[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2291":3,"related-2291":106},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":6,"published_at":10,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":14,"score_detail":15,"sources":22,"tags":24,"primary_topic":30,"secondary_topics":35,"classification":42,"search_phrases":80,"slug":83,"view_count":61,"doi":84,"paper":85,"created_at":105},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",null,"Agricultural Systems","2026-09-12T00:00:00Z","论文",10,false,78,{"impact":16,"substance":17,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":21},18,20,14,8,1,"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。",[23],{"name":9,"url":6},[25,26,27,28,29],"智慧农业","玉米","农业绿色发展","水肥一体化","氮肥管理",{"code":31,"slug":32,"name":26,"path":33,"parent_code":34},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[36],{"code":37,"slug":38,"name":39,"path":40,"parent_code":41},"H3","irrigation","水资源与灌溉","\u002Ftopic\u002Fenvironment\u002Firrigation","H",{"status":43,"locked":13,"version":44,"method":45,"evidence":46},"classified","2026-10-22.2","rules",{"candidates":47,"agricultural_evidence":50},[48,54,59,65,70,75],{"code":31,"score":49,"supported":50,"title":51,"abstract":52,"tags":53},12,true,[32],[32],[26],{"code":37,"score":55,"supported":50,"title":56,"abstract":57,"tags":58},11,[38],[38],[],{"code":60,"score":61,"supported":13,"title":62,"abstract":63,"tags":64},"F3",2,[],[],[29,28],{"code":66,"score":20,"supported":13,"title":67,"abstract":68,"tags":69},"H4",[],[],[27],{"code":71,"score":20,"supported":13,"title":72,"abstract":73,"tags":74},"H8",[],[],[27],{"code":76,"score":20,"supported":13,"title":77,"abstract":78,"tags":79},"I1",[],[],[25],[81,82],"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":84,"openalex_id":86,"authors":87,"venue":9,"cited_by_count":103,"oa_url":6,"card":8,"direction":8,"ingested_from":104},"W7212404025",[88,91,93,95,97,99,101],{"name":89,"orcid":90},"Yu Ye","https:\u002F\u002Forcid.org\u002F0000-0002-6744-3338",{"name":92,"orcid":8},"Xingrui Liu",{"name":94,"orcid":8},"Yuanyuan Chen",{"name":96,"orcid":8},"Shuzhu Shi",{"name":98,"orcid":8},"Zhaocong Wu",{"name":100,"orcid":8},"Kaiping Zhang",{"name":102,"orcid":8},"Rui Xiao",0,"openalex","2026-09-13T23:30:05.153684Z",{"total":107,"page":20,"page_size":107,"items":108},6,[109,193,269,350,398,478],{"id":110,"title":111,"url":112,"summary":113,"summary_zh":114,"content":8,"source_name":9,"source_url":112,"published_at":115,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":116,"score_detail":117,"sources":120,"tags":122,"primary_topic":126,"secondary_topics":127,"classification":134,"search_phrases":161,"slug":164,"view_count":165,"doi":166,"paper":167,"created_at":192},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":16,"substance":118,"depth":16,"authority":18,"freshness":19,"relevant":20,"comment":119},22,"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。",[121],{"name":9,"url":112},[25,26,123,124,125],"水肥优化","多目标优化","模型模拟",{"code":31,"slug":32,"name":26,"path":33,"parent_code":34},[128,133],{"code":129,"slug":130,"name":131,"path":132,"parent_code":41},"H2","soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil",{"code":37,"slug":38,"name":39,"path":40,"parent_code":41},{"status":43,"locked":13,"version":44,"method":45,"evidence":135},{"candidates":136,"agricultural_evidence":50},[137,142,147,152,156