[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1984":3,"related-1984":90},{"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":22,"tags":24,"primary_topic":29,"secondary_topics":35,"classification":36,"search_phrases":64,"slug":67,"view_count":20,"doi":68,"paper":69,"created_at":89},1984,"Design, construction, and calibration of an automated weighing lysimeter to support climate-smart agriculture in the Colombian Orinoquía","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffeart.2026.1910946","In tropical agricultural regions, accurate estimation of reference evapotranspiration (ET 0 ) remains challenging due to climatic variability, data scarcity, and limitations of empirical models such as FAO-56 Penman–Monteith. To provide the technical basis for future field-based evapotranspiration studies, this study reports the design, construction, and calibration of an automated weighing lysimeter installed at the La Libertad Research Center in Villavicencio, Colombia. The system integrates a galvanized steel tank (1 m 3 ) reconstructed with original soil horizons, an automated sprinkler irrigation system, and a monitoring module based on load cells, soil moisture sensors, a professional weather station, and real-time data transmission powered by a photovoltaic unit. Calibration was performed using incremental load–unload cycles, achieving linear response curves with R 2 > 0.99, while soil water retention curves were developed to define irrigation thresholds. The results document the successful construction and integration of the system and show highly linear load-cell responses, also the capacity to provide high-resolution measurements under equatorial conditions, this could reduce methodological uncertainties inherent to model-based approaches. The developed system provides the technical infrastructure required for future evapotranspiration monitoring and comparison with established ET 0 estimation methods. Overall, the proposed lysimeter may support future irrigation-management, water-resource assessment, and climate-smart agriculture applications in tropical regions.","在热带农业地区，由于气候变率、数据稀缺以及FAO-56 Penman–Monteith等经验模型的局限性，参考蒸散量（ET₀）的准确估算仍面临挑战。为未来基于田间的蒸散研究提供技术基础，本研究报道了安装在哥伦比亚比亚维森西奥La Libertad研究中心的一套自动称重蒸渗仪的设计、建造与校准过程。该系统集成了一个按原始土壤层次重构的镀锌钢桶（1 m³）、一套自动喷灌系统，以及基于称重传感器、土壤水分传感器、专业气象站和由光伏单元供电的实时数据传输的监测模块。校准采用递增加载–卸载循环进行，实现了R² > 0.99的线性响应曲线，同时建立了土壤水分特征曲线以确定灌溉阈值。结果记录了系统的成功建造与集成，显示出称重传感器高度线性的响应，并具备在赤道条件下提供高分辨率测量的能力，这可减少基于模型方法固有的方法学不确定性。所开发的系统为未来蒸散监测及与既有ET₀估算方法的比较提供了所需的技术基础设施。总体而言，所提出的蒸渗仪可支持热带地区未来的灌溉管理、水资源评估和气候智慧型农业应用。",null,"Frontiers in Earth Science","2026-09-08T00:00:00Z","论文",10,false,62,{"impact":17,"substance":18,"depth":18,"authority":17,"freshness":19,"relevant":20,"comment":21},12,18,2,1,"论文详述了自动称重蒸渗仪的设计与校准，为热带地区精准灌溉提供技术基础，但时效性较低。",[23],{"name":10,"url":6},[25,26,27,28],"智慧农业","蒸散测量","灌溉管理","仪器研发",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",[],{"status":37,"locked":14,"version":38,"method":39,"evidence":40},"classified","2026-10-22.2","rules",{"candidates":41,"agricultural_evidence":44},[42,49,56],{"code":43,"score":17,"supported":44,"title":45,"abstract":47,"tags":48},"I1",true,[46],"smart agriculture",[46],[25],{"code":30,"score":13,"supported":44,"title":50,"abstract":51,"tags":55},[],[52,53,31,54],"灌溉","水资源","evapotranspiration",[27],{"code":57,"score":58,"supported":44,"title":59,"abstract":60,"tags":63},"H2",6,[],[61,62],"土壤","soil",[],[65,66],"仪器研发 智慧农业 灌溉管理 蒸散测量","仪器研发 