[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4168":3,"related-4168":100},{"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":37,"classification":44,"search_phrases":66,"slug":69,"view_count":70,"doi":71,"paper":72,"created_at":99},4168,"High-throughput phenotyping of maize genotype using drone-based multispectral imagery for NDVI estimation","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs12892-026-00392-w","High-throughput phenotyping of maize genotype using drone-based multispectral imagery for NDVI estimation。Journal of Crop Science and Biotechnology","利用基于无人机的多光谱影像对玉米基因型进行高通量表型分析以估算NDVI。《作物科学与生物技术杂志》",null,"Journal of Crop Science and Biotechnology","2026-10-03T00:00:00Z","论文",10,false,67,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},12,18,16,13,8,1,"核心期刊论文，方法具体但属细分技术进展，影响范围有限，适合主题聚合而非每日精选。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","玉米","NDVI","无人机遥感","高通量表型",{"code":33,"slug":34,"name":28,"path":35,"parent_code":36},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[38],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":45,"locked":14,"version":46,"method":47,"evidence":48},"classified","2026-10-22.2","rules",{"candidates":49,"agricultural_evidence":52},[50,56,61],{"code":33,"score":51,"supported":52,"title":53,"abstract":54,"tags":55},15,true,[34],[28,34],[28],{"code":39,"score":20,"supported":52,"title":57,"abstract":59,"tags":60},[58],"multispectral",[58],[29,30],{"code":62,"score":22,"supported":14,"title":63,"abstract":64,"tags":65},"I1",[],[],[27],[67,68],"玉米 无人机 多光谱 NDVI","高通量表型 玉米 基因型","玉米无人机多光谱NDVI-4168",4,"10.1007\u002Fs12892-026-00392-w",{"doi":71,"openalex_id":73,"authors":74,"venue":10,"cited_by_count":91,"oa_url":9,"card":92,"direction":96,"ingested_from":98},"W7218576117",[75,78,81,84,87,89],{"name":76,"orcid":77},"Nining Nurini Andayani","https:\u002F\u002Forcid.org\u002F0000-0002-4069-1307",{"name":79,"orcid":80},"Muhammad Azrai","https:\u002F\u002Forcid.org\u002F0000-0002-5546-9485",{"name":82,"orcid":83},"Roy Efendi","https:\u002F\u002Forcid.org\u002F0000-0002-6515-1198",{"name":85,"orcid":86},"Muhammad Aqil","https:\u002F\u002Forcid.org\u002F0000-0002-1842-9230",{"name":88,"orcid":9},"Dwitari Lestary",{"name":90,"orcid":9},"Haikal Akbar",0,{"tldr":93,"method":94,"finding":95,"direction":96,"opportunity":97},"利用无人机多光谱影像对玉米基因型进行高通量表型分析并估算NDVI。","无人机多光谱成像与NDVI植被指数估算。","无人机多光谱影像可高效获取玉米基因型NDVI表型数据。","农业遥感与作物表型","可探索多光谱NDVI与产量基因型关联，构建高通量表型育种决策模型。","openalex","2026-10-04T23:30:18.734738Z",{"total":101,"page":22,"page_size":101,"items":102},6,[103,172,248,317,385,480],{"id":104,"title":105,"url":106,"summary":107,"summary_zh":108,"content":9,"source_name":109,"source_url":106,"published_at":110,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":111,"score_detail":112,"sources":115,"tags":117,"primary_topic":120,"secondary_topics":121,"classification":123,"search_phrases":147,"slug":150,"view_count":151,"doi":152,"paper":153,"created_at":171},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":21,"substance":113,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":114},20,"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。",[116],{"name":109,"url":106},[27,118,28,119,29],"产量预测","遥感",{"code":33,"slug":34,"name":28,"path":35,"parent_code":36},[122],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":124},{"candidates":125,"agricultural_evidence":52},[126,130,136,143],{"code":33,"score":51,"supported":52,"title":127,"abstract":128,"tags":129},[34],[28,34],[28],{"code":39,"score":131,"supported":52,"title":132,"abstract":133,"tags":135},9,[],[119,134],"remote