[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1192":3,"related-1192":71},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":66,"slug":69,"view_count":22,"doi":8,"paper":8,"created_at":70},1192,"浙江省农业数字化与食物系统韧性:基于省级时间序列数据的耦合协调与障碍因子分析","https:\u002F\u002Fwww.ebiotrade.com\u002Fnewsf\u002F2026-8\u002F20260826170030468.htm","Frontiers in Sustainable Food Systems发表浙江省农业数字化与食物系统韧性研究成果。基于2016至2024年省级时间序列数据,构建农业数字化与食物系统韧性两套评价体系,采用熵权法、耦合协调模型、相对发展分析与障碍因子模型进行实证分析。研究期内两套系统水平均提升,耦合协调度由失衡迈向高质量协调;农业数字化更常作为滞后子系统出现;主要障碍因子包括通信能力、光缆基础设施、快递运力、有效灌溉面积、农业经济支撑与农村消费能力。结论指出,农业数字化与食物系统韧性密切相关,但耦合协调结果应作为描述性证据而非因果证明。浙江省应进一步推动数字技术对农业生产、食物流通、基础设施改善与风险治理的深度融合。",null,"《Frontiers in Sustainable Food Systems》：Agricultural digitalization and food system resilience in Zhejiang Province: coupling coordination and obstacle-factor analysis based on provincial time-series data\n\n**编辑推荐：**\n\n目的：本研究考察浙江省农业数字化与食物系统韧性之间的耦合协调关系，并识别限制二者协调发展主要因素。方法：基于2016至2024年省级时间序列数据，构建农业数字化与食物系统韧性两套评价体系，采用熵权法（entropy weight method）、耦合协调模型（\n\n目的：本研究考察浙江省农业数字化与食物系统韧性之间的耦合协调关系，并识别限制二者协调发展主要因素。方法：基于2016至2024年省级时间序列数据，构建农业数字化与食物系统韧性两套评价体系，采用熵权法（entropy weight method）、耦合协调模型（coupling coordination model）、相对发展分析（relative development analysis）与障碍因子模型（obstacle-factor model）进行实证分析，并对2016至2021年做全指标敏感性分析。结果：研究期内两套系统水平均提升，耦合协调度由失衡迈向高质量协调；农业数字化更常作为滞后子系统出现；主要障碍因子包括通信能力、光缆基础设施、快递运力、有效灌溉面积、农业经济支撑与农村消费能力。结论：农业数字化与食物系统韧性密切相关，但耦合协调结果应作为描述性证据而非因果证明。浙江省应进一步推动数字技术对农业生产、食物流通、基础设施改善与风险治理的深度融合。\n\n研究背景方面，食物系统日益暴露于气候冲击、价格波动、生态退化、资源约束及产销中断等复合压力之下，学界关注点由单一粮食生产转向食物系统韧性（food system resilience，指系统在扰动下持续提供充足、可获取、稳定与可持续食物的能力）。数字化重塑了农业与食物系统，但数字优势并不自动转化为韧性，效果取决于数字基础设施与流通能力能否有效接入生产、市场稳定、生态治理与灾害响应。既有文献多聚焦全国面板或企业级因果，少有在数字经济先进省份内部诊断数字与韧性是否同步演进。浙江省电信、物流、电商与数字治理优势明显，但同时面临耕地约束、生态投入压力、价格波动与灾害风险，因此适合作为省内时间序列诊断样本，回答“数字能力是否被转化为均衡的食物系统韧性”这一非因果问题。\n\n该研究发表于《Frontiers in Sustainable Food Systems》。研究人员以浙江省2016至2024年国家统计局省级年度数据为基础，构建农业数字化子系统（含通信服务能力、主要通信基础设施、快递运力）与食物系统韧性子系统（含农业经济支撑、生产效率、可及性、价格稳定性、生态压力、农业基础设施、灾害风险；其中农药、农膜、受灾面积等仅在2016至2021敏感性分析中加入）。得出核心结论：两系统指数均上升，耦合协调度（D）由失衡升至高质量协调，但农业数字化多数为滞后方；主要障碍集中于通信能力、光缆长度、快递业务量\u002F收入、有效灌溉面积、第一产业增加值\u002F农林牧渔总产值、城乡收入与消费等。意义在于将省级内部“数字—韧性”错位、滞后子系统与瓶颈诊断出来，弥补国家均值与企业微观研究无法揭示省内系统层协调问题的缺口。\n\n关键技术方法上，研究人员采用熵权法（entropy weight method）依信息熵给各指标客观赋权，经min–max标准化（正向指标公式Z ij=(X ij?min(X j))\u002F(max(X j)?min(X j))，负向指标反向处理）得到农业数字化指数U 1i 与食物系统韧性指数U 2i；继而用耦合度C i=2×sqrt(U 1i×U 2i)\u002F(U 1i+U 2i)、综合发展指数T i=0.5U 1i+0.5U 2i、耦合协调度D i=sqrt(C i×T i)划分10级协调阶段；用相对发展度R i=U 1i\u002FU 2i 判定数字滞后或韧性滞后；用障碍度O ij=(w j×(1?Z ij))\u002FΣ(w j×(1?Z ij))×100%定位制约指标；并以2016至2021扩充生态与灾害指标做敏感性分析。样本为浙江省单省年度序列，无跨省外推。\n\n研究结果部分，4.1节“浙江省农业数字化与食物系统韧性的演变”显示，U 1 从2016年0.0000（标准化基准最低年，非无数字基础）升至2024年0.8026，中间2019年冲高后2020年回落再恢复；U 2 从0.1604升至0.8740，走势更稳且多数年份高于U 1，说明韧性整体快于数字化，但2018至2019年数字曾短暂反超。4.2节“农业数字化与食物系统韧性的耦合协调”通过C、T、D三值表明，D由早期失衡逐级跃至2024年高质量协调区间，但相对发展分析显示农业数字化更常处滞后位。4.3与4.4节（相对发展与障碍因子）指出主要障碍因子为电话普及率、移动电话普及率、光缆线路长度、长途光缆长度、快递业务量、快递业务收入、有效灌溉面积、第一产业增加值、农林牧渔总产值、城乡人均可支配收入与消费支出、食品CPI偏离|CPI i?100|等；其中多指标维度（如可及性用4个指标）在熵权下天然占更多通道，研究人员明确该排序是测量系统内的指标级诊断而非概念维度终极排序。敏感性分析中纳入农药、农膜、受灾与成灾面积后，结论方向基本稳定。\n\n讨论与结论翻译：农业数字化与食物系统韧性密切相关，但耦合协调结果应解释为描述性证据而非因果证明。浙江省应进一步推动数字技术与农业生产、食物流通、基础设施改善及风险治理的深度融合；政策优先级应放在数字基础设施联通、农资物流下沉、灌溉与生态投入优化、价格监测及灾害风险治理上，而非假定数字增长自动带来韧性均衡。