，这些决策本质上具有不确定性，因为有些处理最终被证明是不必要的，尽管这一信息只能通过观测条件回顾性地获得。本研究探索了集成先进孢子检测装置以更精准地指导葡萄园杀菌剂施用的方法，旨在减少植物检疫产品的使用，同时保持葡萄的高品质。该独立装置利用数字全息术结合人工智能（AI）来检测和分类空气中的霜霉病和白粉病孢子。它能够追踪病害动态，并评估环境条件和处理措施对孢子数量的影响。本文介绍了在瑞士Changins和法国Château le Puy进行的两个可实时获取数据的案例研究，并揭示了在保持有效病害控制的同时大幅减少杀菌剂用量的有前景的策略。","Frontiers in Plant Science","2026-09-24T00:00:00Z",80,{"impact":120,"substance":119,"depth":120,"authority":357,"freshness":17,"relevant":22,"comment":358},14,"数字全息结合AI检测葡萄病害孢子，为减少杀菌剂施用提供实证案例，方法新颖且具推广价值。",[360],{"name":353,"url":350},[27,126,362,363,30],"精准施药","葡萄种植",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":367},{"candidates":368,"agricultural_evidence":47},[369,373,378,383],{"code":33,"score":121,"supported":47,"title":370,"abstract":371,"tags":372},[137],[137],[126],{"code":63,"score":21,"supported":47,"title":374,"abstract":376,"tags":377},[375],"smart agriculture",[],[27],{"code":215,"score":250,"supported":47,"title":379,"abstract":380,"tags":382},[],[235,381],"grape",[363],{"code":384,"score":22,"supported":14,"title":385,"abstract":386,"tags":387},"F4",[],[],[362],[389,390],"数字全息 孢子检测 葡萄","霜霉病 白粉病 人工智能","数字全息孢子检测葡萄-3467",4,"10.3389\u002Ffpls.2026.1871510",{"doi":393,"openalex_id":395,"authors":396,"venue":353,"cited_by_count":71,"oa_url":350,"card":428,"direction":102,"ingested_from":104},"W7214152870",[397,399,401,404,407,410,413,415,417,420,423,426],{"name":398,"orcid":9},"Tessa Chiara Basso",{"name":400,"orcid":9},"Sara Leoni",{"name":402,"orcid":403},"Denis Ullmann","https:\u002F\u002Forcid.org\u002F0000-0002-7179-005X",{"name":405,"orcid":406},"Adimulya Kartiyasa","https:\u002F\u002Forcid.org\u002F0009-0003-3142-1174",{"name":408,"orcid":409},"Livio Ruzzante","https:\u002F\u002Forcid.org\u002F0000-0002-8693-8678",{"name":411,"orcid":412},"Sylvain Schnée","https:\u002F\u002Forcid.org\u002F0000-0002-1014-1961",{"name":414,"orcid":9},"Anne‐Lise Fabre",{"name":416,"orcid":9},"Steven Hewison",{"name":418,"orcid":419},"Jérôme Kasparian","https:\u002F\u002Forcid.org\u002F0000-0003-2398-3882",{"name":421,"orcid":422},"Nicolas Berti","https:\u002F\u002Forcid.org\u002F0000-0003-3769-3966",{"name":424,"orcid":425},"Pierre‐Henri Dubuis","https:\u002F\u002Forcid.org\u002F0000-0002-9624-3925",{"name":427,"orcid":9},"Jean-Pierre Wolf",{"tldr":429,"method":430,"finding":431,"direction":102,"opportunity":432},"利用数字全息与AI检测葡萄园空气中霜霉和白粉病菌孢子，优化杀菌剂施用。","数字全息成像结合AI分类孢子，在瑞士和法国葡萄园实时监测。","孢子检测可追踪病害动态，在保持防效下大幅减少杀菌剂使用。","可探索孢子自动监测与气象预报融合的精准施药决策模型，并验证多作物适用性。","2026-09-25T23:30:09.459179Z",{"id":435,"title":436,"url":437,"summary":438,"summary_zh":9,"content":439,"source_name":440,"source_url":9,"published_at":441,"category":442,"cover_url":9,"hotness":13,"is_selected":14,"score":443,"score_detail":444,"sources":446,"tags":448,"primary_topic":451,"secondary_topics":452,"classification":453,"search_phrases":469,"slug":472,"view_count":22,"doi":9,"paper":9,"created_at":473},432,"中国热带农业科学院发布'树薯智汇1.0'木薯产业垂类大模型——热带农业人工智能创新联盟成立","http:\u002F\u002Fwww.hq.xinhua.org\u002F20260808\u002F0a4e336974634a66939cc0d35a15ade8\u002Fc.html","8月6日,中国热带农业科学院科技信息研究所在海口发布'树薯智汇1.0'大模型,这是覆盖全产业链的木薯产业垂类大模型。