],{"code":31,"score":138,"supported":50,"title":139,"abstract":140,"tags":141},15,[32],[26,32],[26],{"code":129,"score":18,"supported":50,"title":143,"abstract":144,"tags":146},[130],[145,130],"土壤",[],{"code":37,"score":18,"supported":50,"title":148,"abstract":149,"tags":151},[38],[150,38],"灌溉",[],{"code":76,"score":20,"supported":13,"title":153,"abstract":154,"tags":155},[],[],[25],{"code":157,"score":20,"supported":13,"title":158,"abstract":159,"tags":160},"I2",[],[],[125],[162,163],"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",3,"10.1016\u002Fj.agsy.2026.104953",{"doi":166,"openalex_id":168,"authors":169,"venue":9,"cited_by_count":103,"oa_url":112,"card":186,"direction":190,"ingested_from":104},"W7205003045",[170,172,175,178,180,181,183,185],{"name":171,"orcid":8},"Yongqiang Wang",{"name":173,"orcid":174},"Fugui Wang","https:\u002F\u002Forcid.org\u002F0000-0002-8349-8398",{"name":176,"orcid":177},"Lanfang Bai","https:\u002F\u002Forcid.org\u002F0000-0001-7862-2844",{"name":179,"orcid":8},"Zhen Wang",{"name":179,"orcid":8},{"name":182,"orcid":8},"Haofang Yan",{"name":184,"orcid":8},"Zhigang Wang",{"name":184,"orcid":8},{"tldr":187,"method":188,"finding":189,"direction":190,"opportunity":191},"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":194,"title":195,"url":196,"summary":197,"summary_zh":198,"content":8,"source_name":199,"source_url":196,"published_at":200,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":201,"score_detail":202,"sources":204,"tags":206,"primary_topic":211,"secondary_topics":212,"classification":214,"search_phrases":246,"slug":249,"view_count":165,"doi":250,"paper":251,"created_at":268},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%。","International Journal of Remote Sensing","2026-09-06T00:00:00Z",72,{"impact":138,"substance":118,"depth":16,"authority":18,"freshness":165,"relevant":20,"comment":203},"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。",[205],{"name":199,"url":196},[25,207,208,209,210],"机器学习","遥感","作物模型","精准灌溉",{"code":31,"slug":32,"name":26,"path":33,"parent_code":34},[213],{"code":37,"slug":38,"name":39,"path":40,"parent_code":41},{"status":43,"locked":13,"version":44,"method":45,"evidence":215},{"candidates":216,"agricultural_evidence":50},[217,221,226,233,237,242],{"code":31,"score":18,"supported":50,"title":218,"abstract":219,"tags":220},[32],[26,32],[],{"code":37,"score":49,"supported":50,"title":222,"abstract":224,"tags":225},[223],"evapotranspiration",[223],[210],{"code":227,"score":228,"supported":50,"title":229,"abstract":230,"tags":232},"I3",7,[],[208,231],"remote sensing",[208],{"code":129,"score":107,"supported":50,"title":234,"abstract":235,"tags":236},[],[145,130],[],{"code":157,"score":238,"supported":50,"title":239,"abstract":240,"tags":241},5,[],[207],[209,207],{"code":76,"score":20,"supported":13,"title":243,"abstract":244,"tags":245},[],[],[25],[247,248],"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":250,"openalex_id":252,"authors":253,"venue":199,"cited_by_count":103,"oa_url":8,"card":262,"direction":266,"ingested_from":104},"W7210284621",[254,257,260],{"name":255,"orcid":256},"Edson Costa‐Filho","https:\u002F\u002Forcid.org\u002F0000-0001-5610-7382",{"name":258,"orcid":259},"José L. Chávez","https:\u002F\u002Forcid.org\u002F0000-0001-6456-0822",{"name":261,"orcid":8},"Huihui