[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4477":3,"related-4477":87},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":8,"source_name":9,"source_url":8,"published_at":8,"category":10,"cover_url":8,"hotness":11,"is_selected":12,"score":13,"score_detail":14,"sources":21,"tags":23,"primary_topic":30,"secondary_topics":35,"classification":36,"search_phrases":71,"slug":74,"view_count":75,"doi":8,"paper":76,"created_at":86},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。",null,"Precision Agriculture in Maize Production Systematic Review 2026","论文",10,false,74,{"impact":15,"substance":16,"depth":15,"authority":17,"freshness":18,"relevant":19,"comment":20},18,22,13,3,1,"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。",[22],{"name":9,"url":6},[24,25,26,27,28,29],"智慧农业","产量预测","机器学习","玉米","精准农业","遥感",{"code":31,"slug":32,"name":27,"path":33,"parent_code":34},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[],{"status":37,"locked":12,"version":38,"method":39,"evidence":40},"classified","2026-10-22.2","rules",{"candidates":41,"agricultural_evidence":44},[42,48,53,59,65],{"code":43,"score":17,"supported":44,"title":45,"abstract":46,"tags":47},"I1",true,[28],[28],[24,28],{"code":31,"score":49,"supported":44,"title":50,"abstract":51,"tags":52},12,[27],[27],[27],{"code":54,"score":55,"supported":44,"title":56,"abstract":57,"tags":58},"I3",5,[],[29],[25,29],{"code":60,"score":61,"supported":44,"title":62,"abstract":63,"tags":64},"I2",4,[],[26],[26],{"code":66,"score":18,"supported":44,"title":67,"abstract":68,"tags":70},"H2",[],[69],"土壤",[],[72,73],"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",2,{"doi":8,"openalex_id":8,"authors":77,"venue":8,"cited_by_count":78,"oa_url":8,"card":79,"direction":83,"ingested_from":85},[],0,{"tldr":80,"method":81,"finding":82,"direction":83,"opportunity":84},"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。","智慧农业 \u002F 农业物联网","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",{"total":88,"page":19,"page_size":88,"items":89},6,[90,166,214,284,357,416],{"id":91,"title":92,"url":93,"summary":94,"summary_zh":95,"content":8,"source_name":96,"source_url":93,"published_at":97,"category":10,"cover_url":8,"hotness":11,"is_selected":12,"score":98,"score_detail":99,"sources":104,"tags":106,"primary_topic":108,"secondary_topics":109,"classification":115,"search_phrases":140,"slug":143,"view_count":18,"doi":144,"paper":145,"created_at":165},3177,"A comparative analysis on maize yield prediction using sentinel 2A and Landsat 8 satellite image in Sundarganj, Gaibandha, Bangladesh","https:\u002F\u002Fdoi.org\u002F10.3329\u002Fbjar.v51i1.92530","Maize is an important cereal crops in Bangladesh. Over the last two decades, its cultivation has increased promisingly, especially in the Northern part of Bangladesh. The effective estimation of crop yields at a regional scale holds significant importance in facilitating decision-making within the agricultural sector, thereby ensuring grain security. The traditional ground-based measurement techniques suffer from inefficiencies, and there exists a need for a reliable, precise, and effective method for estimating regional crop yields. This study used remote sensing (RS) techniques for forecasting pre-harvest maize yield to improve the management system. Currently, the normalized difference vegetation index (NDVI) is widely used to predict crop yield including maize. However, the present study used Landsat 8 (~ 