研究人员强调本研究贡献为诊断型——在保持省内语境恒定下揭示时间错配、滞后子系统与协调瓶颈，互补于全国因果与企业级研究。","Frontiers in Sustainable Food Systems 2026","2026-08-26T08:00:00Z","论文",10,false,74,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,22,19,13,2,1,"基于省级时间序列数据的实证研究，揭示农业数字化与食物系统韧性的协调演进及障碍因子，对省级政策制定有参考价值。",[25],{"name":10,"url":6},[27,28,29,30],"农业数字化","食物系统韧性","耦合协调","浙江省",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,51,56,61],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":50},3,true,[],[49],"灌溉",[],{"code":52,"score":22,"supported":14,"title":53,"abstract":54,"tags":55},"I",[],[],[27],{"code":57,"score":22,"supported":14,"title":58,"abstract":59,"tags":60},"I1",[],[],[27],{"code":62,"score":22,"supported":14,"title":63,"abstract":64,"tags":65},"I4",[],[],[29],[67,68],"食物系统韧性 农业数字化 耦合协调 浙江省","食物系统韧性 农业数字化","食物系统韧性农业数字化耦合协调浙江省-1192","2026-09-01T00:03:40.255192Z",{"total":72,"page":22,"page_size":72,"items":73},6,[74,133,205,275,388,479],{"id":75,"title":76,"url":77,"summary":78,"summary_zh":8,"content":8,"source_name":79,"source_url":77,"published_at":80,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":81,"score_detail":82,"sources":85,"tags":87,"primary_topic":89,"secondary_topics":90,"classification":91,"search_phrases":110,"slug":112,"view_count":22,"doi":113,"paper":114,"created_at":132},1246,"Agricultural digitalization and food system resilience in Zhejiang Province: coupling coordination and obstacle-factor analysis based on provincial time-series data","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffsufs.2026.1908825","Objective This study examines the coupling coordination between agricultural digitalization and food system resilience in Zhejiang Province and identifies the main factors restricting their coordinated development. Methods Based on provincial time-series data from 2016 to 2024, this study constructs two evaluation systems: agricultural digitalization and food system resilience. The entropy weight method, coupling coordination model, relative development analysis, and obstacle-factor model are used for empirical analysis. A full-indicator sensitivity analysis for 2016–2021 is also conducted. Results The results show that both agricultural digitalization and food system resilience improved during the study period. The coupling coordination degree increased from imbalance to high-quality coordination. However, agricultural digitalization was more often the lagging subsystem. The main obstacle factors include communication capacity, optical cable infrastructure, express delivery capacity, effective irrigation area, agricultural economic support, and rural consumption capacity. Conclusion Agricultural digitalization is closely associated with food system resilience, but the coupling coordination results should be interpreted as descriptive evidence rather than causal proof. Zhejiang Province should further promote the deep integration of digital technologies with agricultural production, food circulation, infrastructure improvement, and risk governance.","Frontiers in Sustainable Food Systems","2026-08-25T00:00:00Z",72,{"impact":17,"substance":18,"depth":17,"authority":83,"freshness":21,"relevant":22,"comment":84},12,"基于省级时间序列数据，深入分析农业数字化与食物系统韧性的耦合协调及障碍因素，方法严谨，结论具有政策参考价值。",[86],{"name":79,"url":77},[88,27,28,29],"浙江",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":92},{"candidates":93,"agricultural_evidence":46},[94,98,102,106],{"code":32,"score":45,"supported":46,"title":95,"abstract":96,"tags":97},[],[33],[],{"code":52,"score":22,"supported":14,"title":99,"abstract":100,"tags":101},[],[],[27],{"code":57,"score":22,"supported":14,"title":103,"abstract":104,"tags":105},[],[],[27],{"code":62,"score":22,"supported":14,"title":107,"abstract":108,"tags":109},[],[],[29],[111,68],"食物系统韧性 