[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1905":3,"related-1905":113},{"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":45,"search_phrases":88,"slug":91,"view_count":21,"doi":92,"paper":93,"created_at":112},1905,"A novel surface reflectance calibration approach to improve the estimation of maize actual evapotranspiration in a semi-arid USA region","https:\u002F\u002Fdoi.org\u002F10.1080\u002F01431161.2026.2727171","This study aimed to develop a novel surface reflectance (SR) data calibration approach to improve maize actual crop evapotranspiration (ETa, mm h−1) predictions in agricultural settings. The remote sensing (RS) platforms used in the study included Landsat-8 (30 m spatial resolution), Sentinel-2 (10 m), Planet CubeSat (3 m), a handheld radiometer (1 m), and a small uncrewed aerial system or sUAS (0.03 m). A one-source surface energy balance (OSEB) was applied to estimate ETa and assess any improvements when using original and calibrated SR data. Five machine-learning (ML) algorithms were evaluated as part of the calibration process. The ML approaches were linear regression, regression tree, random forest, support vector machine, and Gaussian process regression. The SR calibration approach incorporated a novel dual-source pixel decomposition model that accounts for the contributions of soil and vegetation light reflectance captured by a given SR band. Two maize research sites, one in Greeley and another in Fort Collins, Colorado, U.S.A. provided the grounds for data collection over a four-year period. Results indicated that the best ML model, for a given RS sensor, depended on the SR contributions from plants and bare soil. Among the evaluated algorithms, the Regression Tree and Gaussian Process Regression provided the most accurate pixel decomposition and reflectance adjustments, consistently demonstrating the highest statistical agreement (R2 = 0.70–0.94) and lowest residual errors (RMSE = 0.004–0.022). Improvements in maize hourly ETa had a 34% error reduction in maize ETa estimation for data from the handheld radiometer and sUAS, compared to a 12% error reduction for the spaceborne sensors, on average.","本研究旨在开发一种新型地表反射率（SR）数据校准方法，以提高农业环境中玉米实际作物蒸散量（ETa, mm h⁻¹）的预测精度。研究中使用的遥感（RS）平台包括Landsat-8（空间分辨率30米）、Sentinel-2（10米）、Planet CubeSat（3米）、手持式辐射计（1米）以及小型无人机系统（sUAS，0.03米）。采用单源地表能量平衡（OSEB）模型估算ETa，并评估使用原始与校准SR数据时的改进效果。校准过程中评估了五种机器学习（ML）算法，包括线性回归、回归树、随机森林、支持向量机和高斯过程回归。SR校准方法引入了一种新型双源像元分解模型，该模型考虑了特定SR波段所捕获的土壤与植被光反射的贡献。研究选取了美国科罗拉多州格里利和柯林斯堡的两个玉米研究站点，在四年期间收集数据。结果表明，对于给定的遥感传感器，最佳ML模型取决于植被和裸土的SR贡献。在所评估的算法中，回归树和高斯过程回归提供了最精确的像元分解和反射率调整，始终表现出最高的一致性统计指标（R² = 0.70–0.94）和最低的残差误差（RMSE = 0.004–0.022）。在玉米小时ETa估算改进方面，手持式辐射计和sUAS数据的ETa估算误差平均降低了34%，而星载传感器的误差平均降低了12%。",null,"International Journal of Remote Sensing","2026-09-06T00:00:00Z","论文",10,false,72,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,22,18,14,3,1,"研究提出地表反射率校准新方法，结合多源遥感与机器学习提升玉米蒸散估算精度，对农业水资源管理有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","机器学习","遥感","作物模型","精准灌溉",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"A3","maize","玉米","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[39],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":46,"locked":14,"version":47,"method":48,"evidence":49},"classified","2026-10-22.2","rules",{"candidates":50,"agricultural_evidence":52},[51,56,62,69,77,83],{"code":33,"score":20,"supported":52,"title":53,"abstract":54,"tags":55},true,[34],[35,34],[],{"code":40,"score":57,"supported":52,"title":58,"abstract":60,"tags":61},12,[59],"evapotranspiration",[59],[31],{"code":63,"score":64,"supported":52,"title":65,"abstract":66,"tags":68},"I3",7,[],[29,67],"remote