30 m) and Sentinel 2A (~ 10 m) high resolution data for 2018-2019 and 2019-2020 to predict maize yield based on the year 2020-2021 at Sundarganj Upazila in Gaibandha district. The single cloud free image acquisition date based on maximum NDVI for both satellite images was used for each maize growing period to develop a yield prediction model. A regression model was performed between NDVI values and 20 farmers field-level maize yields. The absolute mean error of prediction was about 10.30% for Landsat 8 and 6.70% for Sentinel 2A compared to the actual maize yield during 2020-2021. The study revealed that NDVI data extracted from Sentinel 2A high resolution satellite images can be successfully used to predict the maize yield with appreciable accuracy. Finally, this study has demonstrated the efficacy of combining multi-temporal remote sensing data for accurate maize yield estimation, aiding agricultural authorities and production enterprises in the timely formulation and refinement of cropping strategies and management policies for the ongoing season. Bangladesh J. Agril. Res. 51(1): 501-521, March 2026","玉米是孟加拉国重要的谷类作物。过去二十年间，其种植面积增长显著，尤其是在孟加拉国北部地区。在区域尺度上有效估算作物产量对于促进农业部门决策、进而保障粮食安全具有重要意义。传统的地面测量技术效率低下，亟需一种可靠、精确且有效的区域作物产量估算方法。本研究采用遥感（RS）技术预测收获前玉米产量，以改进管理体系。目前，归一化植被指数（NDVI）被广泛用于预测包括玉米在内的作物产量。然而，本研究利用Landsat 8（约30 m）和Sentinel 2A（约10 m）高分辨率数据，基于2018—2019年和2019—2020年的数据，对盖班达县孙达尔甘杰乌帕齐拉2020—2021年的玉米产量进行预测。在每个玉米生长期，选取两颗卫星影像中NDVI最大值对应的单幅无云影像获取日期，用于建立产量预测模型。对NDVI值与20户农民田块级玉米产量进行回归建模。与2020—2021年实际玉米产量相比，Landsat 8的绝对平均预测误差约为10.30%，Sentinel 2A约为6.70%。研究表明，利用Sentinel 2A高分辨率卫星影像提取的NDVI数据可成功用于预测玉米产量，且精度令人满意。最后，本研究证明了结合多时相遥感数据进行准确玉米产量估算的有效性，有助于农业主管部门和生产企业在当季及时制定和完善种植策略与管理政策。Bangladesh J. Agril. Res. 51(1): 501-521, March 2026","Bangladesh Journal of Agricultural Research","2026-09-21T00:00:00Z",65,{"impact":100,"substance":101,"depth":102,"authority":17,"freshness":100,"relevant":19,"comment":103},8,20,16,"基于Sentinel 2A与Landsat 8的玉米遥感估产对比研究，方法清晰、误差数据具体，对遥感估产有参考价值，但属区域小尺度研究，公共影响有限。",[105],{"name":96,"url":93},[24,25,27,29,107],"NDVI",{"code":31,"slug":32,"name":27,"path":33,"parent_code":34},[110],{"code":54,"slug":111,"name":112,"path":113,"parent_code":114},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":37,"locked":12,"version":38,"method":39,"evidence":116},{"candidates":117,"agricultural_evidence":44},[118,123,129,136],{"code":31,"score":119,"supported":44,"title":120,"abstract":121,"tags":122},15,[32],[27,32],[27],{"code":54,"score":124,"supported":44,"title":125,"abstract":126,"tags":128},9,[],[29,127],"remote sensing",[107,25,29],{"code":130,"score":88,"supported":44,"title":131,"abstract":132,"tags":135},"A8",[],[133,134],"粮食安全","grain security",[],{"code":43,"score":19,"supported":12,"title":137,"abstract":138,"tags":139},[],[],[24],[141,142],"Sentinel 2A Landsat 8 玉米产量预测","孟加拉国 Sundarganj 玉米遥感估产","Sentinel2ALandsat8玉米产量预测-3177","10.3329\u002Fbjar.v51i1.92530",{"doi":144,"openalex_id":146,"authors":147,"venue":96,"cited_by_count":78,"oa_url":93,"card":158,"direction":162,"ingested_from":164},"W7213918280",[148,150,152,154,156],{"name":149,"orcid":8},"N Mohammad",{"name":151,"orcid":8},"MA Islam",{"name":153,"orcid":8},"MG Mahboob",{"name":155,"orcid":8},"MM Rahman",{"name":157,"orcid":8},"I Ahmed",{"tldr":159,"method":160,"finding":161,"direction":162,"opportunity":163},"用Sentinel 2A与Landsat 8的NDVI回归模型预测孟加拉国玉米产量并比较精度。","基于最大NDVI单期影像与20个农户地块产量做回归，比较两种卫星。","Sentinel 2A预测绝对平均误差6.70%，优于Landsat 