Zhang",{"tldr":263,"method":264,"finding":265,"direction":266,"opportunity":267},"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。","2026-09-08T23:30:20.154043Z",{"id":270,"title":271,"url":272,"summary":273,"summary_zh":274,"content":8,"source_name":275,"source_url":272,"published_at":276,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":277,"score_detail":278,"sources":282,"tags":284,"primary_topic":286,"secondary_topics":287,"classification":288,"search_phrases":315,"slug":318,"view_count":306,"doi":319,"paper":320,"created_at":349},3620,"Assessment of corn chlorophyll content via UAV‐derived vegetative indices across growth stages","https:\u002F\u002Fdoi.org\u002F10.1002\u002Fagg2.70437","Abstract Traditional chlorophyll (Chl) assessment methods are labor‐intensive and spatially limited. This study evaluated unmanned aerial vehicle (UAV) multispectral imagery for nondestructive field‐scale canopy Chl estimation in corn ( Zea mays L.) to support precision nutrient management. Field experiments were conducted at two Virginia Tech research farms under two nitrogen (N) rates, 112 and 224 kg N ha − 1 , and five phosphorus (P) rates, 0, 56, 112, 168, and 224 kg P 2 O 5 ha − 1 . Canopy Chl was measured using a SPAD‐502 Plus chlorophyll meter (where SPAD is soil plant analysis development). UAV imagery was collected at three sampling times using a DJI Mavic 3 M: early vegetative (Ve‐E), late vegetative (Ve‐L), and reproductive stages, corresponding to V4–V6, V11–V13, and R3–R5 growth stages, respectively. Eight vegetation indices (VIs) were derived from green, red, red‐edge, and near‐infrared bands. Relationships between SPAD and spectral predictors were evaluated using Pearson correlation, simple and multiple linear regression, Lasso, and Elastic Net. Model performance was assessed using fivefold cross‐validated (CV) estimates repeated 30 times with CV R 2 , root mean square error, and relative RMSE. Predictive accuracy was moderate and varied by site and time, with strongest relationships during Ve‐L. At Ve‐L, Green Normalized Difference Vegetation Index (GNDVI) and Chlorophyll Index Red Edge were the best predictors at Orange and Kentland, respectively. Multivariate and regularized models performed comparably but did not improve accuracy over the best single‐VI models. Overall, GNDVI and red‐edge‐based indices were the most useful indicators of canopy Chl variation. These findings indicate that UAV‐derived GNDVI and red‐edge indices can provide moderately accurate and interpretable estimates of SPAD‐based Chl status.","传统叶绿素（Chl）评估方法费时费力且空间覆盖有限。本研究评估了无人机（UAV）多光谱影像在玉米（Zea mays L.）田块尺度冠层叶绿素无损估测中的应用，以支持精准养分管理。田间试验在弗吉尼亚理工大学的两个研究农场进行，设置两个氮（N）水平，分别为112和224 kg N ha⁻¹，以及五个磷（P）水平，分别为0、56、112、168和224 kg P₂O₅ ha⁻¹。使用SPAD-502 Plus叶绿素仪（SPAD即土壤植物分析开发）测定冠层叶绿素。使用DJI Mavic 3 M在三个采样时期采集无人机影像：营养生长早期（Ve-E）、营养生长晚期（Ve-L）和生殖生长阶段，分别对应V4–V6、V11–V13和R3–R5生育时期。从绿、红、红边和近红外波段提取了8种植被指数（VIs）。采用Pearson相关、简单和多元线性回归、Lasso及弹性网（Elastic Net）评估SPAD与光谱预测变量之间的关系。模型性能通过重复30次的五折交叉验证（CV）估计进行评估，指标包括交叉验证R²、均方根误差和相对均方根误差。预测精度为中等水平，且因地点和时间而异，其中Ve-L时期关系最强。在Ve-L时期，绿归一化差异植被指数（GNDVI）和红边叶绿素指数分别在Orange和Kentland表现最佳。多变量模型和正则化模型表现相当，但未能比最佳单植被指数模型提高精度。总体而言，GNDVI和基于红边的指数是冠层叶绿素变异最有用的指标。