sensing",[29,118,119],{"code":137,"score":101,"supported":52,"title":138,"abstract":139,"tags":142},"A8",[],[140,141],"粮食安全","grain security",[],{"code":62,"score":22,"supported":14,"title":144,"abstract":145,"tags":146},[],[],[27],[148,149],"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",3,"10.3329\u002Fbjar.v51i1.92530",{"doi":152,"openalex_id":154,"authors":155,"venue":109,"cited_by_count":91,"oa_url":106,"card":166,"direction":96,"ingested_from":98},"W7213918280",[156,158,160,162,164],{"name":157,"orcid":9},"N Mohammad",{"name":159,"orcid":9},"MA Islam",{"name":161,"orcid":9},"MG Mahboob",{"name":163,"orcid":9},"MM Rahman",{"name":165,"orcid":9},"I Ahmed",{"tldr":167,"method":168,"finding":169,"direction":96,"opportunity":170},"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%。","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。","2026-09-22T23:30:23.551878Z",{"id":173,"title":174,"url":175,"summary":176,"summary_zh":177,"content":9,"source_name":178,"source_url":175,"published_at":179,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":180,"score_detail":181,"sources":185,"tags":187,"primary_topic":190,"secondary_topics":191,"classification":193,"search_phrases":213,"slug":216,"view_count":217,"doi":218,"paper":219,"created_at":247},3973,"A physical-context-conditioned multimodal fusion network for estimating maize canopy nitrogen content using multi-source UAV remote sensing data","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112465","A physical-context-conditioned multimodal fusion network for estimating maize canopy nitrogen content using multi-source UAV remote sensing data。Computers and Electronics in Agriculture","一种基于物理情境条件调节的多模态融合网络，用于利用多源无人机遥感数据估算玉米冠层氮含量。《计算机与电子农业》","Computers and Electronics in Agriculture","2026-10-01T00:00:00Z",81,{"impact":18,"substance":182,"depth":18,"authority":183,"freshness":131,"relevant":22,"comment":184},22,14,"多源无人机遥感与物理上下文多模态融合估算玉米冠层氮素，方法新颖、数据可靠，对精准施肥有参考价值。",[186],{"name":178,"url":175},[27,188,28,119,189],"农业人工智能","氮素营养",{"code":33,"slug":34,"name":28,"path":35,"parent_code":36},[192],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":194},{"candidates":195,"agricultural_evidence":52},[196,200,204,208],{"code":33,"score":51,"supported":52,"title":197,"abstract":198,"tags":199},[34],[28,34],[28],{"code":39,"score":51,"supported":52,"title":201,"abstract":202,"tags":203},[134],[119,134],[119],{"code":62,"score":22,"supported":14,"title":205,"abstract":206,"tags":207},[],[],[27],{"code":209,"score":22,"supported":14,"title":210,"abstract":211,"tags":212},"I2",[],[],[188],[214,215],"无人机 遥感 玉米 氮含量","多模态融合 冠层氮素","无人机遥感玉米氮含量-3973",2,"10.1016\u002Fj.compag.2026.112465",{"doi":218,"openalex_id":220,"authors":221,"venue":178,"cited_by_count":91,"oa_url":9,"card":242,"direction":96,"ingested_from":98},"W7215084428",[222,224,226,229,232,234,236,239],{"name":223,"orcid":9},"Wang Baoju",{"name":225,"orcid":9},"Jing Zhao",{"name":227,"orcid":228},"Yu Yan","https:\u002F\u002Forcid.org\u002F0009-0004-4202-6935",{"name":230,"orcid":231},"Junke Zhu","https:\u002F\u002Forcid.org\u002F0009-0005-7778-9614",{"name":233,"orcid":9},"Xinlong Li",{"name":235,"orcid":9},"Huizheng Wang",{"name":237,"orcid":238},"Weiguang Yang","https:\u002F\u002Forcid.org\u002F0000-0002-9862-5839",{"name":240,"orcid":241},"Yubin