sensing",[29],{"code":70,"score":71,"supported":52,"title":72,"abstract":73,"tags":76},"H2",6,[],[74,75],"土壤","soil",[],{"code":78,"score":79,"supported":52,"title":80,"abstract":81,"tags":82},"I2",5,[],[28],[30,28],{"code":84,"score":22,"supported":14,"title":85,"abstract":86,"tags":87},"I1",[],[],[27],[89,90],"作物模型 智慧农业 机器学习 精准灌溉","作物模型 智慧农业","作物模型智慧农业机器学习精准灌溉-1905","10.1080\u002F01431161.2026.2727171",{"doi":92,"openalex_id":94,"authors":95,"venue":10,"cited_by_count":104,"oa_url":9,"card":105,"direction":109,"ingested_from":111},"W7210284621",[96,99,102],{"name":97,"orcid":98},"Edson Costa‐Filho","https:\u002F\u002Forcid.org\u002F0000-0001-5610-7382",{"name":100,"orcid":101},"José L. Chávez","https:\u002F\u002Forcid.org\u002F0000-0001-6456-0822",{"name":103,"orcid":9},"Huihui Zhang",0,{"tldr":106,"method":107,"finding":108,"direction":109,"opportunity":110},"提出地表反射率校准方法，结合机器学习与双源像素分解，提高半干旱区玉米蒸散发估算精度。","多源遥感数据，OSEB模型，五种机器学习算法，双源像素分解模型。","回归树和高斯过程回归校准效果最佳，误差降低12%-34%，高分辨率传感器提升更显著。","农业遥感与作物表型","可探索将校准方法应用于其他作物或区域，或结合深度学习提升低分辨率卫星数据的校准精度。","openalex","2026-09-08T23:30:20.154043Z",{"total":71,"page":22,"page_size":71,"items":114},[115,192,269,382,436,515],{"id":116,"title":117,"url":118,"summary":119,"summary_zh":9,"content":9,"source_name":120,"source_url":118,"published_at":121,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":122,"score_detail":123,"sources":127,"tags":129,"primary_topic":133,"secondary_topics":134,"classification":136,"search_phrases":168,"slug":171,"view_count":150,"doi":172,"paper":173,"created_at":191},2291,"Optimal management practices of nitrogen and irrigation for the trade-off between economy and environment for maize in China","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104970","Optimal management practices of nitrogen and irrigation for the trade-off between economy and environment for maize in China。Agricultural Systems","Agricultural Systems","2026-09-12T00:00:00Z",78,{"impact":19,"substance":124,"depth":19,"authority":20,"freshness":125,"relevant":22,"comment":126},20,8,"核心期刊论文，提出中国玉米氮肥与灌溉协同优化管理策略，兼顾经济与环境权衡，对农业绿色生产与精准水肥管理有参考价值。",[128],{"name":120,"url":118},[27,35,130,131,132],"农业绿色发展","水肥一体化","氮肥管理",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[135],{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":137},{"candidates":138,"agricultural_evidence":52},[139,143,148,154,159,164],{"code":33,"score":57,"supported":52,"title":140,"abstract":141,"tags":142},[34],[34],[35],{"code":40,"score":144,"supported":52,"title":145,"abstract":146,"tags":147},11,[41],[41],[],{"code":149,"score":150,"supported":14,"title":151,"abstract":152,"tags":153},"F3",2,[],[],[132,131],{"code":155,"score":22,"supported":14,"title":156,"abstract":157,"tags":158},"H4",[],[],[130],{"code":160,"score":22,"supported":14,"title":161,"abstract":162,"tags":163},"H8",[],[],[130],{"code":84,"score":22,"supported":14,"title":165,"abstract":166,"tags":167},[],[],[27],[169,170],"农业绿色发展 水肥一体化 智慧农业 氮肥管理","农业绿色发展 水肥一体化","农业绿色发展水肥一体化智慧农业氮肥管理-2291","10.1016\u002Fj.agsy.2026.104970",{"doi":172,"openalex_id":174,"authors":175,"venue":120,"cited_by_count":104,"oa_url":118,"card":9,"direction":9,"ingested_from":111},"W7212404025",[176,179,181,183,185,187,189],{"name":177,"orcid":178},"Yu