智慧农业","仪器研发智慧农业灌溉管理蒸散测量-1984","10.3389\u002Ffeart.2026.1910946",{"doi":68,"openalex_id":70,"authors":71,"venue":10,"cited_by_count":79,"oa_url":80,"card":81,"direction":87,"ingested_from":88},"W7211943188",[72,74,76],{"name":73,"orcid":9},"Edwin Sebastián Caceres Garcia",{"name":75,"orcid":9},"Christian José Mendoza Castiblanco",{"name":77,"orcid":78},"Andrés Javier Peña Quiñones","https:\u002F\u002Forcid.org\u002F0000-0003-3564-401X",0,"https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fearth-science\u002Farticles\u002F10.3389\u002Ffeart.2026.1910946\u002Fpdf",{"tldr":82,"method":83,"finding":84,"direction":85,"opportunity":86},"设计并校准了自动化称重蒸渗仪，用于热带地区参考蒸散量测量，支持气候智慧农业。","集成称重传感器、土壤水分传感器、气象站和光伏供电的自动化蒸渗仪，进行加载-卸载校","系统线性响应R²>0.99，能高分辨率测量，减少模型不确定性，支持灌溉管理。","其他","可延伸研究蒸渗仪与遥感数据融合，或开发低成本版本以推广至其他热带地区。","智慧农业 \u002F 农业物联网","openalex","2026-09-09T23:30:08.720482Z",{"total":58,"page":20,"page_size":58,"items":91},[92,193,276,377,439,542],{"id":93,"title":94,"url":95,"summary":96,"summary_zh":97,"content":9,"source_name":98,"source_url":95,"published_at":99,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":100,"score_detail":101,"sources":107,"tags":109,"primary_topic":113,"secondary_topics":118,"classification":120,"search_phrases":168,"slug":171,"view_count":79,"doi":172,"paper":173,"created_at":192},4242,"Integrated Irrigation and Disease Management Strategies for Sustainable Wheat Production and Improved Plant Health and Grain Yield","https:\u002F\u002Fdoi.org\u002F10.71317\u002Fkjard.2.10.2026.774","Wheat production was expected to provide reliable grain quality and farmer income while using less water and facing climatic variability, soil degradation, disease development, and rising energy costs. This literature-based paper critically synthesized verified research on irrigation, plant disease management, irrigation–disease interactions, sustainability, digital agriculture, and the Pakistan\u002Fdeveloping-economy context. It considered irrigation and plant health as related management areas rather than completely separate input decisions. The available evidence did not identify a universal irrigation technology or deficit percentage. Instead, it supported a context-specific approach that considered stored soil water and rainfall, protected important growth stages, managed canopy humidity and nitrogen, used resistant cultivars and clean seed, and applied surveillance, forecasting, and fungicides when justified. Global and China-focused studies reported that moderate deficit or optimized water-saving irrigation could improve water productivity, whereas severe water deficit commonly reduced yield. Jointing\u002Fstem elongation was repeatedly identified as an important stage for avoiding water stress, while anthesis and early grain filling were also important when water availability and disease risk required attention. Disease-management evidence emphasized genetic resistance, sanitation, crop rotation, scouting, and forecasting before chemical control. Fusarium head blight required particular attention to weather conditions around anthesis, residue inoculum, cultivar resistance, fungicide timing, and deoxynivalenol risk. Irrigation increased yield without increasing disease in some environments, although dense nitrogen-rich canopies, overhead wetting, and saturated soils could favor foliar diseases and waterlogging. Digital tools were considered decision-support infrastructure that linked observations, analysis, and