Lan","https:\u002F\u002Forcid.org\u002F0000-0001-6664-5571",{"tldr":243,"method":244,"finding":245,"direction":96,"opportunity":246},"提出物理上下文条件化多模态融合网络，用多源无人机遥感估算玉米冠层氮含量。","多源无人机遥感数据与物理上下文条件化多模态融合网络。","该网络能提升玉米冠层氮含量估算精度，融合多源数据优于单一模态。","可探索物理先验与多模态融合在不同作物、生育期及跨区域迁移中的泛化能力。","2026-10-02T23:30:02.413294Z",{"id":249,"title":250,"url":251,"summary":252,"summary_zh":253,"content":9,"source_name":178,"source_url":251,"published_at":254,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":255,"score_detail":256,"sources":258,"tags":260,"primary_topic":262,"secondary_topics":263,"classification":265,"search_phrases":281,"slug":284,"view_count":285,"doi":286,"paper":287,"created_at":316},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。","2026-09-10T00:00:00Z",80,{"impact":18,"substance":182,"depth":18,"authority":183,"freshness":21,"relevant":22,"comment":257},"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。",[259],{"name":178,"url":251},[27,261,118,28,119],"无人机",{"code":33,"slug":34,"name":28,"path":35,"parent_code":36},[264],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":266},{"candidates":267,"agricultural_evidence":52},[268,273,277],{"code":39,"score":19,"supported":52,"title":269,"abstract":270,"tags":272},[58],[271,58],"无人机多光谱",[118,119],{"code":33,"score":51,"supported":52,"title":274,"abstract":275,"tags":276},[34],[28,34],[28],{"code":62,"score":22,"supported":14,"title":278,"abstract":279,"tags":280},[],[],[27],[282,283],"产量预测 智慧农业 无人机 玉米","产量预测 智慧农业","产量预测智慧农业无人机玉米-2117",5,"10.1016\u002Fj.compag.2026.112375",{"doi":286,"openalex_id":288,"authors":289,"venue":178,"cited_by_count":91,"oa_url":251,"card":311,"direction":96,"ingested_from":98},"W7212127922",[290,292,294,296,299,302,304,306,308],{"name":291,"orcid":9},"Chengxin Bai",{"name":293,"orcid":9},"Xiaoyuan Bao",{"name":295,"orcid":9},"Baoyuan Zhang",{"name":297,"orcid":298},"Congcong Guo","https:\u002F\u002Forcid.org\u002F0000-0002-8231-7655",{"name":300,"orcid":301},"Xinying Li","https:\u002F\u002Forcid.org\u002F0000-0002-4256-589X",{"name":303,"orcid":9},"Fuyang Cui",{"name":305,"orcid":9},"Hong Fan",{"name":307,"orcid":9},"Cai Zhao",{"name":309,"orcid":310},"Xiaohe Gu","https:\u002F\u002Forcid.org\u002F0000-0002-7102-1939",{"tldr":312,"method":313,"finding":314,"direction":96,"opportunity":315},"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":318,"title":319,"url":320,"summary":321,"summary_zh":322,"content":9,"source_name":323,"source_url":320,"published_at":324,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":325,"score_detail":326,"sources":328,"tags":330,"primary_topic":332,"secondary_topics":333,"classification":339,"search_phrases":360,"slug":363,"view_count":151,"doi":364,"paper":365,"created_at":384},2058,"Machine Learning-Based Prediction of Fall Armyworm (Spodoptera frugiperda) Outbreaks in Maize Production Systems of Northern Nigeria Using Climate and Remote Sensing Data","https:\u002F\u002Fdoi.org\u002F10.58578\u002Fkijst.v3i3.12026","Fall Armyworm (Spodoptera frugiperda) poses a substantial threat to maize production across sub-Saharan Africa, particularly in Nigeria, where climatic variability intensifies the risk of pest outbreaks. This study developed and evaluated machine learning models for predicting Fall Armyworm outbreaks in northern Nigeria using integrated climate and remote sensing variables. A retrospective modeling framework was applied to simulated but biologically constrained datasets covering a six-year period (2019–2024) and comprising temperature, rainfall, relative