8的10.30%。","农业遥感与作物表型","可探索多时相NDVI与机器学习融合，提升小农户尺度玉米估产精度与迁移性。","openalex","2026-09-22T23:30:23.551878Z",{"id":167,"title":168,"url":169,"summary":170,"summary_zh":8,"content":8,"source_name":171,"source_url":8,"published_at":172,"category":10,"cover_url":8,"hotness":11,"is_selected":12,"score":173,"score_detail":174,"sources":176,"tags":178,"primary_topic":180,"secondary_topics":181,"classification":182,"search_phrases":201,"slug":204,"view_count":75,"doi":8,"paper":205,"created_at":213},2271,"MDPI Agriculture 16(17):1932 厄瓜多尔农场级玉米产量模型空间可迁移性研究","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F17\u002F1932","MDPI Agriculture刊载厄瓜多尔马纳比省农场记录级玉米产量预测模型研究,基于518份数据评估Elastic Net、随机森林和直方图梯度提升模型。结果显示所有模型配置产生负的合并R2值,空间交叉验证显示空间乐观偏差,提示农场管理数据不足以支持对未见地区单作物记录的可靠产量预测。","MDPI Agriculture 16(17):1932","2026-09-06T16:00:00Z",63,{"impact":100,"substance":101,"depth":102,"authority":17,"freshness":88,"relevant":19,"comment":175},"基于518份农场记录检验多种机器学习模型的跨区域可迁移性，得出负面结论，对农业数据驱动产量预测的适用边界有实质警示价值，但属细分方法学研究，影响范围有限。",[177],{"name":171,"url":169},[24,25,26,27,179],"空间迁移",{"code":31,"slug":32,"name":27,"path":33,"parent_code":34},[],{"status":37,"locked":12,"version":38,"method":39,"evidence":183},{"candidates":184,"agricultural_evidence":44},[185,189,193,197],{"code":31,"score":49,"supported":44,"title":186,"abstract":187,"tags":188},[27],[27],[27],{"code":43,"score":19,"supported":12,"title":190,"abstract":191,"tags":192},[],[],[24],{"code":60,"score":19,"supported":12,"title":194,"abstract":195,"tags":196},[],[],[26],{"code":54,"score":19,"supported":12,"title":198,"abstract":199,"tags":200},[],[],[25],[202,203],"产量预测 智慧农业 机器学习 空间迁移","产量预测 智慧农业","产量预测智慧农业机器学习空间迁移-2271",{"doi":8,"openalex_id":8,"authors":206,"venue":8,"cited_by_count":78,"oa_url":8,"card":207,"direction":211,"ingested_from":85},[],{"tldr":208,"method":209,"finding":210,"direction":211,"opportunity":212},"评估三种机器学习模型在厄瓜多尔农场级玉米产量预测中的空间可迁移性。","基于518份农场记录，用Elastic Net、随机森林和直方图梯度提升做空间交","所有模型合并R2为负，存在空间乐观偏差，农场管理数据不足以可靠预测未见地区产量。","农业人工智能与决策模型","可探索融合遥感、气象与土壤等多源数据，提升小农户农场级产量模型的空间泛化能力。","2026-09-13T00:04:04.999192Z",{"id":215,"title":216,"url":217,"summary":218,"summary_zh":219,"content":8,"source_name":220,"source_url":217,"published_at":221,"category":10,"cover_url":8,"hotness":11,"is_selected":12,"score":222,"score_detail":223,"sources":226,"tags":228,"primary_topic":230,"secondary_topics":231,"classification":233,"search_phrases":250,"slug":252,"view_count":55,"doi":253,"paper":254,"created_at":283},2117,"A source-side trait-mediated framework for dynamic maize yield prediction using UAV multispectral imagery","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112375","Dynamic in-season maize yield prediction is essential for cultivation management and precision decision-making. However, traditional yield prediction models based on vegetation indices often lack physiological interpretability, and integrating multitemporal dynamic information within a unified framework remains challenging. This study proposes a source-side trait-mediated approach for dynamic maize yield prediction using UAV multispectral imagery. Leaf area index (LAI), leaf chlorophyll content (SPAD), and aboveground biomass (AGB) were used as intermediate variables. A cascaded framework linking canopy spectra, source-side agronomic traits, and yield was constructed to improve agronomic interpretability. In addition, the temporal dynamic factor (TDF) was introduced to model stage-dependent trait-yield relationships, enabling