management actions. Wheat-specific evidence was relatively stronger for soil-moisture scheduling and weather-based stripe-rust forecasting, whereas many claims involving artificial intelligence, UAVs, and variable-rate irrigation remained predictive or transferable rather than directly supported by causal field evidence. Evidence from Pakistan indicated dependence on groundwater, salinity and waterlogging problems, uneven disease surveillance, and barriers to technology adoption. Differences among official statistics also indicated the need for careful reconciliation rather than simple averaging. The paper proposed an integrated framework involving diagnosis, planning, stage-aware irrigation, plant-health protection, evaluation, and seasonal feedback. It also identified the need for multi-location and multi-season studies that evaluated water use, disease, grain quality, energy use, greenhouse-gas emissions, profitability, and equity together.","小麦生产被期望在提供可靠籽粒品质和农民收入的同时，减少用水并应对气候变异、土壤退化、病害发展和能源成本上升。这篇基于文献的论文批判性地综合了关于灌溉、植物病害管理、灌溉–病害相互作用、可持续性、数字农业以及巴基斯坦\u002F发展中经济体背景的已验证研究。它将灌溉和植物健康视为相关的管理领域，而非完全独立的投入决策。现有证据并未确定一种通用的灌溉技术或亏缺百分比。相反，它支持一种因地制宜的方法，即考虑土壤储水和降雨、保护重要生育阶段、管理冠层湿度和氮素、使用抗性品种和清洁种子，并在有依据时应用监测、预报和杀菌剂。全球和中国的研究报告称，适度亏缺或优化节水灌溉可提高水分生产力，而严重水分亏缺通常降低产量。拔节\u002F茎伸长阶段被反复确定为避免水分胁迫的重要阶段，而在水分可用性和病害风险需要关注时，开花期和灌浆早期也很重要。病害管理证据强调在化学防治之前采用遗传抗性、卫生措施、轮作、田间巡查和预报。赤霉病需要特别关注开花期前后的天气条件、残体接种体、品种抗性、杀菌剂施用时机和脱氧雪腐镰刀菌烯醇风险。在某些环境中，灌溉提高了产量而未增加病害，尽管密集的富氮冠层、 overhead wetting 和饱和土壤可能有利于叶部病害和渍害。数字工具被视为连接观测、分析和管理行动的决策支持基础设施。小麦-specific 证据在土壤水分调度和基于天气的条锈病预报方面相对更强，而许多涉及人工智能、无人机和变量灌溉的说法仍属于预测性或可迁移性，而非直接得到因果田间证据的支持。来自巴基斯坦的证据表明对地下水的依赖、盐渍化和渍害问题、病害监测不均衡以及技术采用的障碍。官方统计数据之间的差异也表明需要谨慎。","Kashmir Journal of Academic Research and Development","2026-10-04T00:00:00Z",66,{"impact":17,"substance":102,"depth":103,"authority":104,"freshness":105,"relevant":20,"comment":106},20,17,8,9,"综述性论文，系统整合灌溉与病害协同管理及数字农业证据，对小麦节水抗病生产有参考价值，但非原创数据、层级偏学术。",[108],{"name":98,"url":95},[110,25,111,112,27],"数字农业","小麦","病害防控",{"code":114,"slug":115,"name":111,"path":116,"parent_code":117},"A2","wheat","\u002Ftopic\u002Ffield-crops\u002Fwheat","A",[119],{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"status":37,"locked":14,"version":38,"method":39,"evidence":121},{"candidates":122,"agricultural_evidence":44},[123,128,132,137,144,148,155,162],{"code":114,"score":124,"supported":44,"title":125,"abstract":126,"tags":127},15,[115],[111,115],[111],{"code":30,"score":124,"supported":44,"title":129,"abstract":130,"tags":131},[31],[52,31],[27],{"code":43,"score":104,"supported":44,"title":133,"abstract":134,"tags":136},[],[110,135],"digital agriculture",[110,25],{"code":138,"score":58,"supported":44,"title":139,"abstract":140,"tags":143},"A7",[],[141,142],"轮作","crop rotation",[],{"code":57,"score":58,"supported":44,"title":145,"abstract":146,"tags":147},[],[61,62],[],{"code":149,"score":58,"supported":44,"title":150,"abstract":151,"tags":154},"H7",[],[152,153],"渍害","waterlogging",[],{"code":156,"score":157,"supported":44,"title":158,"abstract":159,"tags":161},"B7",3,[],[160],"greenhouse",[],{"code":163,"score":157,"supported":44,"title":164,"abstract":165,"tags":167},"I2",[],[166],"artificial intelligence",[],[169,170],"小麦 灌溉 病害 综合管理","小麦 节水灌溉 赤霉病","小麦灌溉病害综合管理-4242","10.71317\u002Fkjard.2.10.2026.774",{"doi":172,"openalex_id":174,"authors":175,"venue":98,"cited_by_count":79,"oa_url":95,"card":186,"direction":191,"ingested_from":88},"W7219840451",[176,178,180,182,184],{"name":177,"orcid":9},"Amir