[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-1276":3,"related-1276":120},{"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":38,"search_phrases":65,"slug":68,"view_count":69,"doi":70,"paper":71,"created_at":119},1276,"Integrating UAV-derived enhanced disease detection index and texture features for monitoring southern corn rust severity","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112336","Integrating UAV-derived enhanced disease detection index and texture features for monitoring southern corn rust severity。Computers and Electronics in Agriculture","利用无人机获取的增强型病害检测指数与纹理特征集成监测南方玉米锈病严重度。《计算机与电子在农业中的应用》",null,"Computers and Electronics in Agriculture","2026-08-31T00:00:00Z","论文",10,false,69,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,20,18,14,2,1,"利用无人机影像与纹理特征监测玉米南方锈病，方法新颖，数据详实，对精准农业有参考价值。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","玉米","遥感","病害监测",{"code":33,"slug":34,"name":29,"path":35,"parent_code":36},"A3","maize","\u002Ftopic\u002Ffield-crops\u002Fmaize","A",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":45},[44,50,55,60],{"code":33,"score":17,"supported":45,"title":46,"abstract":48,"tags":49},true,[47],"corn",[29,47],[29],{"code":51,"score":22,"supported":14,"title":52,"abstract":53,"tags":54},"I1",[],[],[27],{"code":56,"score":22,"supported":14,"title":57,"abstract":58,"tags":59},"I2",[],[],[28],{"code":61,"score":22,"supported":14,"title":62,"abstract":63,"tags":64},"I3",[],[],[30],[66,67],"农业人工智能 智慧农业 病害监测 玉米","农业人工智能 智慧农业","农业人工智能智慧农业病害监测玉米-1276",4,"10.1016\u002Fj.compag.2026.112336",{"doi":70,"openalex_id":72,"authors":73,"venue":10,"cited_by_count":111,"oa_url":9,"card":112,"direction":116,"ingested_from":118},"W7204748765",[74,76,78,81,84,87,89,91,93,95,97,99,101,103,106,108],{"name":75,"orcid":9},"Yanan Wei",{"name":77,"orcid":9},"Qing Li",{"name":79,"orcid":80},"Jia Yin","https:\u002F\u002Forcid.org\u002F0000-0002-6242-1092",{"name":82,"orcid":83},"Dalei Hao","https:\u002F\u002Forcid.org\u002F0000-0003-3497-9774",{"name":85,"orcid":86},"Renan Caldas Umburanas","https:\u002F\u002Forcid.org\u002F0000-0002-4112-3598",{"name":88,"orcid":9},"Yongyuan Gao",{"name":90,"orcid":9},"Weijian Yu",{"name":92,"orcid":9},"Zhixiong Li",{"name":94,"orcid":9},"Yachang He",{"name":96,"orcid":9},"Guanyu Qiao",{"name":98,"orcid":9},"Yu Zhang",{"name":100,"orcid":9},"Carlos Camino",{"name":102,"orcid":9},"Jun Liu",{"name":104,"orcid":105},"Tianyi Wang","https:\u002F\u002Forcid.org\u002F0000-0003-3032-1246",{"name":107,"orcid":9},"Hong Sun",{"name":109,"orcid":110},"Yelu Zeng","https:\u002F\u002Forcid.org\u002F0000-0003-4267-1841",0,{"tldr":113,"method":114,"finding":115,"direction":116,"opportunity":117},"结合无人机增强病害检测指数与纹理特征，监测南方玉米锈病严重度。","无人机影像，增强病害检测指数与纹理特征融合，机器学习建模。","融合指数与纹理特征可提高玉米锈病严重度监测精度。","农业遥感与作物表型","可探索多时相、多传感器融合及深度学习，提升病害早期监测与预测能力。","openalex","2026-09-01T23:30:01.368713Z",{"total":121,"page":22,"page_size":121,"items":122},6,[123,200,289,360,428,499],{"id":124,"title":125,"url":126,"summary":127,"summary_zh":128,"content":9,"source_name":10,"source_url":126,"published_at":129,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":130,"score_detail":131,"sources":135,"tags":137,"primary_topic":139,"secondary_topics":140,"classification":146,"search_phrases":166,"slug":169,"view_count":21,"doi":170,"paper":171,"created_at":199},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》","2026-10-01T00:00:00Z",81,{"impact":19,"substance":132,"depth":19,"authority":20,"freshness":133,"relevant":22,"comment":134},22,9,"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。",[136],{"name":10,"url":126},[27,28,29,30,138],"氮素营养",{"code":33,"slug":34,"name":29,"path":35,"parent_code":36},[141],{"code":61,"slug":142,"name":143,"path":144,"parent_code":145},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"status":39,"locked":14,"version":40,"method":41,"evidence":147},{"candidates":148,"agricultural_evidence":45},[149,153,158,162],{"code":33,"score":17,"supported":45,"title":150,"abstract":151,"tags":152},[34],[29,34],[29],{"code":61,"score":17,"supported":45,"title":154,"abstract":156,"tags":157},[155],"remote