dynamic yield prediction. The results showed that increasing planting density significantly enhanced LAI and AGB, whereas yield first increased with planting density and then decreased slightly. For agronomic trait prediction, XGBoost generally outperformed RF. In the independent multi-cultivar test, the XGBoost models achieved coefficients of determination (R 2 ) of 0.8185, 0.8758, and 0.8113 for LAI, SPAD, and AGB, respectively. The TDF fitting results for the 2025 dataset showed that the linear relationship between yield and the Comprehensive Yield Index (CYI) had R 2 values ranging from 0.67 to 0.86 and RMSE values from 0.428 to 0.648 t\u002Fha across growth stages. In the 2024 interannual independent test, the model maintained an R 2 of 0.7803 and an RMSE of 0.6076 t\u002Fha. Furthermore, in a multi-cultivar scenario, yield prediction achieved an R 2 of 0.6824 and an RMSE of 0.3746 t\u002Fha. The proposed framework improved the agronomic interpretability and interannual and cross-cultivar applicability of maize yield prediction using UAV-based multitemporal imagery.","动态的玉米生长季产量预测对栽培管理和精准决策至关重要。然而，传统的基于植被指数的产量预测模型往往缺乏生理可解释性，且在统一框架内整合多时相动态信息仍具挑战性。本研究提出了一种基于源端性状介导的方法，利用无人机多光谱影像进行玉米动态产量预测。以叶面积指数（LAI）、叶片叶绿素含量（SPAD）和地上生物量（AGB）作为中间变量，构建了连接冠层光谱、源端农艺性状与产量的级联框架，以提高农艺可解释性。此外，引入时间动态因子（TDF）来建模阶段依赖的性状-产量关系，从而实现动态产量预测。结果表明，增加种植密度显著提高了LAI和AGB，而产量随种植密度先增加后略有下降。在农艺性状预测方面，XGBoost通常优于RF。在独立多品种测试中，XGBoost模型对LAI、SPAD和AGB的决定系数（R²）分别为0.8185、0.8758和0.8113。2025年数据集的TDF拟合结果显示，在各生育阶段，产量与综合产量指数（CYI）之间的线性关系R²范围为0.67至0.86，RMSE范围为0.428至0.648 t\u002Fha。在2024年际独立测试中，模型保持了0.7803的R²和0.6076 t\u002Fha的RMSE。此外，在多品种场景下，产量预测的R²为0.6824，RMSE为0.3746 t\u002Fha。所提出的框架提高了基于无人机多时相影像的玉米产量预测的农艺可解释性以及年际和跨品种适用性。","Computers and Electronics in Agriculture","2026-09-10T00:00:00Z",80,{"impact":15,"substance":16,"depth":15,"authority":224,"freshness":100,"relevant":19,"comment":225},14,"提出源端性状中介的无人机多光谱玉米动态产量预测框架，方法新颖、跨年跨品种验证扎实，对精准农业决策有实质参考价值。",[227],{"name":220,"url":217},[24,229,25,27,29],"无人机",{"code":31,"slug":32,"name":27,"path":33,"parent_code":34},[232],{"code":54,"slug":111,"name":112,"path":113,"parent_code":114},{"status":37,"locked":12,"version":38,"method":39,"evidence":234},{"candidates":235,"agricultural_evidence":44},[236,242,246],{"code":54,"score":102,"supported":44,"title":237,"abstract":239,"tags":241},[238],"multispectral",[240,238],"无人机多光谱",[25,29],{"code":31,"score":119,"supported":44,"title":243,"abstract":244,"tags":245},[32],[27,32],[27],{"code":43,"score":19,"supported":12,"title":247,"abstract":248,"tags":249},[],[],[24],[251,203],"产量预测 智慧农业 无人机 玉米","产量预测智慧农业无人机玉米-2117","10.1016\u002Fj.compag.2026.112375",{"doi":253,"openalex_id":255,"authors":256,"venue":220,"cited_by_count":78,"oa_url":217,"card":278,"direction":162,"ingested_from":164},"W7212127922",[257,259,261,263,266,269,271,273,275],{"name":258,"orcid":8},"Chengxin Bai",{"name":260,"orcid":8},"Xiaoyuan Bao",{"name":262,"orcid":8},"Baoyuan Zhang",{"name":264,"orcid":265},"Congcong Guo","https:\u002F\u002Forcid.org\u002F0000-0002-8231-7655",{"name":267,"orcid":268},"Xinying Li","https:\u002F\u002Forcid.org\u002F0000-0002-4256-589X",{"name":270,"orcid":8},"Fuyang Cui",{"name":272,"orcid":8},"Hong Fan",{"name":274,"orcid":8},"Cai Zhao",{"name":276,"orcid":277},"Xiaohe