Khan Jamali",{"name":179,"orcid":9},"Zahir Muhammad Larik",{"name":181,"orcid":9},"Videsh Kumar",{"name":183,"orcid":9},"Um E Habiba",{"name":185,"orcid":9},"Amanullah Maree",{"tldr":187,"method":188,"finding":189,"direction":87,"opportunity":190},"综述小麦灌溉与病害管理整合策略，强调因地制宜而非通用方案。","文献综述，综合灌溉、病害、数字农业及巴基斯坦等发展经济体证据。","适度亏缺灌溉可提高水分生产力，但严重亏缺减产；病害管理需结合抗性、预测与适时用药。","AI、无人机和变量灌溉在小麦中的因果田间证据不足，可开展整合灌溉-病害的验证研究。","数字乡村与农业信息化","2026-10-05T23:30:30.962770Z",{"id":194,"title":195,"url":196,"summary":197,"summary_zh":198,"content":9,"source_name":199,"source_url":196,"published_at":200,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":201,"score_detail":202,"sources":206,"tags":208,"primary_topic":212,"secondary_topics":218,"classification":220,"search_phrases":258,"slug":261,"view_count":19,"doi":262,"paper":263,"created_at":275},2667,"A systematic review of remote sensing applications for agricultural water management in a water-stressed South Africa","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs43621-026-04728-y","Abstract Recent advancements in earth observation and computational technologies have enabled the prudent use of remote sensing (RS) for monitoring and managing water resources. RS is a spatially explicit and effective approach for field-level to large-scale monitoring through data-driven insights on key agricultural water management (AWM) aspects of water consumption, productivity, agricultural drought and irrigated area extent mapping. However, the progress made in the South African context is not well documented. Hence, this review aimed to assess how RS is utilized for AWM in South Africa. Article search was conducted in the Scopus and Web of Science databases using keywords remote sensing, earth observation, agricultural water management, irrigation, evapotranspiration, water use, water productivity, agricultural drought, and South Africa. The bibliometric analysis from 165 articles revealed that number of publications have a 8.3% annual growth over 26 years. RS data have been utilized to assess evapotranspiration (ET), crop water productivity (CWP), agricultural drought, scheme performance, and irrigation water quality. Data limitations, insufficient technical capacity, and limited computational infrastructure were the notable challenges. Future research should focus on integrated use of multi-temporal and multi-source data to minimize gaps across sensors and improve accuracy. Overall, RS has substantial potential to address water management issues in the agricultural sector of South Africa. Addressing the challenges through continued innovation, data integration, and capacity building is key to utilizing RS for sustainable agriculture and water security.","摘要 地球观测与计算技术的最新进展使得遥感（RS）能够被审慎地应用于水资源监测与管理。遥感是一种空间显式且有效的方法，可通过数据驱动的洞察，从田块尺度到大规模监测农业水资源管理（AWM）的关键方面，包括耗水量、生产力、农业干旱及灌溉面积范围制图。然而，南非在这一领域的进展尚未得到充分记录。因此，本综述旨在评估遥感在南非农业水资源管理中的应用情况。研究在Scopus和Web of Science数据库中使用遥感、地球观测、农业水资源管理、灌溉、蒸散发、用水量、水分生产力、农业干旱和南非等关键词进行文献检索。对165篇文章的文献计量分析显示，过去26年间发表数量年增长率为8.3%。遥感数据已被用于评估蒸散发（ET）、作物水分生产力（CWP）、农业干旱、灌区性能及灌溉水质。数据局限性、技术能力不足和计算基础设施有限是显著挑战。未来研究应侧重于多时相和多源数据的集成使用，以缩小传感器之间的差距并提高精度。总体而言，遥感在解决南非农业部门水资源管理问题方面具有巨大潜力。