humidity, Normalized Difference Vegetation Index (NDVI), and vegetation condition indices. Three supervised learning algorithms—Random Forest, Support Vector Machine, and Gradient Boosting—were trained and validated using five-fold cross-validation. Gradient Boosting achieved the highest predictive accuracy at 92.8%, followed by Random Forest at 89.2% and Support Vector Machine at 84.6%. Temperature, relative humidity, and NDVI emerged as the most influential predictors of outbreak occurrence, while the integration of satellite-derived vegetation indices improved overall model performance. These findings demonstrate the potential of combining machine learning with remote sensing data to develop scalable and cost-effective early warning systems for agricultural pest management. However, because the models were developed using simulated data, their predictive validity requires confirmation using field-collected observational data. The proposed framework contributes to data-driven pest surveillance by providing a basis for anticipating outbreaks and supporting timely decision-making in maize production systems.","草地贪夜蛾（Spodoptera frugiperda）对撒哈拉以南非洲的玉米生产构成重大威胁，在尼日利亚尤为突出，因为气候变率加剧了害虫暴发的风险。本研究开发并评估了机器学习模型，利用气候与遥感综合变量预测尼日利亚北部草地贪夜蛾的暴发。研究采用回顾性建模框架，基于模拟但受生物学约束的数据集，覆盖六年时间（2019—2024年），包括温度、降雨量、相对湿度、归一化植被指数（NDVI）及植被状况指数。三种监督学习算法——随机森林、支持向量机和梯度提升——通过五折交叉验证进行训练与验证。梯度提升取得了最高的预测准确率，达92.8%，其次为随机森林（89.2%）和支持向量机（84.6%）。温度、相对湿度和NDVI是暴发发生最具影响力的预测因子，而卫星衍生植被指数的整合提升了模型的整体性能。这些发现表明，将机器学习与遥感数据相结合，有望开发可扩展且成本效益高的农业害虫管理预警系统。然而，由于模型基于模拟数据开发，其预测效度仍需利用实地观测数据加以验证。所提出的框架为数据驱动的害虫监测提供了依据，有助于预判暴发并支持玉米生产系统中的及时决策。","Kwaghe International Journal of Sciences and Technology","2026-09-09T00:00:00Z",75,{"impact":18,"substance":113,"depth":19,"authority":17,"freshness":131,"relevant":22,"comment":327},"将机器学习与气候及遥感数据结合用于尼日利亚北部玉米草地贪夜蛾暴发预测，方法框架有参考价值，但基于模拟数据、结论可靠性待田间验证，属细分领域研究进展。",[329],{"name":323,"url":320},[27,188,28,119,331],"病虫害预警",{"code":33,"slug":34,"name":28,"path":35,"parent_code":36},[334,338],{"code":209,"slug":335,"name":336,"path":337,"parent_code":43},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":340},{"candidates":341,"agricultural_evidence":52},[342,346,352,356],{"code":33,"score":51,"supported":52,"title":343,"abstract":344,"tags":345},[34],[28,34],[28],{"code":209,"score":51,"supported":52,"title":347,"abstract":349,"tags":351},[348],"machine learning",[350,348],"机器学习",[188],{"code":39,"score":51,"supported":52,"title":353,"abstract":354,"tags":355},[134],[119,134],[119],{"code":62,"score":22,"supported":14,"title":357,"abstract":358,"tags":359},[],[],[27],[361,362],"农业人工智能 病虫害预警 智慧农业 玉米","农业人工智能 病虫害预警","农业人工智能病虫害预警智慧农业玉米-2058","10.58578\u002Fkijst.v3i3.12026",{"doi":364,"openalex_id":366,"authors":367,"venue":323,"cited_by_count":91,"oa_url":9,"card":378,"direction":96,"ingested_from":98},"W7212022953",[368,370,372,374,376],{"name":369,"orcid":9},"Okwor Jude I.",{"name":371,"orcid":9},"Attamah Chinyere G.",{"name":373,"orcid":9},"Uchendu Christian N.",{"name":375,"orcid":9},"Onyima John O.",{"name":377,"orcid":9},"Idoko Bartholomew",{"tldr":379,"method":380,"finding":381,"direction":382,"opportunity":383},"用气候与遥感数据构建机器学习模型预测尼日利亚北部玉米草地贪夜蛾暴发。","随机森林、SVM、梯度提升，结合温度、降雨、湿度、NDVI等模拟数据，五折交叉验","梯度提升准确率最高达92.8%，温度、相对湿度和NDVI是最关键预测因子。","农业人工智能与决策模型","模型基于模拟数据，亟需用田间实测数据验证，并开发可扩展的实时早期预警系统。","2026-09-10T23:30:27.820508Z",{"id":386,"title":387,"url":388,"summary":389,"summary_zh":390,"content":9,"source_name":391,"source_url":388,"published_at":392,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":393,"score_detail":394,"sources":396,"tags":398,"primary_topic":401,"secondary_topics":402,"classification":404,"search_phrases":449,"slug":452,"view_count":22,"doi":453,"paper":454,"created_at":479},1902,"Improving