Ye","https:\u002F\u002Forcid.org\u002F0000-0002-6744-3338",{"name":180,"orcid":9},"Xingrui Liu",{"name":182,"orcid":9},"Yuanyuan Chen",{"name":184,"orcid":9},"Shuzhu Shi",{"name":186,"orcid":9},"Zhaocong Wu",{"name":188,"orcid":9},"Kaiping Zhang",{"name":190,"orcid":9},"Rui Xiao","2026-09-13T23:30:05.153684Z",{"id":193,"title":194,"url":195,"summary":196,"summary_zh":197,"content":9,"source_name":120,"source_url":195,"published_at":198,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":199,"score_detail":200,"sources":202,"tags":204,"primary_topic":208,"secondary_topics":209,"classification":214,"search_phrases":238,"slug":241,"view_count":21,"doi":242,"paper":243,"created_at":268},1383,"Pareto-front-based optimization of irrigation and nitrogen application schedule in Sandy-soil maize: Trade-offs among yield, water productivity, nitrogen physiological efficiency, and grain nitrogen concentration","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104953","Context Developing appropriate irrigation and nitrogen application schedules (INASs) is essential for maize production on sandy soils, where low water-retention and nutrient-holding capacities make crop performance highly sensitive to within-season water and nitrogen management. However, most crop-model-based optimization studies have focused mainly on yield and resource-use efficiency, while grain nutritional quality and the economic qualifications of optimized schedules have received less attention. Objective This study aimed to develop a CERES-Maize–NSGA-III framework to optimize INASs for shallow-buried drip-irrigated maize on sandy soil, while clarifying trade-offs among yield, crop water productivity (WP c ), nitrogen physiological efficiency (PE N ), and grain nitrogen concentration (GNC), and evaluate the economic performance of selected candidate schedules through partial-budget analysis. Methods CERES-Maize was calibrated and validated using two years of field observations and then coupled with NSGA-III to optimize irrigation timing, irrigation amount, nitrogen application timing, and nitrogen rate. Pareto-optimal schedules were further evaluated through candidate-schedule selection, weather-year stress testing, and partial-budget economic sensitivity analysis. Results and conclusions CERES-Maize reproduced maize growth, yield, seasonal water use, and total nitrogen uptake with acceptable accuracy. The Pareto front revealed clear trade-offs among yield, WP c , PE N , and GNC. The 16 high-yield (HY) candidates selected using the historical farmer-yield benchmark exceeded the corresponding weather-matched farmer yield in all five annual simulations and maintained positive ΔPNR across all tested price–cost combinations. Among these candidates, HY01 showed the strongest combined yield and partial-budget performance, with the highest five-year mean simulated yield (12,003.2 kg\u002Fha) and the highest mean ΔPNR relative to the corresponding farmer-managed schedules (1307.83 yuan\u002Fha) across the tested weather-year and price–cost combinations. In contrast, the distance-to-utopia compromise schedule produced lower yield than the corresponding farmer-managed schedule in each weather year and showed weather- and price–cost-dependent economic performance. Significance The proposed framework provides a biophysical optimization tool for generating candidate INASs and clarifying trade-offs among yield, WP c , PE N , and GNC in sandy-soil maize. Weather variability was evaluated only through ex-post simulations of selected candidates and was not incorporated directly into the optimization. Further field-level, operational, economic, and environmental validation is therefore required before practical implementation.","背景 制定合理的灌溉与施氮方案（INASs）对砂质土壤上的玉米生产至关重要，因为砂质土壤的保水性和保肥能力较低，作物表现对季内水分和氮素管理高度敏感。