农业数字化 耦合协调 浙江","食物系统韧性农业数字化耦合协调浙江-1246","10.3389\u002Ffsufs.2026.1908825",{"doi":113,"openalex_id":115,"authors":116,"venue":79,"cited_by_count":123,"oa_url":124,"card":125,"direction":129,"ingested_from":131},"W7204176874",[117,119,121],{"name":118,"orcid":8},"Weixin Zhu",{"name":120,"orcid":8},"Jie Wei",{"name":122,"orcid":8},"Y. Fei",0,"https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fsustainable-food-systems\u002Farticles\u002F10.3389\u002Ffsufs.2026.1908825\u002Fpdf",{"tldr":126,"method":127,"finding":128,"direction":129,"opportunity":130},"研究浙江省农业数字化与食物系统韧性的耦合协调度及障碍因素。","熵权法、耦合协调模型、相对发展分析、障碍因子模型及敏感性分析。","两者均提升，耦合协调度从失调转为优质协调，但数字化常滞后，主要障碍为通信、光缆、快递等。","数字乡村与农业信息化","可探索数字化滞后子系统如何通过基础设施和流通能力提升，促进与食物系统韧性的协同演化。","openalex","2026-09-01T04:03:09.490150Z",{"id":134,"title":135,"url":136,"summary":137,"summary_zh":138,"content":8,"source_name":139,"source_url":136,"published_at":140,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":141,"score_detail":142,"sources":146,"tags":148,"primary_topic":154,"secondary_topics":155,"classification":161,"search_phrases":186,"slug":189,"view_count":123,"doi":190,"paper":191,"created_at":204},5591,"Hierarchical Conformal Risk Control for Artificial Intelligence Agriculture Irrigation Scheduling Under Unmodeled Infiltration: A Computer Safety-Guarantee Evaluation","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23543665","Irrigation scheduling algorithms that rely on soil-moisture forecasts are vulnerable to unmodeled infiltration, the process by which water bypasses the root zone through preferential pathways that resistance-based soil models cannot represent. The framework is evaluated through a replication-based design study that characterises the operating characteristics of the before-after-control-impact estimator used to audit scheduler performance, under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64. Power was estimated across 2000 design cells spanning cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The analysis indicates that 54 clusters are required to detect a 5-percentage-point effect at 80% power, and that the minimum detectable effect declines steeply as cluster count rises from 8 to 32. The contribution is a formal safety guarantee for irrigation decision support that remains valid when the infiltration model is wrong, together with an audit design that makes the guarantee empirically checkable.","依赖土壤湿度预报的灌溉调度算法容易受到未建模入渗的影响，即水分通过优先路径绕过根区的过程，而基于阻力的土壤模型无法表征这一过程。该框架通过一项基于重复的设计研究进行评估，该研究刻画了用于审计调度器性能的前后对照影响估计量的运行特性，其所依据的声明方差结构为：组间标准差6.0、残差标准差4.5、组内相关系数0.64。功效估计覆盖2000个设计单元，涵盖聚类数8、16、24和32以及效应量3、5和8个百分点。分析表明，在80%功效下检测5个百分点效应需要54个聚类，且最小可检测效应随聚类数从8增至32而急剧下降。本文的贡献在于为灌溉决策支持提供了一种正式的安全保证，该保证在入渗模型错误时仍然有效，同时提出了一种使该保证可经实证检验的审计设计。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",69,{"impact":143,"substance":18,"depth":17,"authority":83,"freshness":144,"relevant":22,"comment":145},8,9,"该研究提出在入渗模型失配条件下仍有效的灌溉决策安全保证框架，并给出可审计的试验设计，方法新颖、结论可靠，但属细分方法学研究，产业影响有限，适合作为专业精选而非头条。",[147],{"name":139,"url":136},[149,150,151,152,153],"智慧农业","农业人工智能","灌溉决策","安全保证","试验设计",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[156],{"code":157,"slug":158,"name":159,"path":160,"parent_code":52},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":39,"locked":14,"version":40,"method":41,"evidence":162},{"candidates":163,"agricultural_evidence":46},[164,169,175,182],{"code":32,"score":165,"supported":46,"title":166,"abstract":167,"tags":168},15,[33],[49,33],[151],{"code":157,"score":83,"supported":46,"title":170,"abstract":172,"tags":174},[171],"artificial