Maize Yield Estimation Accuracy by Integrating Satellite Remote Sensing Data and the WOFOST Model Through Data Assimilation","https:\u002F\u002Fdoi.org\u002F10.3390\u002Fagronomy16171742","Under intensified climate change, improving yield estimation accuracy is essential for food security and precision agricultural management. This research aimed to enhance summer maize yield estimation accuracy. GF-1 imagery was used to derive vegetation indices for leaf area index (LAI) modeling; vegetation index–LAI models used partial least squares regression (PLSR), random forest (RF), extreme gradient boosting (XGBoost), support vector regression (SVR), convolutional neural network (CNN), and categorical feature-enhanced gradient boosting (CatBoost). Retrieved summer maize LAI and measured soil moisture (SM) were assimilated into a calibrated World Food Studies (WOFOST) model using ensemble Kalman filtering (EnKF) and four-dimensional variational assimilation (4D-Var). The influence of ensemble size and assimilation period on the accuracy of yield simulations was analyzed. Results showed that all machine learning models effectively retrieved LAI, with CNN achieving the highest accuracy under the evaluation conditions of this study. Compared with the non-assimilation scenario, data assimilation significantly improved WOFOST-based maize yield estimation; EnKF performed best for yield estimation, whereas 4D-Var yielded slightly lower LAI simulation errors than EnKF under the experimental conditions of this study. Among the ensemble sizes evaluated, an ensemble size of 100 produced the lowest yield prediction error, while assimilation during the tasseling-to-grain-filling period resulted in lower yield prediction errors than continuous whole-growth-period assimilation under the conditions examined. Overall, data assimilation effectively improved WOFOST-based maize yield estimation and supported regional crop monitoring and precision agricultural management.","在气候变化加剧的背景下，提高产量估算精度对于粮食安全和精准农业管理至关重要。本研究旨在提升夏玉米产量估算精度。利用GF-1影像提取植被指数用于叶面积指数（LAI）建模；植被指数-LAI模型采用偏最小二乘回归（PLSR）、随机森林（RF）、极端梯度提升（XGBoost）、支持向量回归（SVR）、卷积神经网络（CNN）和类别特征增强梯度提升（CatBoost）等方法。将反演的夏玉米LAI及实测土壤水分（SM）通过集合卡尔曼滤波（EnKF）和四维变分同化（4D-Var）同化到校准后的世界粮食研究（WOFOST）模型中。分析了集合规模和同化时段对产量模拟精度的影响。结果表明，所有机器学习模型均能有效反演LAI，其中在本研究评价条件下CNN取得了最高精度。与未同化情景相比，数据同化显著提高了基于WOFOST的玉米产量估算精度；EnKF在产量估算中表现最佳，而4D-Var在本研究试验条件下产生的LAI模拟误差略低于EnKF。在评估的集合规模中，集合规模为100时产量预测误差最低，而在本研究所考察条件下，抽雄至灌浆期的同化比全生育期连续同化产生了更低的产量预测误差。总体而言，数据同化有效提高了基于WOFOST的玉米产量估算精度，并支持区域作物监测和精准农业管理。","Agronomy","2026-09-07T00:00:00Z",71,{"impact":18,"substance":113,"depth":18,"authority":17,"freshness":151,"relevant":22,"comment":395},"研究结合遥感与WOFOST模型提升玉米产量估测精度，方法新颖且结论可靠，对精准农业有参考价值。",[397],{"name":391,"url":388},[27,350,119,399,400],"产量估测","数据同化",{"code":33,"slug":34,"name":28,"path":35,"parent_code":36},[403],{"code":39,"slug":40,"name":41,"path":42,"parent_code":43},{"status":45,"locked":14,"version":46,"method":47,"evidence":405},{"candidates":406,"agricultural_evidence":52},[407,411,415,420,425,432,439,444],{"code":33,"score":183,"supported":52,"title":408,"abstract":409,"tags":410},[34],[28,34],[],{"code":39,"score":13,"supported":52,"title":412,"abstract":413,"tags":414},[134],[],[399,119],{"code":209,"score":416,"supported":52,"title":417,"abstract":418,"tags":419},7,[],[350,348],[350],{"code":137,"score":101,"supported":52,"title":421,"abstract":422,"tags":424},[],[140,423],"food