通过持续创新、数据集成和能力建设来应对这些挑战，是利用遥感实现可持续农业和水安全的关键。","Discover Sustainability","2026-09-15T00:00:00Z",74,{"impact":203,"substance":102,"depth":103,"authority":204,"freshness":104,"relevant":20,"comment":205},16,13,"以南非为对象的遥感农业水资源管理系统性综述，方法规范、数据规模明确，对干旱区灌溉与遥感应用有参考价值，但属区域案例研究，公共影响层级有限。",[207],{"name":199,"url":196},[25,209,210,27,211],"农业水资源","遥感","农业干旱",{"code":213,"slug":214,"name":215,"path":216,"parent_code":217},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",[219],{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"status":37,"locked":14,"version":38,"method":39,"evidence":221},{"candidates":222,"agricultural_evidence":44},[223,228,234,240,247,254],{"code":213,"score":124,"supported":44,"title":224,"abstract":226,"tags":227},[225],"remote sensing",[210,225],[210],{"code":30,"score":229,"supported":44,"title":230,"abstract":231,"tags":233},11,[],[52,53,232,31,54],"蒸散发",[209,27],{"code":149,"score":58,"supported":44,"title":235,"abstract":236,"tags":239},[],[237,238],"干旱","drought",[],{"code":241,"score":58,"supported":44,"title":242,"abstract":243,"tags":246},"H8",[],[244,245],"可持续农业","sustainable agriculture",[],{"code":248,"score":58,"supported":44,"title":249,"abstract":250,"tags":253},"I7",[],[251,252],"文献计量","bibliometric",[],{"code":43,"score":20,"supported":14,"title":255,"abstract":256,"tags":257},[],[],[25],[259,260],"农业水资源 农业干旱 智慧农业 灌溉管理","农业水资源 农业干旱","农业水资源农业干旱智慧农业灌溉管理-2667","10.1007\u002Fs43621-026-04728-y",{"doi":262,"openalex_id":264,"authors":265,"venue":199,"cited_by_count":79,"oa_url":196,"card":269,"direction":273,"ingested_from":88},"W7213266434",[266],{"name":267,"orcid":268},"Yilkal Gebeyehu Mekonnen","https:\u002F\u002Forcid.org\u002F0000-0001-9450-1081",{"tldr":270,"method":271,"finding":272,"direction":273,"opportunity":274},"系统综述遥感在南非农业水资源管理中的应用进展与挑战。","Scopus与Web of Science检索165篇文献，做文献计量分析。","遥感用于蒸散、水分生产力、干旱与灌溉监测，但受数据与能力限制。","农业遥感与作物表型","多源多时相遥感数据融合以填补传感器空缺、提升精度，是值得切入的方向。","2026-09-16T23:30:29.241938Z",{"id":277,"title":278,"url":279,"summary":280,"summary_zh":281,"content":9,"source_name":282,"source_url":279,"published_at":283,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":284,"score_detail":285,"sources":287,"tags":289,"primary_topic":292,"secondary_topics":293,"classification":294,"search_phrases":348,"slug":351,"view_count":19,"doi":352,"paper":353,"created_at":376},2167,"Crop Simulation Model: A Digital Tool for Sustainable Water Management in Agriculture","https:\u002F\u002Fdoi.org\u002F10.9734\u002Fijecc\u002F2026\u002Fv16i95667","Efficient management of agricultural water resources is increasingly important under climate variability and freshwater scarcity. Accurate estimation of crop water requirements (CWR) supports crop productivity, irrigation scheduling and water-use efficiency. Conventional approaches, including crop-coefficient, Penman–Monteith and soil-water-balance methods, remain widely used but can require extensive field observations and may have limited capacity to represent dynamic soil–crop–atmosphere interactions. Crop simulation models provide a process-based digital framework that integrates weather, soil, crop and management information to simulate crop growth, evapotranspiration, yield and water productivity under different environmental and management conditions. This review synthesises the roles of widely used models, including DSSAT, APSIM, AquaCrop, CROPWAT and CropSyst, in estimating crop water requirements and supporting irrigation management. Applications across maize, wheat, rice, cotton and other crops show that these models can evaluate irrigation schedules, deficit-irrigation strategies, water-saving practices and potential responses to future climate conditions. Model performance, however, depends on appropriate input data, local calibration and validation. The review also identifies opportunities to strengthen crop simulation through integration with hydrological information, remote sensing, artificial intelligence and modules for biotic stresses. Overall, crop simulation models provide useful decision-support tools for evaluating water-management options and improving the evidence base for sustainable agricultural water-resource planning.","在气候变率和淡水稀缺背景下，农业水资源的高效管理日益重要。