这些结果表明，无人机获取的GNDVI和红边指数能够对基于SPAD的叶绿素状况提供中等精度且可解释的估测。","Agrosystems Geosciences & Environment","2026-09-24T00:00:00Z",69,{"impact":49,"substance":17,"depth":279,"authority":280,"freshness":19,"relevant":20,"comment":281},16,13,"基于无人机多光谱影像评估玉米冠层叶绿素，方法规范、结论明确，对精准养分管理有参考价值，但属细分领域研究，公共影响有限。",[283],{"name":275,"url":272},[25,26,208,29,285],"植被指数",{"code":31,"slug":32,"name":26,"path":33,"parent_code":34},[],{"status":43,"locked":13,"version":44,"method":45,"evidence":289},{"candidates":290,"agricultural_evidence":50},[291,296,300,305,311],{"code":31,"score":138,"supported":50,"title":292,"abstract":294,"tags":295},[293],"corn",[26,293],[26],{"code":129,"score":107,"supported":50,"title":297,"abstract":298,"tags":299},[],[145,130],[],{"code":227,"score":238,"supported":50,"title":301,"abstract":302,"tags":304},[],[303],"multispectral",[285,208],{"code":60,"score":306,"supported":50,"title":307,"abstract":308,"tags":310},4,[],[309],"养分管理",[29],{"code":76,"score":20,"supported":13,"title":312,"abstract":313,"tags":314},[],[],[25],[316,317],"无人机 玉米 叶绿素 遥感","Virginia Tech 玉米 氮肥 SPAD","无人机玉米叶绿素遥感-3620","10.1002\u002Fagg2.70437",{"doi":319,"openalex_id":321,"authors":322,"venue":275,"cited_by_count":103,"oa_url":343,"card":344,"direction":266,"ingested_from":104},"W7214232972",[323,326,329,332,334,336,338,340],{"name":324,"orcid":325},"Aarati Khulal","https:\u002F\u002Forcid.org\u002F0000-0003-4405-1320",{"name":327,"orcid":328},"Huijie Gan","https:\u002F\u002Forcid.org\u002F0000-0001-6634-5704",{"name":330,"orcid":331},"Jitender Rathore","https:\u002F\u002Forcid.org\u002F0009-0006-5764-1652",{"name":333,"orcid":8},"Sheetal Kumari",{"name":335,"orcid":8},"Ivy Flory",{"name":337,"orcid":8},"Caleb Bishop",{"name":339,"orcid":8},"Santosh Rijal",{"name":341,"orcid":342},"Olga S. Walsh","https:\u002F\u002Forcid.org\u002F0000-0002-2958-931X","https:\u002F\u002Fonlinelibrary.wiley.com\u002Fdoi\u002Fpdfdirect\u002F10.1002\u002Fagg2.70437",{"tldr":345,"method":346,"finding":347,"direction":266,"opportunity":348},"用无人机多光谱植被指数评估玉米不同生育期冠层叶绿素含量。","DJI Mavic 3 M多光谱影像，8个植被指数，SPAD实测，线性回归与La","GNDVI与红边指数在营养生长后期预测SPAD最准，多变量模型未优于单指数。","可探索多生育期时序融合与多源数据耦合，提升跨站点跨年份叶绿素估算泛化能力。","2026-09-27T23:30:35.468236Z",{"id":351,"title":352,"url":353,"summary":354,"summary_zh":8,"content":8,"source_name":9,"source_url":353,"published_at":355,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":356,"score_detail":357,"sources":359,"tags":361,"primary_topic":362,"secondary_topics":363,"classification":364,"search_phrases":383,"slug":386,"view_count":20,"doi":387,"paper":388,"created_at":397},1713,"Nitrogen management on coarse Sandy soils: A DSSAT-based mechanistic evaluation of controlled-release and conventional fertilizers for maize","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.agsy.2026.104954","Nitrogen management on coarse Sandy soils: A DSSAT-based mechanistic evaluation of controlled-release and conventional fertilizers for maize。Agricultural Systems","2026-09-04T00:00:00Z",62,{"impact":49,"substance":16,"depth":279,"authority":18,"freshness":61,"relevant":20,"comment":358},"基于DSSAT模型对粗砂土玉米控释肥与常规肥的机制评估，方法新颖，数据可靠，但时效性较低。",[360],{"name":9,"url":353},[25,26,29,125],{"code":31,"slug":32,"name":26,"path":33,"parent_code":34},[],{"status":43,"locked":13,"version":44,"method":45,"evidence":365},{"candidates":366,"agricultural_evidence":50},[367,371,375,379],{"code":31,"score":49,"supported":50,"title":368,"abstract":369,"tags":370},[32],[32],[26],{"code":60,"score":20,"supported":13,"title":372,"abstract":373,"tags":374},[],[],[29],{"code":76,"score":20,"supported":13,"title":376,"abstract":377,"tags":378},[],[],[25],{"code":157,"score":20,"supported":13,"title":380,"abstract":381,"tags":382},[],[],[125],[384,385],"智慧农业 