然而，大多数基于作物模型的优化研究主要关注产量和资源利用效率，而籽粒营养品质及优化方案的经济可行性受到的关注较少。目的 本研究旨在构建CERES-Maize–NSGA-III耦合框架，优化砂质土壤浅埋滴灌玉米的灌溉与施氮方案，明确产量、作物水分生产力（WPc）、氮素生理效率（PEN）和籽粒氮浓度（GNC）之间的权衡关系，并通过部分预算分析评估所选候选方案的经济表现。方法 利用两年田间观测数据对CERES-Maize模型进行校准和验证，随后与NSGA-III算法耦合，优化灌溉时间、灌溉量、施氮时间和施氮量。对Pareto最优方案进一步开展候选方案筛选、天气年胁迫测试和部分预算经济敏感性分析。结果与结论 CERES-Maize模型能够以可接受的精度再现玉米生长、产量、季节性水分利用和总氮吸收量。Pareto前沿揭示了产量、WPc、PEN和GNC之间明显的权衡关系。以历史农户产量基准筛选出的16个高产（HY）候选方案在全部五个模拟年份中均超过了相应天气匹配的农户产量，并在所有测试的价格—成本组合下保持正的ΔPNR。在这些候选方案中，HY01表现出最强的产量和部分预算综合表现，其五年平均模拟产量最高（12,003.2 kg\u002Fha），且在测试的天气年和价格—成本组合下，相对于相应农户管理方案的平均ΔPNR最高（1,307.83元\u002Fha）。相比之下，距离理想点折中方案在每个天气年的产量均低于相应的农户管理方案，且其经济表现依赖于天气和价格—成本条件。意义 所提出的框架为砂质土壤玉米生成候选灌溉施氮方案并阐明产量、WPc、PEN和GNC之间的权衡关系提供了生物物理优化工具。天气变异性","2026-09-02T00:00:00Z",80,{"impact":19,"substance":18,"depth":19,"authority":20,"freshness":125,"relevant":22,"comment":201},"研究提出CERES-Maize-NSGA-III框架优化沙土玉米水氮管理，兼顾产量、水氮效率与品质，方法新颖，结论可靠，对精准农业有参考价值。",[203],{"name":120,"url":195},[27,35,205,206,207],"水肥优化","多目标优化","模型模拟",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[210,213],{"code":70,"slug":75,"name":211,"path":212,"parent_code":44},"土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":215},{"candidates":216,"agricultural_evidence":52},[217,221,225,230,234],{"code":33,"score":17,"supported":52,"title":218,"abstract":219,"tags":220},[34],[35,34],[35],{"code":70,"score":20,"supported":52,"title":222,"abstract":223,"tags":224},[75],[74,75],[],{"code":40,"score":20,"supported":52,"title":226,"abstract":227,"tags":229},[41],[228,41],"灌溉",[],{"code":84,"score":22,"supported":14,"title":231,"abstract":232,"tags":233},[],[],[27],{"code":78,"score":22,"supported":14,"title":235,"abstract":236,"tags":237},[],[],[207],[239,240],"多目标优化 智慧农业 模型模拟 水肥优化","多目标优化 智慧农业","多目标优化智慧农业模型模拟水肥优化-1383","10.1016\u002Fj.agsy.2026.104953",{"doi":242,"openalex_id":244,"authors":245,"venue":120,"cited_by_count":104,"oa_url":195,"card":262,"direction":266,"ingested_from":111},"W7205003045",[246,248,251,254,256,257,259,261],{"name":247,"orcid":9},"Yongqiang Wang",{"name":249,"orcid":250},"Fugui Wang","https:\u002F\u002Forcid.org\u002F0000-0002-8349-8398",{"name":252,"orcid":253},"Lanfang Bai","https:\u002F\u002Forcid.org\u002F0000-0001-7862-2844",{"name":255,"orcid":9},"Zhen Wang",{"name":255,"orcid":9},{"name":258,"orcid":9},"Haofang Yan",{"name":260,"orcid":9},"Zhigang Wang",{"name":260,"orcid":9},{"tldr":263,"method":264,"finding":265,"direction":266,"opportunity":267},"构建CERES-Maize-NSGA-III框架，优化沙土玉米灌溉施氮方案，权衡产量、水生产力、氮效","CERES-Maize模型与NSGA-III多目标优化，结合田间数据校准验证，并","高产候选方案在产量和经济效益上优于农民方案，但折中方案表现不稳定。","农业人工智能与决策模型","可探索将多目标优化与实时气象预测结合，开发动态灌溉施氮决策支持系统，兼顾产量、品质与经济效益。","2026-09-02T23:30:05.454493Z",{"id":270,"title":271,"url":272,"summary":273,"summary_zh":274,"content":9,"source_name":275,"source_url":272,"published_at":276,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":277,"score_detail":278,"sources":281,"tags":283,"primary_topic":286,"secondary_topics":287,"classification":298,"search_phrases":352,"slug":355,"view_count":104,"doi":356,"paper":357,"created_at":381},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","2026-10-21T00:00:00Z",79,{"impact":19,"substance":18,"depth":19,"authority":57,"freshness":279,"relevant":22,"comment":280},9,"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。",[282],{"name":275,"url":272},[27,284,29,285,31],"农业人工智能","作物水分胁迫",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},[288,293],{"code":78,"slug":289,"name":290,"path":291,"parent_code":292},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"code":294,"slug":295,"name":296,"path":297,"parent_code":292},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":46,"locked":14,"version":47,"method":48,"evidence":299},{"candidates":300,"agricultural_evidence":52},[301,305,311,318,324,328,335,339,346],{"code":40,"score":17,"supported":52,"title":302,"abstract":303,"tags":304},[41],[228,41],[31],{"code":78,"score":13,"supported":52,"title":306,"abstract":307,"tags":310},[],[28,308,309],"machine learning","artificial intelligence",[284],{"code":294,"score":279,"supported":52,"title":312,"abstract":313,"tags":317},[],[314,315,316],"物联网","internet of things","IoT",[],{"code":84,"score":64,"supported":52,"title":319,"abstract":320,"tags":323},[],[321,322],"数字孪生","digital twin",[27],{"code":63,"score":64,"supported":52,"title":325,"abstract":326,"tags":327},[],[29,67],[29],{"code":329,"score":71,"supported":52,"title":330,"abstract":331,"tags":334},"A2",[],[332,333],"小麦","wheat",[],{"code":33,"score":71,"supported":52,"title":336,"abstract":337,"tags":338},[],[35,34],[],{"code":340,"score":71,"supported":52,"title":341,"abstract":342,"tags":345},"A5",[],[343,344],"马铃薯","potato",[],{"code":347,"score":21,"supported":52,"title":348,"abstract":349,"tags":351},"B2",[],[350],"葡萄",[],[353,354],"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":356,"openalex_id":358,"authors":359,"venue":275,"cited_by_count":104,"oa_url":272,"card":375,"direction":380,"ingested_from":111},"W7223088921",[360,363,366,369,372],{"name":361,"orcid":362},"R M Jeya","https:\u002F\u002Forcid.org\u002F0009-0001-3621-8533",{"name":364,"orcid":365},"P. C. Prabu","https:\u002F\u002Forcid.org\u002F0000-0002-7237-9481",{"name":367,"orcid":368},"K P Ragunath","https:\u002F\u002Forcid.org\u002F0000-0002-7851-4437",{"name":370,"orcid":371},"P. Kannan","https:\u002F\u002Forcid.org\u002F0000-0001-7003-3535",{"name":373,"orcid":374},"P. M. Shanmugam","https:\u002F\u002Forcid.org\u002F0000-0002-6706-8419",{"tldr":376,"method":377,"finding":378,"direction":109,"opportunity":379},"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":383,"title":384,"url":385,"summary":386,"summary_zh":9,"content":9,"source_name":387,"source_url":9,"published_at":9,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":388,"score_detail":389,"sources":392,"tags":394,"primary_topic":397,"secondary_topics":398,"classification":399,"search_phrases":423,"slug":426,"view_count":150,"doi":9,"paper":427,"created_at":435},4477,"玉米生产精准农业系统综述：技术、应用与产量优化（2020—2026）","https:\u002F\u002Fm2.mtmt.hu\u002Fapi\u002Fpublication\u002F37500368","依据PRISMA 2020指南，系统检索Scopus与Web of Science共464篇文献，最终纳入129项研究；样本构成为田间试验（28.6%）、遥感与精准农业整合研究（25.5%）、机器学习应用（19.4%）、气候知情精准农业策略（23.4%）和土壤退化研究（3.1%）；研究表明精准农业技术通过定点管理和数据驱动决策支持，提升了玉米生产力、养分利用效率、水利用效率和产量预测准确性；指出采用精准农业仍受制于实施成本高、技术复杂、数据有限等因素。","Precision