intelligence",[173],"decision support",[150],{"code":176,"score":72,"supported":46,"title":177,"abstract":178,"tags":181},"H2",[],[179,180],"土壤","soil",[],{"code":57,"score":22,"supported":14,"title":183,"abstract":184,"tags":185},[],[],[149],[187,188],"灌溉调度 安全保证","农业人工智能 安全保证 智慧农业 灌溉决策","灌溉调度安全保证-5591","10.5281\u002Fzenodo.23543665",{"doi":190,"openalex_id":192,"authors":193,"venue":139,"cited_by_count":123,"oa_url":136,"card":198,"direction":202,"ingested_from":131},"W7223131205",[194,196],{"name":195,"orcid":8},"Nassima Benkhelifa",{"name":197,"orcid":8},"Yacine Aït Ahmed",{"tldr":199,"method":200,"finding":201,"direction":202,"opportunity":203},"提出分层保形风险控制，为未建模入渗下的灌溉调度提供安全保证。","分层保形风险控制与基于重复设计的BACI审计，2000个设计单元功效分析。","检测5个百分点效应需54个集群，最小可检测效应随集群数增加陡降。","农业人工智能与决策模型","可将保形风险控制扩展到其他未建模扰动下的农业决策安全保证与审计设计。","2026-10-22T11:21:10.283561Z",{"id":206,"title":207,"url":208,"summary":209,"summary_zh":210,"content":8,"source_name":211,"source_url":208,"published_at":212,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":213,"score_detail":214,"sources":218,"tags":220,"primary_topic":226,"secondary_topics":227,"classification":228,"search_phrases":253,"slug":256,"view_count":123,"doi":257,"paper":258,"created_at":274},5575,"Wetland vegetation dynamics and its linkage with climatic, hydrological, and land-use\u002F land cover changes in the Foket floodplain of the Fetam River Watershed, Northwestern Ethiopia","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs44274-026-01136-x","Timely monitoring of wetland changes is vital for effective conservation and management of the landscape. This study, therefore, examines vegetation dynamics and their climatic, hydrological, and land-use drivers in the Foket floodplain wetland, Upper Blue Nile basin, Ethiopia. The LULC analysis was employed based on 1985, 2002, and 2020 cloud-free Landsat images. The maximum likelihood classification algorithm was applied to classify the images into eight LULC classes. The spatial and temporal dynamics of the floodplain’s surface water were further analyzed using the Global Surface Water Explorer (1984–2020), focusing on water occurrence, seasonality, transitions, and changes in surface-water dynamics. The Normalized Difference Vegetation Index (NDVI) was used to assess the temporal dynamics of wetland vegetation, while annual rainfall, temperature, and evapotranspiration data were used to examine the climatic context of vegetation change. The results reveal that the area coverage of water body and wetland decreased by 63.2% and 29%, respectively, over the study period. In contrast, cultivated land, settlement, and eucalyptus plantation had expanded by 39.8%, 280.6%, and 380%, respectively, at the expense of wetland, woodland, and shrubland. Surface-water dynamics showed a substantial decline in seasonally inundated areas and changes in the spatial distribution and persistence of surface water. This indicates the reduction of the floodplain’s hydrological connectivity. The NDVI analysis also revealed a significant increasing of wetland vegetation over 1985–2020\u002F21. Vegetation growth was very fast during 1985–2000, slowed down between 2001 and 2012, and dropped slightly from 2013 to 2020\u002F21. Analysis of climatic variables showed no major change in annual rainfall or evapotranspiration, whereas temperature exhibited