模型模拟 氮肥管理 玉米","智慧农业 模型模拟","智慧农业模型模拟氮肥管理玉米-1713","10.1016\u002Fj.agsy.2026.104954",{"doi":387,"openalex_id":389,"authors":390,"venue":9,"cited_by_count":103,"oa_url":353,"card":8,"direction":8,"ingested_from":104},"W7208709428",[391,393,395],{"name":392,"orcid":8},"Rakesh K. Singh",{"name":394,"orcid":8},"Gerrit Hoogenboom",{"name":396,"orcid":8},"Vivek Sharma","2026-09-05T23:30:05.498995Z",{"id":399,"title":400,"url":401,"summary":402,"summary_zh":403,"content":8,"source_name":404,"source_url":401,"published_at":405,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":277,"score_detail":406,"sources":409,"tags":411,"primary_topic":415,"secondary_topics":420,"classification":422,"search_phrases":456,"slug":459,"view_count":103,"doi":460,"paper":461,"created_at":477},5496,"Multi-Crop Irrigation Need Classification Based on Machine Learning with an Indonesian-Language GenAI-UX Recommendation Layer","https:\u002F\u002Fdoi.org\u002F10.30871\u002Fjaic.v10i5.13774","Inefficient irrigation water use remains a critical challenge in Indonesian agriculture, and the complexity of raw sensor data hinders adoption by smallholder farmers who lack data-analysis skills. This study develops an intelligent irrigation Decision Support System that integrates interpretable Machine Learning with an Indonesian-language GenAI-UX recommendation layer, using a publicly available multi-crop irrigation scheduling dataset (501 records, nine crops) with an analytical focus on paddy and maize. Four components are combined into one pipeline: (i) irrigation-need classification compared across six algorithms; (ii) regression of a continuous water-requirement score; (iii) sensor-anomaly detection using Isolation Forest cross-validated with the Interquartile Range method; and (iv) a rule-based natural-language recommendation layer whose operating point is optimized by a Genetic Algorithm. Validity was safeguarded through stratified five-fold cross-validation and leakage-controlled preprocessing. Gradient Boosting achieved the best classification performance (F1-score 0.980; cross-validated F1 0.963), Random Forest Regressor reached R square of 0.807, Isolation Forest agreed with IQR outliers at 95.2%, and the Genetic Algorithm attained a confidence of 0.993 within realistic agronomic bounds. In an illustrative usability evaluation, the GenAI-UX interface obtained the highest System Usability Scale score (Good category). The results indicate that pairing accurate, interpretable models with a language-based delivery layer can transform complex sensor data into actionable irrigation recommendations, lowering the adoption barrier for non-technical users. Field validation with real respondents and edge deployment are identified as the next steps.","灌溉用水效率低下仍是印度尼西亚农业面临的一项关键挑战，而原始传感器数据的复杂性阻碍了缺乏数据分析技能的小农户采用相关技术。本研究开发了一种智能灌溉决策支持系统（Decision Support System），将可解释的机器学习与印度尼西亚语生成式人工智能用户体验（GenAI-UX）推荐层相结合，使用公开可用的多作物灌溉调度数据集（501条记录，九种作物），并以水稻和玉米为分析重点。四个组件被整合为一条流水线：（i）在六种算法之间比较灌溉需求分类；（ii）对连续需水量得分进行回归；（iii）使用孤立森林（Isolation Forest）进行传感器异常检测，并与四分位距（IQR）方法交叉验证；（iv）基于规则的自然语言推荐层，其操作点由遗传算法优化。通过分层五折交叉验证和防泄漏预处理保障了有效性。梯度提升（Gradient Boosting）取得了最佳分类性能（F1分数0.980；交叉验证F1为0.963），随机森林回归器（Random Forest Regressor）达到R平方0.807，孤立森林与IQR异常值的一致率为95.2%，遗传算法在现实农艺范围内达到0.993的置信度。在一项示例性可用性评估中，GenAI-UX界面获得了最高的系统可用性量表（System Usability Scale）得分（良好类别）。结果表明，将准确、可解释的模型与基于语言的交付层相结合，能够将复杂的传感器数据转化为可操作的灌溉建议，从而降低非技术用户的采用门槛。