Gu","https:\u002F\u002Forcid.org\u002F0000-0002-7102-1939",{"tldr":279,"method":280,"finding":281,"direction":162,"opportunity":282},"提出基于无人机多光谱的源端性状中介框架，实现玉米动态产量预测。","用LAI、SPAD、AGB作中间变量，构建冠层光谱-性状-产量级联框架，引入时间","框架提升农学可解释性，跨年独立测试R²=0.78，多品种场景R²=0.68。","可探索将源端性状框架扩展至其他作物或结合多源遥感数据，提升跨区域泛化能力。","2026-09-11T23:30:01.789186Z",{"id":285,"title":286,"url":287,"summary":288,"summary_zh":289,"content":8,"source_name":220,"source_url":287,"published_at":290,"category":10,"cover_url":8,"hotness":11,"is_selected":12,"score":222,"score_detail":291,"sources":293,"tags":295,"primary_topic":297,"secondary_topics":298,"classification":299,"search_phrases":318,"slug":321,"view_count":18,"doi":322,"paper":323,"created_at":356},2016,"Transfer learning integrated with SCOPE model for maize yield prediction based on spectral data for different spatial scales","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112381","Transfer learning integrated with SCOPE model for maize yield prediction based on spectral data for different spatial scales。Computers and Electronics in Agriculture","基于光谱数据的不同空间尺度玉米产量预测中，将迁移学习与SCOPE模型相结合","2026-09-09T00:00:00Z",{"impact":15,"substance":16,"depth":15,"authority":224,"freshness":100,"relevant":19,"comment":292},"将迁移学习与SCOPE辐射传输模型结合，实现跨空间尺度光谱数据的玉米产量预测，方法新颖且对遥感估产有实用价值，值得进入每日精选。",[294],{"name":220,"url":287},[24,296,25,27,29],"农业人工智能",{"code":31,"slug":32,"name":27,"path":33,"parent_code":34},[],{"status":37,"locked":12,"version":38,"method":39,"evidence":300},{"candidates":301,"agricultural_evidence":44},[302,306,310,314],{"code":31,"score":119,"supported":44,"title":303,"abstract":304,"tags":305},[32],[27,32],[27],{"code":54,"score":75,"supported":12,"title":307,"abstract":308,"tags":309},[],[],[25,29],{"code":43,"score":19,"supported":12,"title":311,"abstract":312,"tags":313},[],[],[24],{"code":60,"score":19,"supported":12,"title":315,"abstract":316,"tags":317},[],[],[296],[319,320],"农业人工智能 产量预测 智慧农业 玉米","农业人工智能 产量预测","农业人工智能产量预测智慧农业玉米-2016","10.1016\u002Fj.compag.2026.112381",{"doi":322,"openalex_id":324,"authors":325,"venue":220,"cited_by_count":78,"oa_url":8,"card":351,"direction":162,"ingested_from":164},"W7211999297",[326,328,331,334,336,338,340,343,345,347,349],{"name":327,"orcid":8},"Ruomei Zhao",{"name":329,"orcid":330},"Yanling Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-2323-2600",{"name":332,"orcid":333},"Weimin Guo","https:\u002F\u002Forcid.org\u002F0000-0003-4318-7154",{"name":335,"orcid":8},"Wei Liu",{"name":337,"orcid":8},"Long Zhao",{"name":339,"orcid":8},"Xiaoyuan Tian",{"name":341,"orcid":342},"Yelu Zeng","https:\u002F\u002Forcid.org\u002F0000-0003-4267-1841",{"name":344,"orcid":8},"Lulu An",{"name":346,"orcid":8},"Aiguo Wang",{"name":348,"orcid":8},"Qiang Xu",{"name":350,"orcid":8},"Hong Sun",{"tldr":352,"method":353,"finding":354,"direction":162,"opportunity":355},"结合迁移学习与SCOPE模型，利用不同空间尺度光谱数据预测玉米产量。","迁移学习与SCOPE辐射传输模型，多尺度光谱数据。","该方法能跨空间尺度提升玉米产量预测精度。","可探索迁移学习在跨区域、跨年份及多作物产量预测中的泛化能力。","2026-09-10T23:30:01.419872Z",{"id":358,"title":359,"url":360,"summary":361,"summary_zh":8,"content":8,"source_name":362,"source_url":360,"published_at":363,"category":10,"cover_url":8,"hotness":11,"is_selected":12,"score":364,"score_detail":365,"sources":367,"tags":369,"primary_topic":371,"secondary_topics":372,"classification":378,"search_phrases":399,"slug":402,"view_count":75,"doi":403,"paper":404,"created_at":415},1211,"A map-based decision-support framework for variable-depth seeding of fodder maize integrating proximal soil sensing and multi-temporal satellite NDVI data","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs11119-026-10437-3","A map-based decision-support framework for variable-depth seeding of fodder maize integrating proximal soil sensing and multi-temporal satellite NDVI data。