a significant upward trend. The overall findings revealed the association of the floodplain vegetation dynamics with land-use land cover, hydrological, and climatic changes. This indicates that land-use land cover, hydrological, and climatic changes have contributed to the degradation of the floodplain vegetation, highlighting the urgent need for integrated management interventions that regulate agricultural expansion, grazing, and water diversion while maintaining hydrological connectivity and wetland vegetation. Clinical trial number Not applicable (NA).","及时监测湿地变化对于景观的有效保护和管理至关重要。因此，本研究考察了埃塞俄比亚上青尼罗河流域福凯特洪泛平原湿地的植被动态及其气候、水文和土地利用驱动因素。基于1985年、2002年和2020年无云Landsat影像进行土地利用\u002F土地覆盖（LULC）分析，采用最大似然分类算法将影像分为八个LULC类别。进一步利用全球地表水探索器（1984—2020年）分析洪泛平原地表水的时空动态，重点关注水的出现频率、季节性、转换及地表水动态变化。采用归一化差异植被指数（NDVI）评估湿地植被的时间动态，同时利用年降水量、气温和蒸散发数据考察植被变化的气候背景。结果表明，研究期间水体和湿地的面积覆盖率分别下降了63.2%和29%。相比之下，耕地、居民点和桉树种植园分别扩张了39.8%、280.6%和380%，侵占了湿地、林地和灌丛。地表水动态显示季节性淹没区域大幅减少，地表水的空间分布和持续性发生变化，表明洪泛平原水文连通性降低。NDVI分析还揭示1985—2020\u002F21年间湿地植被显著增加。植被生长在1985—2000年间非常迅速，2001—2012年间放缓，2013—2020\u002F21年间略有下降。气候变量分析显示年降水量或蒸散发无重大变化，而气温呈显著上升趋势。总体研究结果揭示了洪泛平原植被动态与土地利用\u002F土地覆盖、水文和气候变化的关联。这表明土地利用\u002F土地覆盖、水文和气候变化共同导致了洪泛平原植被退化，凸显了迫切需要综合管理干预措施，以调控农业扩张、放牧和引水，同时维持水文连通性和湿地植被。临床试验编号：不适用（NA）。","Discover Environment","2026-10-20T00:00:00Z",65,{"impact":143,"substance":215,"depth":216,"authority":83,"freshness":144,"relevant":22,"comment":217},20,16,"基于Landsat与NDVI的湿地植被动态实证研究，数据扎实但属区域案例，国际参考价值有限，不宜进入每日精选。",[219],{"name":211,"url":208},[221,222,223,224,225],"NDVI","埃塞俄比亚","土地利用变化","湿地遥感","水文连通性",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":229},{"candidates":230,"agricultural_evidence":46},[231,237,243,248],{"code":32,"score":72,"supported":46,"title":232,"abstract":233,"tags":236},[],[234,235],"蒸散发","evapotranspiration",[],{"code":238,"score":45,"supported":46,"title":239,"abstract":240,"tags":242},"H7",[],[241],"气候变化",[],{"code":244,"score":22,"supported":14,"title":245,"abstract":246,"tags":247},"H6",[],[],[223],{"code":249,"score":22,"supported":14,"title":250,"abstract":251,"tags":252},"I3",[],[],[221],[254,255],"土地利用变化 埃塞俄比亚 水文连通性 湿地遥感","土地利用变化 埃塞俄比亚","土地利用变化埃塞俄比亚水文连通性湿地遥感-5575","10.1007\u002Fs44274-026-01136-x",{"doi":257,"openalex_id":259,"authors":260,"venue":211,"cited_by_count":123,"oa_url":267,"card":268,"direction":272,"ingested_from":131},"W7223064056",[261,264],{"name":262,"orcid":263},"Workiyie Worie Assefa","https:\u002F\u002Forcid.org\u002F0000-0002-4029-9022",{"name":265,"orcid":266},"Belachew Getnet Eneyew","https:\u002F\u002Forcid.org\u002F0000-0003-1196-9004","https:\u002F\u002Flink.springer.com\u002Fcontent\u002Fpdf\u002F10.1007\u002Fs44274-026-01136-x.pdf",{"tldr":269,"method":270,"finding":271,"direction":272,"opportunity":273},"分析埃塞俄比亚Foket洪泛湿地植被动态及其气候、水文与土地利用驱动因素。","用Landsat影像做最大似然分类，结合全球地表水数据与NDVI及气候数据分析。","水体与湿地面积大幅减少，耕地、居民点和桉树种植扩张，温度显著上升。","农业遥感与作物表型","可结合多源遥感与水文连通性指标，量化湿地退化对农业灌溉与乡村生态服务的影响。","2026-10-22T11:20:42.476484Z",{"id":276,"title":277,"url":278,"summary":279,"summary_zh":280,"content":8,"source_name":281,"source_url":278,"published_at":140,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":282,"score_detail":283,"sources":285,"tags":287,"primary_topic":291,"secondary_topics":292,"classification":299,"search_phrases":358,"slug":361,"view_count":123,"doi":362,"paper":363,"created_at":387},5564,"Dynamic thermal imaging for crop water stress detection across phenological stages: Enhancing irrigation scheduling