准确估算作物需水量（CWR）有助于支撑作物生产力、灌溉调度和水分利用效率。传统方法，包括作物系数法、Penman–Monteith法和土壤水量平衡法，仍被广泛使用，但可能需要大量田间观测，且在表征动态的土壤–作物–大气相互作用方面能力有限。作物模拟模型提供了一种基于过程的数字化框架，可整合天气、土壤、作物和管理信息，以模拟不同环境和管理条件下的作物生长、蒸散发、产量和水分生产力。本文综述了广泛使用的模型，包括DSSAT、APSIM、AquaCrop、CROPWAT和CropSyst，在估算作物需水量和支持灌溉管理中的作用。在玉米、小麦、水稻、棉花及其他作物上的应用表明，这些模型能够评估灌溉制度、亏缺灌溉策略、节水措施以及对未来气候条件的潜在响应。然而，模型性能取决于适当的输入数据、本地校准和验证。本综述还指出了通过与水文信息、遥感、人工智能及生物胁迫模块集成来加强作物模拟的机遇。总体而言，作物模拟模型为评估水资源管理方案和改善可持续农业水资源规划的证据基础提供了有用的决策支持工具。","International Journal of Environment and Climate Change","2026-09-10T00:00:00Z",76,{"impact":18,"substance":102,"depth":103,"authority":204,"freshness":104,"relevant":20,"comment":286},"系统综述DSSAT、APSIM、AquaCrop等作物模拟模型在作物需水估算与灌溉决策中的应用，并提出与遥感、AI融合方向，对农业水资源数字化管理有参考价值。",[288],{"name":282,"url":279},[25,290,209,210,27,291],"农业人工智能","作物模拟模型",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[],{"status":37,"locked":14,"version":38,"method":39,"evidence":295},{"candidates":296,"agricultural_evidence":44},[297,301,306,313,317,324,331,335,340,344],{"code":30,"score":229,"supported":44,"title":298,"abstract":299,"tags":300},[],[52,53,232,31,54],[209,27],{"code":213,"score":302,"supported":44,"title":303,"abstract":304,"tags":305},7,[],[210,225],[210],{"code":307,"score":58,"supported":44,"title":308,"abstract":309,"tags":312},"A1",[],[310,311],"水稻","rice",[],{"code":114,"score":58,"supported":44,"title":314,"abstract":315,"tags":316},[],[111,115],[],{"code":318,"score":58,"supported":44,"title":319,"abstract":320,"tags":323},"A3",[],[321,322],"玉米","maize",[],{"code":325,"score":58,"supported":44,"title":326,"abstract":327,"tags":330},"A6",[],[328,329],"棉花","cotton",[],{"code":57,"score":58,"supported":44,"title":332,"abstract":333,"tags":334},[],[61,62],[],{"code":163,"score":336,"supported":44,"title":337,"abstract":338,"tags":339},4,[],[166],[290],{"code":241,"score":157,"supported":44,"title":341,"abstract":342,"tags":343},[],[244],[],{"code":43,"score":20,"supported":14,"title":345,"abstract":346,"tags":347},[],[],[25],[349,350],"作物模拟模型 农业人工智能 农业水资源 智慧农业","作物模拟模型 农业人工智能","作物模拟模型农业人工智能农业水资源智慧农业-2167","10.9734\u002Fijecc\u002F2026\u002Fv16i95667",{"doi":352,"openalex_id":354,"authors":355,"venue":282,"cited_by_count":79,"oa_url":279,"card":370,"direction":273,"ingested_from":88},"W7212194041",[356,358,360,362,364,366,368],{"name":357,"orcid":9},"P. S. Manju",{"name":359,"orcid":9},"N. Manikandan",{"name":361,"orcid":9},"A.P. Ramaraj",{"name":363,"orcid":9},"V. S. Jinsy",{"name":365,"orcid":9},"K. V. Sumesh",{"name":367,"orcid":9},"V. Dhanalakshmi",{"name":369,"orcid":9},"N. Gopika",{"tldr":371,"method":372,"finding":373,"direction":374,"opportunity":375},"综述作物模拟模型在估算作物需水与灌溉管理中的应用及改进方向。","综述DSSAT、APSIM、AquaCrop、CROPWAT、CropSyst等","模型可评估灌溉策略与节水实践，但精度依赖本地校准与输入数据。","农业人工智能与决策模型","可探索作物模型与遥感、AI及水文信息耦合，提升需水估算与灌溉决策精度。","2026-09-11T23:30:30.176174Z",{"id":378,"title":379,"url":380,"summary":381,"summary_zh":382,"content":9,"source_name":383,"source_url":380,"published_at":384,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":385,"score_detail":386,"sources":389,"tags":391,"primary_topic":395,"secondary_topics":396,"classification":401,"search_phrases":421,"slug":424,"view_count":79,"doi":425,"paper":426,"created_at":438},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":104,"substance":387,"depth":18,"authority":17,"freshness":105,"relevant":20,"comment":388},22,"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。",[390],{"name":383,"url":380},[25,290,392,393,394],"灌溉决策","安全保证","试验设计",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[397],{"code":163,"slug":398,"name":399,"path":400,"parent_code":217},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":37,"locked":14,"version":38,"method":39,"evidence":402},{"candidates":403,"agricultural_evidence":44},[404,408,413,417],{"code":30,"score":124,"supported":44,"title":405,"abstract":406,"tags":407},[31],[52,31],[392],{"code":163,"score":17,"supported":44,"title":409,"abstract":410,"tags":412},[166],[411],"decision support",[290],{"code":57,"score":58,"supported":44,"title":414,"abstract":415,"tags":416},[],[61,62],[],{"code":43,"score":20,"supported":14,"title":418,"abstract":419,"tags":420},[],[],[25],[422,423],"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":425,"openalex_id":427,"authors":428,"venue":383,"cited_by_count":79,"oa_url":380,"card":433,"direction":374,"ingested_from":88},"W7223131205",[429,431],{"name":430,"orcid":9},"Nassima Benkhelifa",{"name":432,"orcid":9},"Yacine Aït Ahmed",{"tldr":434,"method":435,"finding":436,"direction":374,"opportunity":437},"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。","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":440,"title":441,"url":442,"summary":443,"summary_zh":444,"content":9,"source_name":445,"source_url":442,"published_at":384,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":446,"score_detail":447,"sources":449,"tags":451,"primary_topic":454,"secondary_topics":455,"classification":462,"search_phrases":513,"slug":516,"view_count":79,"doi":517,"paper":518,"created_at":541},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":18,"substance":387,"depth":18,"authority":17,"freshness":105,"relevant":20,"comment":448},"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。",[450],{"name":445,"url":442},[25,290,210,452,453],"作物水分胁迫","精准灌溉",{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},[456,457],{"code":163,"slug":398,"name":399,"path":400,"parent_code":217},{"code":458,"slug":459,"name":460,"path":461,"parent_code":217},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":37,"locked":14,"version":38,"method":39,"evidence":463},{"candidates":464,"agricultural_evidence":44},[465,469,475,482,488,492,496,500,507],{"code":30,"score":124,"supported":44,"title":466,"abstract":467,"tags":468},[31],[52,31],[453],{"code":163,"score":13,"supported":44,"title":470,"abstract":471,"tags":474},[],[472,473,166],"机器学习","machine learning",[290],{"code":458,"score":105,"supported":44,"title":476,"abstract":477,"tags":481},[],[478,479,480],"物联网","internet of things","IoT",[],{"code":43,"score":302,"supported":44,"title":483,"abstract":484,"tags":487},[],[485,486],"数字孪生","digital