系统整合品种资料、病虫草害图谱、产业数据、行业标准和专家经验,设置产业行情、品种推荐、病虫草害识别、品质智能分析、繁育栽培、木薯科普、木薯厨房、行业标准八大核心功能模块,通过微信小程序等便捷入口提供给农户、农技人员、收购商、加工企业及科研人员。中国热科院信息所、海南农科院、广东农科院、云南农科院经信所等9家单位联合成立'木薯产业人工智能创新联盟'。","木薯作为我国热带、亚热带地区重要的粮食作物和工业原料，广泛应用于生产淀粉、饲料、食品、生物能源等产品。广西、海南、广东、云南等地拥有较好的木薯产业基础，但在实际生产经营中，品种和技术信息较为分散、市场行情获取渠道有限、上下游供需对接效率不高等问题，仍在一定程度上制约产业提质增效。\n\n中国热带农业科学院科技信息研究所所长叶剑秋介绍，“树薯智汇1.0”大模型正是从这些具体问题出发，将分散的专业知识、产业数据和专家经验进行系统整合，面向木薯种植、收购、加工、科普和标准服务等全产业链场景，通过微信小程序等便捷入口，为农户、农技人员、收购商、加工企业及科研人员提供智能服务，让科研成果从“资料库”走向生产一线，让专业服务从“人找信息”逐步转向“信息找人”。\n\n对于想了解木薯安全食用和加工方法的普通用户，平台设置了木薯科普、木薯厨房等模块；针对企业和从业人员，平台还可提供原料验收、质量检测、储运规范和行业标准等方面的查询服务，形成覆盖木薯产业关键环节的核心功能。","新华网海南 2026-08-08","2026-08-08T00:00:00Z","报道",72,{"impact":120,"substance":18,"depth":238,"authority":20,"freshness":107,"relevant":22,"comment":445},"国家级科研机构发布木薯产业垂类大模型，面向全产业链提供智能服务，具有产业级影响，信息增量明确，但报道深度一般，时效性稍差。",[447],{"name":440,"url":437},[27,28,449,450],"热带农业","垂类大模型",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":454},{"candidates":455,"agricultural_evidence":47},[456,461,465],{"code":33,"score":135,"supported":47,"title":457,"abstract":459,"tags":460},[126,458],"大模型",[458],[],{"code":53,"score":22,"supported":14,"title":462,"abstract":463,"tags":464},[],[],[28],{"code":63,"score":22,"supported":14,"title":466,"abstract":467,"tags":468},[],[],[27],[470,471],"垂类大模型 智慧农业 热带农业 木薯","垂类大模型 智慧农业","垂类大模型智慧农业热带农业木薯-432","2026-08-12T23:57:20.540629Z",{"id":475,"title":476,"url":477,"summary":478,"summary_zh":479,"content":9,"source_name":480,"source_url":477,"published_at":481,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":291,"score_detail":482,"sources":484,"tags":486,"primary_topic":490,"secondary_topics":495,"classification":497,"search_phrases":512,"slug":515,"view_count":71,"doi":516,"paper":517,"created_at":531},5592,"Evaluation of Concept-Rank Collapse Audits for Computer Vision Agriculture Disease Classifiers Under Low-Label Regimes: An Interpretability Capacity Bound for Smallholder Fungicide Selectivity","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545193","Concept-based interpretability methods for plant-disease classifiers are typically evaluated by whether their concept activations correlate with human-annotated labels, but this criterion is silent on a prior question: whether the classifier's internal representation retains enough concept-rank structure for any audit to be informative. the present analysis formalises that prior question as an interpretability capacity bound. A classifier is modelled as a concept-rank operator whose singular values govern how much concept-level signal survives the projection from pixel space to class logits, and a collapse audit is defined as the ratio of retained concept-rank energy to total concept energy. Under a low-label regime, where concept annotations