precision","https:\u002F\u002Fdoi.org\u002F10.14719\u002Fpst.17728","Thermal remote sensing and derived indices such as the crop water stress index (CWSI) can assess crop water status and support precision irrigation. However, most operational and artificial intelligence (AI)-based approaches rely on fixed, season-long thresholds to interpret canopy–air temperature differences and often overlook the influence of crop phenology. This review synthesises evidence from grapevine, wheat, maize, potato and several other crops on how developmental stage affects the non-water-stressed baseline (NWSB), CWSI thresholds and irrigation-scheduling algorithms. It covers CWSI theory and estimation practices; crop-specific, phenology-dependent behaviour of baselines and thresholds; a comparison of threshold-based, machine learning, Internet of Things (IoT), digital twin and Food and Agriculture Organization (FAO)-56 approaches with respect to crop phenology; and a conceptual framework for phenology-aware scheduling. Across the reviewed crops, baseline behaviour generally varied systematically across the growing season, the relationship between CWSI and crop water status generally strengthened after canopy closure and CWSI measured during reproductive stages was generally most closely related to yield. A quantitative audit of 56 AI-, thermal-imaging-, or CWSI-based studies found that 78.6 % either did not explicitly incorporate crop phenology or relied on day of year as a temporal proxy, whereas only 5.4 % explicitly encoded phenological stage. The proposed framework considers phenology explicitly in baseline estimation, threshold setting and data fusion or machine learning for irrigation scheduling; the framework will require multi-year, multi-site field validation before operational use. Future research should prioritise standardised phenology definitions and long-term, multi-location studies to establish and validate stage-specific parameters.","热红外遥感及其衍生的作物水分胁迫指数（crop water stress index, CWSI）等指标可评估作物水分状况并支持精准灌溉。然而，大多数业务化方法和基于人工智能（artificial intelligence, AI）的方法依赖固定的全生育期阈值来解释冠层—空气温差，且往往忽视作物物候的影响。本文综述了葡萄、小麦、玉米、马铃薯及其他若干作物在发育阶段如何影响非水分胁迫基线（non-water-stressed baseline, NWSB）、CWSI阈值和灌溉调度算法的相关证据。综述内容涵盖CWSI理论与估算实践；基线和阈值的作物特异性、物候依赖性行为；基于阈值、机器学习、物联网（Internet of Things, IoT）、数字孪生及联合国粮农组织（Food and Agriculture Organization, FAO）-56等方法在作物物候方面的比较；以及物候感知调度的概念框架。在所综述的作物中，基线行为在整个生长季通常呈系统性变化，CWSI与作物水分状况之间的关系在冠层封垄后总体增强，且在生殖阶段测得的CWSI与产量的关系总体最为密切。对56项基于AI、热成像或CWSI的研究进行的定量审查发现，78.6%的研究要么未明确纳入作物物候，要么依赖年积日作为时间代理变量，而仅5.4%的研究明确编码了物候阶段。所提出的框架在基线估算、阈值设定以及用于灌溉调度的数据融合或机器学习中明确考虑物候；该框架在投入业务应用前尚需多年、多站点的田间验证。未来研究应优先建立标准化的物候定义，并开展长期、多地点研究以建立和验证阶段特异性参数。","Plant Science Today",79,{"impact":17,"substance":18,"depth":17,"authority":83,"freshness":144,"relevant":22,"comment":284},"系统综述热成像与CWSI在物候期感知灌溉中的应用，含56项研究量化审计，方法学价值突出，但属综述类论文，影响偏学术层面。",[286],{"name":281,"url":278},[149,150,288,289,290],"遥感","作物水分胁迫","精准灌溉",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[293,294],{"code":157,"slug":158,"name":159,"path":160,"parent_code":52},{"code":295,"slug":296,"name":297,"path":298,"parent_code":52},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":39,"locked":14,"version":40,"method":41,"evidence":300},{"candidates":301,"agricultural_evidence":46},[302,306,312,319,326,331,338,345,352],{"code":32,"score":165,"supported":46,"title":303,"abstract":304,"tags":305},[33],[49,33],[290],{"code":157,"score":13,"supported":46,"title":307,"abstract":308,"tags":311},[],[309,310,171],"机器学习","machine