Precision Agriculture","Precision Agriculture","2026-08-25T00:00:00Z",70,{"impact":119,"substance":101,"depth":15,"authority":119,"freshness":75,"relevant":19,"comment":366},"论文提出结合土壤传感与卫星NDVI的变量播种决策框架，方法新颖，数据可靠，但时效性较低。",[368],{"name":362,"url":360},[24,27,28,29,370],"土壤传感",{"code":31,"slug":32,"name":27,"path":33,"parent_code":34},[373],{"code":66,"slug":374,"name":375,"path":376,"parent_code":377},"soil","土壤健康与改良","\u002Ftopic\u002Fenvironment\u002Fsoil","H",{"status":37,"locked":12,"version":38,"method":39,"evidence":379},{"candidates":380,"agricultural_evidence":44},[381,385,390,395],{"code":31,"score":49,"supported":44,"title":382,"abstract":383,"tags":384},[32],[32],[27],{"code":66,"score":386,"supported":44,"title":387,"abstract":388,"tags":389},11,[374],[374],[],{"code":43,"score":55,"supported":44,"title":391,"abstract":392,"tags":394},[],[393],"precision agriculture",[24,28],{"code":54,"score":19,"supported":12,"title":396,"abstract":397,"tags":398},[],[],[29],[400,401],"土壤传感 智慧农业 精准农业 玉米","土壤传感 智慧农业","土壤传感智慧农业精准农业玉米-1211","10.1007\u002Fs11119-026-10437-3",{"doi":403,"openalex_id":405,"authors":406,"venue":362,"cited_by_count":78,"oa_url":8,"card":8,"direction":8,"ingested_from":164},"W7204189193",[407,409,412],{"name":408,"orcid":8},"Jialu Sun",{"name":410,"orcid":411},"Manuel Vázquez-Arellano","https:\u002F\u002Forcid.org\u002F0000-0001-7820-5143",{"name":413,"orcid":414},"Abdul Mounem Mouazen","https:\u002F\u002Forcid.org\u002F0000-0002-0354-0067","2026-09-01T04:03:03.736293Z",{"id":417,"title":418,"url":419,"summary":420,"summary_zh":421,"content":8,"source_name":422,"source_url":419,"published_at":423,"category":10,"cover_url":8,"hotness":11,"is_selected":12,"score":173,"score_detail":424,"sources":426,"tags":428,"primary_topic":430,"secondary_topics":431,"classification":436,"search_phrases":455,"slug":458,"view_count":78,"doi":459,"paper":460,"created_at":474},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",{"impact":100,"substance":15,"depth":102,"authority":49,"freshness":124,"relevant":19,"comment":425},"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。",[427],{"name":422,"url":419},[24,296,26,27,429],"耐盐育种",{"code":31,"slug":32,"name":27,"path":33,"parent_code":34},[432],{"code":60,"slug":433,"name":434,"path":435,"parent_code":114},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":37,"locked":12,"version":38,"method":39,"evidence":437},{"candidates":438,"agricultural_evidence":44},[439,446,450],{"code":60,"score":440,"supported":44,"title":441,"abstract":443,"tags":445},19,[442],"machine learning",[26,442,444],"decision support",[296,26],{"code":31,"score":119,"supported":44,"title":447,"abstract":448,"tags":449},[32],[27,32],[27],{"code":43,"score":451,"supported":44,"title":452,"abstract":453,"tags":454},7,[],[28,393],[24],[456,457],"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":459,"openalex_id":461,"authors":462,"venue":422,"cited_by_count":78,"oa_url":419,"card":469,"direction":211,"ingested_from":164},"W7221462188",[463,466],{"name":464,"orcid":465},"Mustafa Turan Arslan","https:\u002F\u002Forcid.org\u002F0000-0001-5498-6571",{"name":467,"orcid":468},"Gülay Zülkadir","https:\u002F\u002Forcid.org\u002F0000-0003-3488-4011",{"tldr":470,"method":471,"finding":472,"direction":211,"opportunity":473},"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"]