twin",[25],{"code":213,"score":302,"supported":44,"title":489,"abstract":490,"tags":491},[],[210,225],[210],{"code":114,"score":58,"supported":44,"title":493,"abstract":494,"tags":495},[],[111,115],[],{"code":318,"score":58,"supported":44,"title":497,"abstract":498,"tags":499},[],[321,322],[],{"code":501,"score":58,"supported":44,"title":502,"abstract":503,"tags":506},"A5",[],[504,505],"马铃薯","potato",[],{"code":508,"score":157,"supported":44,"title":509,"abstract":510,"tags":512},"B2",[],[511],"葡萄",[],[514,515],"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":517,"openalex_id":519,"authors":520,"venue":445,"cited_by_count":79,"oa_url":442,"card":536,"direction":87,"ingested_from":88},"W7223088921",[521,524,527,530,533],{"name":522,"orcid":523},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":525,"orcid":526},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":528,"orcid":529},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":531,"orcid":532},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":534,"orcid":535},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":537,"method":538,"finding":539,"direction":273,"opportunity":540},"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。","2026-10-22T11:20:24.526683Z",{"id":543,"title":544,"url":545,"summary":546,"summary_zh":547,"content":9,"source_name":548,"source_url":545,"published_at":384,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":549,"score_detail":550,"sources":552,"tags":554,"primary_topic":557,"secondary_topics":558,"classification":561,"search_phrases":608,"slug":611,"view_count":79,"doi":612,"paper":613,"created_at":631},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":18,"substance":387,"depth":18,"authority":204,"freshness":105,"relevant":20,"comment":551},"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。",[553],{"name":548,"url":545},[25,290,478,555,556],"精准农业","气候韧性",{"code":163,"slug":398,"name":399,"path":400,"parent_code":217},[559,560],{"code":458,"slug":459,"name":460,"path":461,"parent_code":217},{"code":30,"slug":31,"name":32,"path":33,"parent_code":34},{"status":37,"locked":14,"version":38,"method":39,"evidence":562},{"candidates":563,"agricultural_evidence":44},[564,572,577,583,587,593,597,602],{"code":163,"score":18,"supported":44,"title":565,"abstract":566,"tags":571},[166],[567,472,568,473,569,570],"深度学习","计算机视觉","deep learning","computer vision",[290],{"code":43,"score":203,"supported":44,"title":573,"abstract":575,"tags":576},[574],"precision agriculture",[555,574],[25,555],{"code":458,"score":13,"supported":44,"title":578,"abstract":579,"tags":582},[],[478,580,480,581],"边缘计算","edge computing",[478],{"code":30,"score":105,"supported":44,"title":584,"abstract":585,"tags":586},[],[52,53,31],[],{"code":149,"score":302,"supported":44,"title":588,"abstract":589,"tags":592},[],[590,591],"气候变化","climate change",[556],{"code":57,"score":58,"supported":44,"title":594,"abstract":595,"tags":596},[],[61,62],[],{"code":138,"score":157,"supported":44,"title":598,"abstract":599,"tags":601},[],[600],"间作",[],{"code":603,"score":157,"supported":44,"title":604,"abstract":605,"tags":607},"F3",[],[606],"养分管理",[],[609,610],"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":612,"openalex_id":614,"authors":615,"venue":548,"cited_by_count":79,"oa_url":545,"card":626,"direction":87,"ingested_from":88},"W7223121456",[616,619,621,623],{"name":617,"orcid":618},"Olumayowa J. Iyanda","https:\u002F\u002Forcid.org\u002F0000-0002-5340-9884",{"name":620,"orcid":9},"Oluwadare OYEBODE",{"name":622,"orcid":9},"Iretiayo Adelaiye",{"name":624,"orcid":625},"Ayomide Afolabi","https:\u002F\u002Forcid.org\u002F0000-0002-5171-886X",{"tldr":627,"method":628,"finding":629,"direction":374,"opportunity":630},"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"]