Agriculture in Maize Production Systematic Review 2026",74,{"impact":19,"substance":18,"depth":19,"authority":390,"freshness":21,"relevant":22,"comment":391},13,"基于PRISMA 2020的系统综述，纳入129项研究，数据规模与方法规范性强，对玉米精准农业技术路径与瓶颈有实质参考价值，但发布日期不明、时效性偏弱。",[393],{"name":387,"url":385},[27,395,28,35,396,29],"产量预测","精准农业",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":46,"locked":14,"version":47,"method":48,"evidence":400},{"candidates":401,"agricultural_evidence":52},[402,406,410,414,419],{"code":84,"score":390,"supported":52,"title":403,"abstract":404,"tags":405},[396],[396],[27,396],{"code":33,"score":57,"supported":52,"title":407,"abstract":408,"tags":409},[35],[35],[35],{"code":63,"score":79,"supported":52,"title":411,"abstract":412,"tags":413},[],[29],[395,29],{"code":78,"score":415,"supported":52,"title":416,"abstract":417,"tags":418},4,[],[28],[28],{"code":70,"score":21,"supported":52,"title":420,"abstract":421,"tags":422},[],[74],[],[424,425],"玉米 精准农业 系统综述","玉米 遥感 机器学习 产量","玉米精准农业系统综述-4477",{"doi":9,"openalex_id":9,"authors":428,"venue":9,"cited_by_count":104,"oa_url":9,"card":429,"direction":380,"ingested_from":434},[],{"tldr":430,"method":431,"finding":432,"direction":380,"opportunity":433},"系统综述2020—2026年玉米精准农业技术、应用与产量优化研究。","按PRISMA 2020检索Scopus与WoS，纳入129项研究做分类分析。","精准农业通过定点管理与数据决策提升玉米产量、养分和水利用效率。","成本高、技术复杂与数据不足制约推广，低成本轻量化方案与本地化数据共享是研究空白。","agent","2026-10-09T00:05:02.451957Z",{"id":437,"title":438,"url":439,"summary":440,"summary_zh":441,"content":9,"source_name":442,"source_url":439,"published_at":443,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":444,"score_detail":445,"sources":447,"tags":449,"primary_topic":452,"secondary_topics":457,"classification":464,"search_phrases":492,"slug":495,"view_count":415,"doi":496,"paper":497,"created_at":514},3607,"Analysis of drought carry-over effect on apple trees using the Leafiness-LiDAR index","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.biosystemseng.2026.104603","Apple trees ( Malus × domestica Borkh.) are widely grown in Mediterranean regions, where drought-induced irrigation restrictions can impair canopy development and productivity. This study assessed the Leafiness-LiDAR Index (LLI), derived from terrestrial Light Detection and Ranging (LiDAR) point clouds, as a proxy for Leaf Area Index (LAI) and as a non-destructive indicator of canopy response and recovery following water deficit. An ‘Opal®’ apple orchard was monitored over three growing seasons (2023–2025), encompassing one drought year and two recovery years. In 2023, full irrigation (FI; 480 mm) was compared with deficit irrigation (DI; 300 mm; 37.5% less water) under two planting densities (0.5 and 1.0 m tree spacing). Mixed-effects models revealed significant Year × Irrigation interactions for LAI (p = 0.006) and LLI (p \u003C 0.001), but not for yield (p = 0.654), suggesting a partial decoupling between structural and productive recovery. During the drought year, DI reduced LLI by 37% and yield by 47%. Following irrigation restoration, LLI differences persisted, particularly in 2024, indicating lasting structural effects of water deficit. Higher planting density promoted canopy recovery and increased yield. LLI proved to be a valuable, non-destructive estimator of LAI for detecting drought legacy effects and tracking canopy recovery, supporting precision orchard management under Mediterranean water scarcity.","苹果树（Malus × domestica Borkh.）