learning",[150],{"code":295,"score":144,"supported":46,"title":313,"abstract":314,"tags":318},[],[315,316,317],"物联网","internet of things","IoT",[],{"code":57,"score":320,"supported":46,"title":321,"abstract":322,"tags":325},7,[],[323,324],"数字孪生","digital twin",[149],{"code":249,"score":320,"supported":46,"title":327,"abstract":328,"tags":330},[],[288,329],"remote sensing",[288],{"code":332,"score":72,"supported":46,"title":333,"abstract":334,"tags":337},"A2",[],[335,336],"小麦","wheat",[],{"code":339,"score":72,"supported":46,"title":340,"abstract":341,"tags":344},"A3",[],[342,343],"玉米","maize",[],{"code":346,"score":72,"supported":46,"title":347,"abstract":348,"tags":351},"A5",[],[349,350],"马铃薯","potato",[],{"code":353,"score":45,"supported":46,"title":354,"abstract":355,"tags":357},"B2",[],[356],"葡萄",[],[359,360],"热成像 作物水分胁迫","CWSI 灌溉调度","热成像作物水分胁迫-5564","10.14719\u002Fpst.17728",{"doi":362,"openalex_id":364,"authors":365,"venue":281,"cited_by_count":123,"oa_url":278,"card":381,"direction":386,"ingested_from":131},"W7223088921",[366,369,372,375,378],{"name":367,"orcid":368},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":370,"orcid":371},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":373,"orcid":374},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":376,"orcid":377},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":379,"orcid":380},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":382,"method":383,"finding":384,"direction":272,"opportunity":385},"综述物候期对热成像CWSI阈值的影响，提出物候感知灌溉调度框架。","综述葡萄、小麦、玉米等作物的热遥感CWSI研究，并审计56项AI\u002F热成像研究。","78.6%研究未显式纳入物候，仅5.4%编码物候期；CWSI与产量在生殖期关联最强。","可开展多站点多年物候期特异性CWSI阈值标定与标准化物候定义研究。","智慧农业 \u002F 农业物联网","2026-10-22T11:20:24.526683Z",{"id":389,"title":390,"url":391,"summary":392,"summary_zh":393,"content":8,"source_name":394,"source_url":391,"published_at":140,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":395,"score_detail":396,"sources":398,"tags":400,"primary_topic":403,"secondary_topics":404,"classification":407,"search_phrases":455,"slug":458,"view_count":123,"doi":459,"paper":460,"created_at":478},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)",80,{"impact":17,"substance":18,"depth":17,"authority":20,"freshness":144,"relevant":22,"comment":397},"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。",[399],{"name":394,"url":391},[149,150,315,401,402],"精准农业","气候韧性",{"code":157,"slug":158,"name":159,"path":160,"parent_code":52},[405,406],{"code":295,"slug":296,"name":297,"path":298,"parent_code":52},{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":408},{"candidates":409,"agricultural_evidence":46},[410,418,423,429,434,439,443,449],{"code":157,"score":17,"supported":46,"title":411,"abstract":412,"tags":417},[171],[413,309,414,310,415,416],"深度学习","计算机视觉","deep learning","computer vision",[150],{"code":57,"score":216,"supported":46,"title":419,"abstract":421,"tags":422},[420],"precision agriculture",[401,420],[149,401],{"code":295,"score":13,"supported":46,"title":424,"abstract":425,"tags":428},[],[315,426,317,427],"边缘计算","edge computing",[315],{"code":32,"score":144,"supported":46,"title":430,"abstract":431,"tags":433},[],[49,432,33],"水资源",[],{"code":238,"score":320,"supported":46,"title":435,"abstract":436,"tags":438},[],[241,437],"climate change",[402],{"code":176,"score":72,"supported":46,"title":440,"abstract":441,"tags":442},[],[179,180],[],{"code":444,"score":45,"supported":46,"title":445,"abstract":446,"tags":448},"A7",[],[447],"间作",[],{"code":450,"score":45,"supported":46,"title":451,"abstract":452,"tags":454},"F3",[],[453],"养分管理",[],[456,457],"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":459,"openalex_id":461,"authors":462,"venue":394,"cited_by_count":123,"oa_url":391,"card":473,"direction":386,"ingested_from":131},"W7223121456",[463,466,468,470],{"name":464,"orcid":465},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":467,"orcid":8},"Oluwadare OYEBODE",{"name":469,"orcid":8},"Iretiayo Adelaiye",{"name":471,"orcid":472},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":474,"method":475,"finding":476,"direction":202,"opportunity":477},"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":480,"title":481,"url":482,"summary":483,"summary_zh":484,"content":8,"source_name":139,"source_url":482,"published_at":140,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":485,"score_detail":486,"sources":488,"tags":490,"primary_topic":494,"secondary_topics":495,"classification":501,"search_phrases":527,"slug":530,"view_count":123,"doi":531,"paper":532,"created_at":542},5552,"INTEGRATING IoT, REMOTE SENSING, AND ARTIFICIAL INTELLIGENCE FOR SMART IRRIGATION: A REVIEW OF ARCHITECTURES, METHODS, AND CHALLENGES","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23531020","This research paper reviews the integration of Internet of Things (IoT), remote sensing, and Artificial Intelligence (AI) in smart irrigation systems. It discusses system architectures, intelligent irrigation techniques, automated water management, key challenges, and future research opportunities to improve water-use efficiency and promote sustainable agriculture.","本文综述了物联网（Internet of Things, IoT）、遥感与人工智能（Artificial Intelligence, AI）在智能灌溉系统中的融合应用，探讨了系统架构、智能灌溉技术、自动化水资源管理、关键挑战及未来研究方向，以提升用水效率并促进可持续农业发展。",75,{"impact":17,"substance":215,"depth":216,"authority":83,"freshness":144,"relevant":22,"comment":487},"系统综述物联网、遥感与AI融合的智能灌溉架构与方法，属智慧农业细分领域有实质增量的国际综述，值得入选。",[489],{"name":139,"url":482},[150,491,288,492,493],"农业物联网","智慧灌溉","节水农业",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[496,497],{"code":295,"slug":296,"name":297,"path":298,"parent_code":52},{"code":249,"slug":498,"name":499,"path":500,"parent_code":52},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":39,"locked":14,"version":40,"method":41,"evidence":502},{"candidates":503,"agricultural_evidence":46},[504,508,512,516,520],{"code":32,"score":19,"supported":46,"title":505,"abstract":506,"tags":507},[33],[49,432,33],[492,493],{"code":295,"score":17,"supported":46,"title":509,"abstract":510,"tags":511},[317],[315,316,317],[491],{"code":249,"score":165,"supported":46,"title":513,"abstract":514,"tags":515},[329],[288,329],[288],{"code":157,"score":83,"supported":46,"title":517,"abstract":518,"tags":519},[171],[171],[150],{"code":521,"score":72,"supported":46,"title":522,"abstract":523,"tags":526},"H8",[],[524,525],"可持续农业","sustainable agriculture",[],[528,529],"IoT 遥感 AI 智能灌溉","农业人工智能 农业物联网 智慧灌溉 节水农业","IoT遥感AI智能灌溉-5552","10.5281\u002Fzenodo.23531020",{"doi":531,"openalex_id":533,"authors":534,"venue":139,"cited_by_count":123,"oa_url":482,"card":537,"direction":386,"ingested_from":131},"W7223090389",[535],{"name":536,"orcid":8},"Patil",{"tldr":538,"method":539,"finding":540,"direction":386,"opportunity":541},"综述物联网、遥感与人工智能融合的智能灌溉系统架构、方法与挑战。","文献综述，梳理IoT、遥感与AI集成的系统架构与智能灌溉技术。","融合三者可提升用水效率并推动可持续农业，但仍面临关键挑战。","可针对多源数据融合的实时灌溉决策模型与边缘部署开展实证研究。","2026-10-22T11:20:18.028252Z"]