are scarce and the audit must be estimated from few labelled examples, the bound predicts that audit reliability degrades as a function of the concept-rank spectrum and the label budget. The analysis is a theoretical and methodological contribution: it derives the bound, states the conditions under which a collapse audit is identifiable, and characterises the operating characteristics of the audit as a decision procedure under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64). Power was estimated across 2000 design cells for cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points; 52 clusters were required for 5 percentage points at 80% power. The bound reframes concept audits as capacity tests rather than fidelity tests, with consequences for how smallholder fungicide-selectivity decisions should be supported by interpretable disease classifiers.","面向植物病害分类器的基于概念的可解释性方法，通常以其概念激活是否与人工标注标签相关作为评价标准，但该标准对一个更先前的问题保持沉默：分类器的内部表征是否保留了足够的概念秩结构，从而使任何审计具有信息量。本分析将该先前问题形式化为一个可解释性容量上界。分类器被建模为概念秩算子，其奇异值支配着从像素空间到类别logits的投影中概念级信号得以保留的程度，并将坍缩审计定义为保留的概念秩能量与总概念能量之比。在低标签情形下，概念标注稀缺且审计必须由少量标注样本估计，该上界预测审计可靠性会随概念秩谱和标签预算的函数而退化。本分析是一项理论和方法论贡献：推导出该上界，陈述坍缩审计可识别的条件，并在声明的方差结构（簇间SD = 6.0，残差SD = 4.5，组内相关系数 = 0.64）下刻画审计作为决策程序的操作特征。在2000个设计单元中，针对簇数为8、16、24和32以及效应量为3、5和8个百分点的情形估计了功效；在80%功效下达到5个百分点需要52个簇。该上界将概念审计重新框定为容量检验而非保真度检验，并对可解释病害分类器应如何支持小农杀菌剂选择性决策具有启示意义。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",{"impact":17,"substance":207,"depth":120,"authority":121,"freshness":13,"relevant":22,"comment":483},"理论方法论文，提出可解释性容量上界并给出审计功效分析，对智慧农业病害识别可信度有参考价值，但属小众方法学研究、缺乏田间实证，暂不宜进入每日精选。",[485],{"name":480,"url":477},[27,126,487,488,489],"小农户","植物病害识别","可解释性",{"code":169,"slug":491,"name":492,"path":493,"parent_code":494},"farm-services","社会化服务与规模经营","\u002Ftopic\u002Frural-services\u002Ffarm-services","G",[496],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},{"status":40,"locked":14,"version":41,"method":42,"evidence":498},{"candidates":499,"agricultural_evidence":47},[500,504,508],{"code":169,"score":121,"supported":47,"title":501,"abstract":502,"tags":503},[172],[172],[487],{"code":33,"score":13,"supported":47,"title":505,"abstract":506,"tags":507},[140],[],[126,488],{"code":63,"score":22,"supported":14,"title":509,"abstract":510,"tags":511},[],[],[27],[513,514],"农业人工智能 植物病害识别 可解释性 智慧农业","农业人工智能 植物病害识别","农业人工智能植物病害识别可解释性智慧农业-5592","10.5281\u002Fzenodo.23545193",{"doi":516,"openalex_id":518,"authors":519,"venue":480,"cited_by_count":71,"oa_url":477,"card":526,"direction":193,"ingested_from":104},"W7223137323",[520,522,524],{"name":521,"orcid":9},"Akshay Ramgoolam",{"name":523,"orcid":9},"Priya Bundhoo Appadoo",{"name":525,"orcid":9},"Jean-Noël LEspérance",{"tldr":527,"method":528,"finding":529,"direction":193,"opportunity":530},"提出可解释性容量上界，评估低标签下概念秩崩溃审计的可靠性。","将分类器建模为概念秩算子，推导容量上界并做2000格功效模拟。","审计可靠性随概念秩谱与标签预算下降；5个百分点效应需52个聚类达80%功效。","可将容量上界用于小农户杀菌剂选择性决策的可解释模型审计与标签预算设计。","2026-10-22T11:21:10.425290Z"]