security",[],{"code":426,"score":101,"supported":52,"title":427,"abstract":428,"tags":431},"H2",[],[429,430],"土壤","soil",[],{"code":433,"score":101,"supported":52,"title":434,"abstract":435,"tags":438},"H7",[],[436,437],"气候变化","climate change",[],{"code":62,"score":70,"supported":52,"title":440,"abstract":441,"tags":443},[],[442],"精准农业",[27],{"code":445,"score":22,"supported":14,"title":446,"abstract":447,"tags":448},"I5",[],[],[400],[450,451],"产量估测 数据同化 智慧农业 机器学习","产量估测 数据同化","产量估测数据同化智慧农业机器学习-1902","10.3390\u002Fagronomy16171742",{"doi":453,"openalex_id":455,"authors":456,"venue":391,"cited_by_count":91,"oa_url":388,"card":474,"direction":96,"ingested_from":98},"W7211913464",[457,460,463,465,467,469,471],{"name":458,"orcid":459},"Xuqing Li","https:\u002F\u002Forcid.org\u002F0009-0009-4215-6701",{"name":461,"orcid":462},"Zekun Zhang","https:\u002F\u002Forcid.org\u002F0009-0003-8014-0707",{"name":464,"orcid":9},"Zhihe Hu",{"name":466,"orcid":9},"Ligang Cui",{"name":468,"orcid":9},"Long Li",{"name":470,"orcid":9},"Tingxuan Wang",{"name":472,"orcid":473},"Tian Yang","https:\u002F\u002Forcid.org\u002F0000-0001-6935-4147",{"tldr":475,"method":476,"finding":477,"direction":96,"opportunity":478},"通过数据同化整合卫星遥感与WOFOST模型，提高夏玉米产量估算精度。","GF-1影像反演LAI，结合土壤水分，用EnKF和4D-Var同化进WOFOST","CNN反演LAI最准，EnKF同化产量估算最优，集合大小100最佳，抽雄至灌浆期同化效果最好。","可探索不同作物、区域及同化策略的泛化性，或结合多源遥感与深度学习改进LAI反演及同化效率。","2026-09-08T23:30:19.053510Z",{"id":481,"title":482,"url":483,"summary":484,"summary_zh":485,"content":9,"source_name":178,"source_url":483,"published_at":486,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":180,"score_detail":487,"sources":489,"tags":491,"primary_topic":492,"secondary_topics":493,"classification":494,"search_phrases":514,"slug":517,"view_count":70,"doi":518,"paper":519,"created_at":552},3006,"Explainable spectral–image fusion multi-task learning for maize canopy biochemical and structural trait retrieval","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112450","Accurate and interpretable estimation of multiple maize canopy traits is important for proximal crop monitoring and high-throughput phenotyping. This study developed a spectral–image fusion multi-task learning framework (MM-MTL) for the simultaneous retrieval of chlorophyll index (Chl index), leaf area index (LAI), nitrogen balance index (NBI), and anthocyanin index (Anth index). Proximal hyperspectral observations were collected using a Specim IQ camera covering 400–1000 nm with 204 spectral bands. For each observation, an ROI-mean spectral vector was extracted to retain fine-grained canopy reflectance information, while a co-registered pseudo-RGB image derived from visible bands of the same hyperspectral cube preserved two-dimensional canopy structure and visible appearance. MM-MTL integrates spectral and image feature extraction, task-specific fusion, and uncertainty-weighted multi-task learning to jointly estimate the four traits. A total of 1,023 valid samples collected from nine field campaigns across the 2024 and 2025 growing seasons were used for model development and evaluation. Under plot-grouped five-fold cross-validation, MM-MTL achieved R 2 values of 0.872, 0.885, 0.722, and 0.807 for Chl index, LAI, NBI, and Anth index, respectively, and consistently outperformed the