广泛种植于地中海地区，该地区因干旱导致的灌溉限制会损害树冠发育和生产力。本研究评估了基于地面激光雷达（LiDAR）点云衍生的叶量-激光雷达指数（Leafiness-LiDAR Index, LLI）作为叶面积指数（Leaf Area Index, LAI）的替代指标，以及作为水分亏缺后树冠响应与恢复的非破坏性指标的可行性。对一个‘Opal®’苹果园进行了三个生长季（2023—2025年）的监测，涵盖一个干旱年和两个恢复年。2023年，在两种种植密度（株距0.5 m和1.0 m）下，比较了充分灌溉（FI；480 mm）与亏缺灌溉（DI；300 mm；减少37.5%水量）。混合效应模型显示，LAI（p = 0.006）和LLI（p \u003C 0.001）存在显著的年×灌溉交互作用，而产量（p = 0.654）则无此交互作用，表明结构与产量恢复之间存在部分脱耦。在干旱年，DI使LLI降低37%，产量降低47%。灌溉恢复后，LLI差异持续存在，尤其在2024年，表明水分亏缺具有持久的结构性影响。较高的种植密度促进了树冠恢复并提高了产量。LLI被证明是一种有价值的、非破坏性的LAI估算指标，可用于检测干旱遗留效应并追踪树冠恢复，为地中海水资源短缺条件下的精准果园管理提供支持。","Biosystems Engineering","2026-09-26T00:00:00Z",77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":125,"relevant":22,"comment":446},"地中海苹果园三年试验证实LiDAR叶量指数可无损监测干旱遗留效应与冠层恢复，方法新颖、数据扎实，对果园精准灌溉有参考价值。",[448],{"name":442,"url":439},[27,450,29,451,31],"苹果","果园管理",{"code":347,"slug":453,"name":454,"path":455,"parent_code":456},"fruit","果树","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",[458,463],{"code":459,"slug":460,"name":461,"path":462,"parent_code":44},"H7","climate","气候适应与防灾减灾","\u002Ftopic\u002Fenvironment\u002Fclimate",{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":465},{"candidates":466,"agricultural_evidence":52},[467,473,479,484,488],{"code":347,"score":19,"supported":52,"title":468,"abstract":470,"tags":472},[469],"apple",[454,450,471,469],"orchard",[450],{"code":459,"score":20,"supported":52,"title":474,"abstract":476,"tags":478},[475],"drought",[477,475],"干旱",[],{"code":40,"score":13,"supported":52,"title":480,"abstract":481,"tags":483},[],[228,482,41],"水资源",[31],{"code":84,"score":22,"supported":14,"title":485,"abstract":486,"tags":487},[],[],[27],{"code":63,"score":22,"supported":14,"title":489,"abstract":490,"tags":491},[],[],[29],[493,494],"LiDAR 苹果园 干旱","Leafiness-LiDAR Index 苹果","LiDAR苹果园干旱-3607","10.1016\u002Fj.biosystemseng.2026.104603",{"doi":496,"openalex_id":498,"authors":499,"venue":442,"cited_by_count":104,"oa_url":439,"card":509,"direction":109,"ingested_from":111},"W7214458453",[500,503,506],{"name":501,"orcid":502},"Leire Sandonís-Pozo","https:\u002F\u002Forcid.org\u002F0000-0003-2472-0259",{"name":504,"orcid":505},"José Antonio Martínez-Casasnovas","https:\u002F\u002Forcid.org\u002F0000-0003-1480-3632",{"name":507,"orcid":508},"Miquel Pascual","https:\u002F\u002Forcid.org\u002F0000-0002-3329-4207",{"tldr":510,"method":511,"finding":512,"direction":109,"opportunity":513},"用LiDAR叶量指数追踪苹果园干旱遗留效应与冠层恢复。","三年果园试验，地面LiDAR点云提取LLI，混合效应模型分析。","亏缺灌溉使LLI降37%、产量降47%，复水后结构差异仍持续。","可探索LLI与多源遥感融合，构建果园干旱遗留效应早期预警与精准补灌决策模型。","2026-09-27T23:30:09.907348Z",{"id":516,"title":517,"url":518,"summary":519,"summary_zh":520,"content":9,"source_name":521,"source_url":518,"published_at":522,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":523,"sources":527,"tags":529,"primary_topic":531,"secondary_topics":532,"classification":535,"search_phrases":557,"slug":560,"view_count":22,"doi":561,"paper":562,"created_at":580},2543,"Effects of Noise on the Accuracy of Estimating Rootzone Total Soil Moisture Using a Non‐Linear Autoregressive Exogenous Machine Learning Model for Precision Agriculture","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fird.70226","ABSTRACT Widely used evapotranspiration (ET)–based irrigation methods contain many uncertainties that can affect irrigation quantity. To reduce these uncertainties, an alternative approach is the use of soil moisture (SM) data to estimate plant water uptake (PWU). If rootzone SM dynamics are understood, a surrogate machine learning (ML) model to predict the behaviour of SM dynamics can be developed to estimate PWU. Given the time and external variable dependency of SM, the non‐linear autoregressive exogenous (NARX) algorithm can be a better ML model for this purpose. However, the effect of measurement errors can affect prediction quality and hence the full deployment of data collection technology and computational algorithms. This paper presents a methodology consisting of analysing real‐world data, developing a generalized hypothetical SM curve, simulating measurement errors to develop noisy datasets and developing an ML model. The results show that the prediction accuracy is inversely proportional to the noise level. Up to a 10% measurement error, the NARX model can capture the SM dynamics with relatively high accuracy. However, the prediction quality decreases significantly as the noise level increases to 20%. For noise levels within 5%, the model prediction accuracy is significantly high, with correlation coefficients higher than 0.90 for all sets.","摘要 广泛使用的基于蒸散发（ET）的灌溉方法存在许多不确定性，可能影响灌溉量。为减少这些不确定性，一种替代方法是利用土壤水分（SM）数据估算植物吸水量（PWU）。如果理解了根区土壤水分动态，就可以开发一个替代性机器学习（ML）模型来预测土壤水分动态行为，从而估算植物吸水量。鉴于土壤水分对时间和外部变量的依赖性，非线性自回归外生（NARX）算法可能是更适合此目的的机器学习模型。然而，测量误差的影响可能影响预测质量，进而影响数据采集技术和计算算法的全面部署。本文提出了一套方法，包括分析真实世界数据、建立广义假设土壤水分曲线、模拟测量误差以生成含噪数据集，以及开发机器学习模型。结果表明，预测精度与噪声水平成反比。在测量误差不超过10%时，NARX模型能够以较高精度捕捉土壤水分动态。然而，当噪声水平增至20%时，预测质量显著下降。对于5%以内的噪声水平，模型预测精度显著较高，所有数据集的决定系数均高于0.90。","Irrigation and Drainage","2026-09-14T00:00:00Z",{"impact":390,"substance":524,"depth":525,"authority":390,"freshness":125,"relevant":22,"comment":526},21,17,"核心期刊论文，量化了测量噪声对NARX土壤水分预测精度的影响，为精准灌溉传感器布设与数据质量控制提供参考，但属细分方法研究，公共影响有限。",[528],{"name":521,"url":518},[27,284,28,31,530],"土壤墒情",{"code":78,"slug":289,"name":290,"path":291,"parent_code":292},[533,534],{"code":70,"slug":75,"name":211,"path":212,"parent_code":44},{"code":40,"slug":41,"name":42,"path":43,"parent_code":44},{"status":46,"locked":14,"version":47,"method":48,"evidence":536},{"candidates":537,"agricultural_evidence":52},[538,543,547,552],{"code":78,"score":539,"supported":52,"title":540,"abstract":541,"tags":542},16,[308],[28,308],[284,28],{"code":70,"score":17,"supported":52,"title":544,"abstract":545,"tags":546},[75],[74,75],[530],{"code":40,"score":13,"supported":52,"title":548,"abstract":549,"tags":551},[],[228,550,41,59],"蒸散发",[31],{"code":84,"score":279,"supported":52,"title":553,"abstract":555,"tags":556},[554],"precision agriculture",[],[27],[558,559],"农业人工智能 土壤墒情 智慧农业 机器学习","农业人工智能 土壤墒情","农业人工智能土壤墒情智慧农业机器学习-2543","10.1002\u002Fird.70226",{"doi":561,"openalex_id":563,"authors":564,"venue":521,"cited_by_count":104,"oa_url":9,"card":575,"direction":266,"ingested_from":111},"W7213181547",[565,568,571,573],{"name":566,"orcid":567},"Fayzul Pasha","https:\u002F\u002Forcid.org\u002F0000-0002-8295-0602",{"name":569,"orcid":570},"Ashok Inturi","https:\u002F\u002Forcid.org\u002F0009-0004-1934-4310",{"name":572,"orcid":9},"Kinnoree R. Pasha",{"name":574,"orcid":9},"Dilruba Yeasmin",{"tldr":576,"method":577,"finding":578,"direction":266,"opportunity":579},"该论文评估了测量噪声对NARX机器学习模型估算根区土壤水分动态精度的影响。","使用NARX模型，基于真实数据、广义假设曲线和模拟噪声数据集。","预测精度与噪声水平成反比；噪声≤10%时精度较高，≤5%时相关系数>0.90，20%时显著下降。","可研究自适应去噪或鲁棒NARX模型，以在20%以上噪声下维持土壤水分预测精度。","2026-09-15T23:30:38.023164Z"]