与真实受访者进行田间验证以及边缘部署被确定为下一步工作。","Journal of Applied Informatics and Computing","2026-10-19T00:00:00Z",{"impact":49,"substance":17,"depth":407,"authority":49,"freshness":19,"relevant":20,"comment":408},17,"方法组合完整、指标扎实的农业AI决策支持论文，但属印尼小农户场景的实验室级研究，公共影响有限，适合作为技术参考而非每日精选头条。",[410],{"name":404,"url":401},[25,412,413,26,414],"农业人工智能","水稻","灌溉决策",{"code":157,"slug":416,"name":417,"path":418,"parent_code":419},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[421],{"code":37,"slug":38,"name":39,"path":40,"parent_code":41},{"status":43,"locked":13,"version":44,"method":45,"evidence":423},{"candidates":424,"agricultural_evidence":50},[425,431,435,441,445,452],{"code":157,"score":16,"supported":50,"title":426,"abstract":428,"tags":430},[427],"machine learning",[207,427,429],"decision support",[412],{"code":37,"score":138,"supported":50,"title":432,"abstract":433,"tags":434},[38],[150,38],[414],{"code":436,"score":228,"supported":50,"title":437,"abstract":438,"tags":440},"A1",[],[413,439],"paddy",[413],{"code":31,"score":228,"supported":50,"title":442,"abstract":443,"tags":444},[],[26,32],[26],{"code":446,"score":107,"supported":50,"title":447,"abstract":448,"tags":451},"G2",[],[449,450],"小农户","smallholder",[],{"code":76,"score":20,"supported":13,"title":453,"abstract":454,"tags":455},[],[],[25],[457,458],"印尼 多作物 灌溉决策","GenAI 灌溉推荐","印尼多作物灌溉决策-5496","10.30871\u002Fjaic.v10i5.13774",{"doi":460,"openalex_id":462,"authors":463,"venue":404,"cited_by_count":103,"oa_url":401,"card":471,"direction":190,"ingested_from":104},"W7223016583",[464,467,469],{"name":465,"orcid":466},"Muhammad Rusdi","https:\u002F\u002Forcid.org\u002F0000-0002-1048-5000",{"name":468,"orcid":8},"Zaenuddin Zaenuddin",{"name":470,"orcid":8},"Hendra Sanjaya",{"tldr":472,"method":473,"finding":474,"direction":475,"opportunity":476},"构建融合可解释机器学习与印尼语GenAI推荐层的智能灌溉决策支持系统。","六种分类算法、回归、孤立森林异常检测与遗传算法优化规则推荐层。","梯度提升分类F1达0.980，GenAI界面可用性最佳，降低非技术用户使用门槛。","智慧农业 \u002F 农业物联网","可探索多语言本地化GenAI推荐层与边缘部署，并开展真实农户田间验证。","2026-10-21T23:30:46.441864Z",{"id":479,"title":480,"url":481,"summary":482,"summary_zh":483,"content":8,"source_name":484,"source_url":481,"published_at":485,"category":11,"cover_url":8,"hotness":12,"is_selected":13,"score":486,"score_detail":487,"sources":490,"tags":492,"primary_topic":494,"secondary_topics":495,"classification":497,"search_phrases":515,"slug":518,"view_count":103,"doi":519,"paper":520,"created_at":534},4759,"Machine Learning-based Prediction of SVI Parameter in Maize: A Comparative Study and Feature Importance Analysis","https:\u002F\u002Fdoi.org\u002F10.24925\u002Fturjaf.v14i10.2870-2879.9043","Background: Accurate estimation of seedling vigor indices (SVI) is critical for monitoring plant health and predicting yields in precision agriculture. This study comparatively evaluates the performance of different machine learning algorithms in estimating the SVI parameter. Methods: Germination data were collected from 39 maize genotypes under three different salinity conditions (0 mM, 100 mM, and 200 mM NaCl), including the parameters Gmax, MGT, GI, SL, RL, SdL, SMR, RMR, and SVI. The dataset was preprocessed through normalization to the [0–1] range. The SVI parameter was estimated using four different machine learning methods: Random Forest, CatBoost, LightGBM, and Support Vector Regression (SVR). To evaluate the performance of the models, both 10-fold cross-validation