single-task, single-representation, and conventional regression baselines. Performance decreased under more challenging generalization settings, with R 2 values ranging from 0.543 to 0.680 under leave-one-campaign-out validation and from 0.425 to 0.640 under bidirectional cross-year validation. Ablation and post-hoc analyses further showed that preserving image spatial organization improved prediction, while task-wise routing, input-representation masking, wavelength perturbation, and cross-fold stability analyses revealed trait-dependent use of spectral and spatial information. These results demonstrate that complementary spectral and spatial representations derived from the same hyperspectral observation can improve multi-trait maize canopy retrieval, while transfer across acquisition campaigns, years, and field environments remains an important direction for further improvement.","准确且可解释地估算多个玉米冠层性状，对于近地作物监测和高通量表型分析具有重要意义。本研究开发了一种光谱—图像融合多任务学习框架（MM-MTL），用于同时反演叶绿素指数（Chl index）、叶面积指数（LAI）、氮平衡指数（NBI）和花青素指数（Anth index）。近地高光谱观测使用Specim IQ相机采集，覆盖400–1000 nm，共204个光谱波段。对于每次观测，提取ROI均值光谱向量以保留细粒度冠层反射率信息，同时从同一高光谱立方体的可见光波段生成配准的伪RGB图像，以保留二维冠层结构和可见外观。MM-MTL集成了光谱与图像特征提取、任务特定融合以及不确定性加权多任务学习，以联合估算这四种性状。研究使用2024年和2025年生长季9次田间试验采集的共计1,023个有效样本进行模型开发与评估。在按小区分组的五折交叉验证下，MM-MTL对Chl index、LAI、NBI和Anth index的R²分别为0.872、0.885、0.722和0.807，且持续优于单任务、单表征和传统回归基线。在更具挑战性的泛化设置下，模型性能有所下降，留一试验验证的R²范围为0.543–0.680，双向跨年验证的R²范围为0.425–0.640。消融分析和事后分析进一步表明，保留图像空间组织可提升预测性能，而任务路由、输入表征掩蔽、波长扰动和跨折稳定性分析揭示了光谱与空间信息的性状依赖性利用方式。这些结果表明，从同一高光谱观测中提取的互补光谱与空间表征可改善多性状玉米冠层反演，而跨采集试验、年份和田间环境的迁移仍是未来改进的重要方向。","2026-09-19T00:00:00Z",{"impact":18,"substance":182,"depth":18,"authority":183,"freshness":131,"relevant":22,"comment":488},"提出光谱-图像融合多任务学习框架，1023份样本跨两年验证，方法新颖且结论可靠，对作物高通量表型研究有实质参考价值。",[490],{"name":178,"url":483},[27,188,28,119,31],{"code":33,"slug":34,"name":28,"path":35,"parent_code":36},[],{"status":45,"locked":14,"version":46,"method":47,"evidence":495},{"candidates":496,"agricultural_evidence":52},[497,501,506,510],{"code":33,"score":51,"supported":52,"title":498,"abstract":499,"tags":500},[34],[28,34],[28],{"code":39,"score":70,"supported":52,"title":502,"abstract":503,"tags":505},[],[504],"hyperspectral",[119],{"code":62,"score":22,"supported":14,"title":507,"abstract":508,"tags":509},[],[],[27],{"code":209,"score":22,"supported":14,"title":511,"abstract":512,"tags":513},[],[],[188],[515,516],"玉米冠层 多任务学习 高光谱","Specim IQ 玉米 表型","玉米冠层多任务学习高光谱-3006","10.1016\u002Fj.compag.2026.112450",{"doi":518,"openalex_id":520,"authors":521,"venue":178,"cited_by_count":91,"oa_url":483,"card":547,"direction":96,"ingested_from":98},"W7213658705",[522,524,526,528,530,533,535,538,540,542,544],{"name":523,"orcid":9},"Penglei Zhang",{"name":525,"orcid":9},"Tianbo Hao",{"name":527,"orcid":9},"Zhuoyuan Zhao",{"name":529,"orcid":9},"Hong Sun",{"name":531,"orcid":532},"Yelu Zeng","https:\u002F\u002Forcid.org\u002F0000-0003-4267-1841",{"name":534,"orcid":9},"Zheng Cui",{"name":536,"orcid":537},"Ta Na","https:\u002F\u002Forcid.org\u002F0000-0002-1348-5655",{"name":539,"orcid":9},"Lang Qiao",{"name":541,"orcid":9},"Durval Dourado Neto",{"name":543,"orcid":9},"Feng Yang",{"name":545,"orcid":546},"Jingzhu Wu","https:\u002F\u002Forcid.org\u002F0000-0002-8386-1038",{"tldr":548,"method":549,"finding":550,"direction":96,"opportunity":551},"提出光谱-图像融合多任务学习框架，同时反演玉米冠层四个生化与结构性状。","Specim IQ高光谱与伪RGB融合，不确定性加权多任务学习，1023样本交叉","融合模型精度优于单任务基线，但跨年份与跨环境泛化性能明显下降。","可研究跨年份\u002F跨环境域适应与迁移学习，提升多性状反演泛化能力。","2026-09-20T23:30:01.739142Z"]