and 80%–20% hold-out validation techniques were employed. The model performance was examined using RMSE, MSE, MAE, and R² metrics. Results: The findings show that all models have a high explanatory power in the training dataset. Specifically, the CatBoost model showed the highest success in the training phase (R²=0.9962), whereas the SVR model achieved the highest generalization performance on the test dataset (R²=0.9712, RMSE=0.0582). Feature importance analysis results revealed that the 'App' parameter had the highest impact on the target variable in most models, but different structural interactions were prominent in the LightGBM model. The results demonstrate that machine learning techniques provide high accuracy in SVI estimation and that the tendency towards overfitting is limited. Conclusions: In conclusion, this study confirms that machine learning techniques are powerful and reliable tools for estimating spectral indices in precision agriculture applications. The integration of SVR and ensemble-based models into agricultural decision support systems is expected to significantly contribute to optimizing resource use and improving yield predictions.","背景：准确估计幼苗活力指数（SVI）对于精准农业中监测植物健康和预测产量至关重要。本研究比较评估了不同机器学习算法在估计SVI参数方面的性能。方法：从39个玉米基因型在三种不同盐度条件（0 mM、100 mM和200 mM NaCl）下收集发芽数据，包括Gmax、MGT、GI、SL、RL、SdL、SMR、RMR和SVI等参数。数据集通过归一化至[0–1]范围进行预处理。采用四种不同的机器学习方法估计SVI参数：随机森林、CatBoost、LightGBM和支持向量回归（SVR）。为评估模型性能，采用了10折交叉验证和80%–20%留出验证技术。使用RMSE、MSE、MAE和R²指标检验模型性能。结果：研究结果表明，所有模型在训练数据集中均具有较高的解释力。具体而言，CatBoost模型在训练阶段表现出最高的成功率（R²=0.9962），而SVR模型在测试数据集上取得了最高的泛化性能（R²=0.9712，RMSE=0.0582）。特征重要性分析结果显示，在大多数模型中，“App”参数对目标变量的影响最大，但在LightGBM模型中不同的结构交互作用更为突出。结果表明，机器学习技术在SVI估计中提供了高精度，且过拟合倾向有限。结论：总之，本研究证实机器学习技术是精准农业应用中估计光谱指数的强大而可靠的工具。将SVR和基于集成的模型整合到农业决策支持系统中，预计将显著有助于优化资源利用和改善产量预测。","Turkish Journal of Agriculture - Food Science and Technology","2026-10-11T00:00:00Z",63,{"impact":19,"substance":16,"depth":279,"authority":49,"freshness":488,"relevant":20,"comment":489},9,"方法对比扎实、结论可靠，但属细分领域论文，产业影响有限，适合主题聚合而非头条精选。",[491],{"name":484,"url":481},[25,412,207,26,493],"耐盐育种",{"code":31,"slug":32,"name":26,"path":33,"parent_code":34},[496],{"code":157,"slug":416,"name":417,"path":418,"parent_code":419},{"status":43,"locked":13,"version":44,"method":45,"evidence":498},{"candidates":499,"agricultural_evidence":50},[500,505,509],{"code":157,"score":501,"supported":50,"title":502,"abstract":503,"tags":504},19,[427],[207,427,429],[412,207],{"code":31,"score":138,"supported":50,"title":506,"abstract":507,"tags":508},[32],[26,32],[26],{"code":76,"score":228,"supported":50,"title":510,"abstract":511,"tags":514},[],[512,513],"精准农业","precision agriculture",[25],[516,517],"玉米 SVI 机器学习","玉米 耐盐 幼苗活力指数","玉米SVI机器学习-4759","10.24925\u002Fturjaf.v14i10.2870-2879.9043",{"doi":519,"openalex_id":521,"authors":522,"venue":484,"cited_by_count":103,"oa_url":481,"card":529,"direction":190,"ingested_from":104},"W7221462188",[523,526],{"name":524,"orcid":525},"Mustafa Turan Arslan","https:\u002F\u002Forcid.org\u002F0000-0001-5498-6571",{"name":527,"orcid":528},"Gülay Zülkadir","https:\u002F\u002Forcid.org\u002F0000-0003-3488-4011",{"tldr":530,"method":531,"finding":532,"direction":190,"opportunity":533},"用四种机器学习模型基于39个玉米基因型盐胁迫发芽数据预测幼苗活力指数SVI。","39个玉米基因型在三种盐浓度下的发芽数据，归一化后用RF、CatBoost、Li","SVR泛化最优（R²=0.9712），CatBoost训练拟合最高；App参数对SVI影响最大。","可扩展到多作物多胁迫环境，结合光谱或图像表型数据提升SVI预测的田间适用性。","2026-10-12T23:30:44.762497Z"]