sensing",[30,155],[30],{"code":51,"score":22,"supported":14,"title":159,"abstract":160,"tags":161},[],[],[27],{"code":56,"score":22,"supported":14,"title":163,"abstract":164,"tags":165},[],[],[28],[167,168],"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","10.1016\u002Fj.compag.2026.112465",{"doi":170,"openalex_id":172,"authors":173,"venue":10,"cited_by_count":111,"oa_url":9,"card":194,"direction":116,"ingested_from":118},"W7215084428",[174,176,178,181,184,186,188,191],{"name":175,"orcid":9},"Wang Baoju",{"name":177,"orcid":9},"Jing Zhao",{"name":179,"orcid":180},"Yu Yan","https:\u002F\u002Forcid.org\u002F0009-0004-4202-6935",{"name":182,"orcid":183},"Junke Zhu","https:\u002F\u002Forcid.org\u002F0009-0005-7778-9614",{"name":185,"orcid":9},"Xinlong Li",{"name":187,"orcid":9},"Huizheng Wang",{"name":189,"orcid":190},"Weiguang Yang","https:\u002F\u002Forcid.org\u002F0000-0002-9862-5839",{"name":192,"orcid":193},"Yubin Lan","https:\u002F\u002Forcid.org\u002F0000-0001-6664-5571",{"tldr":195,"method":196,"finding":197,"direction":116,"opportunity":198},"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":201,"title":202,"url":203,"summary":204,"summary_zh":205,"content":9,"source_name":206,"source_url":203,"published_at":207,"category":12,"cover_url":9,"hotness":13,"is_selected":45,"score":130,"score_detail":208,"sources":210,"tags":212,"primary_topic":214,"secondary_topics":215,"classification":220,"search_phrases":250,"slug":253,"view_count":254,"doi":255,"paper":256,"created_at":288},3842,"Raspberry Pi Lightweight Deep Learning Models Deployed for Corn Growth Stage Classification Using PhenoCam Images","https:\u002F\u002Fdoi.org\u002F10.3390\u002Frs18193337","Identifying crop growth stages is essential for comprehensive assessment that facilitates timely and precise agronomic management. However, conventional field-based manual scouting is labor-intensive, time-consuming, and lacks scalability. Digital field images with advanced models deployed on portable edge-computing devices offer an alternative solution for real-time crop monitoring and decision support. Therefore, in this study, the standard 8 corn growth stages were defined as 8 classes based on distinct visual canopy appearance patterns observed in PhenoCam (near-surface [proximal] remote sensing network camera) imagery collected from 10 PhenoCam sites. These classes do not correspond directly to standard agronomic vegetation and reproductive growth stages. For modeling, four lightweight deep learning (DL) models, ELiteCrop0, ELiteCrop1, ELiteCrop4, and MobNetCropV2, were developed and evaluated across five image vertical clipping levels (0–40%) using a supercomputer (NDSU CCAST). The optimized model was subsequently deployed on a single-board Raspberry Pi 5 computer for edge inference. Model training accounted for the majority of the total CPU time, exceeding 97%, while the testing times ranged from 0.01–0.12 min, enabling real-time applications. Among the models, ELiteCrop0 achieved the most balanced performance with a confusion-matrix diagonal ratio (CMDR) of 0.93, followed by ELiteCrop1 (CMDR = 0.92). Overall, model performance decreased with increasing vertical clipping; therefore, a moderate image clipping (0–10%) was recommended for improved computational efficiency. Analysis with a supercomputer produced an intrasite (same training sites) accuracy of 0.90–0.93 (Raspberry Pi: 0.78–0.81) and an intersite (unseen test sites) accuracy of 0.48–0.50 (Raspberry Pi: 0.41–0.43), indicating challenges with model generalization. Raspberry Pi processed ≈ 1000 images\u002Fmin at 68 °C, with a PhenoCam:Raspberry Pi processing ratio of 1:2500 at 50% capacity over an 8 h day. Overall, this study presents a scalable and cost-effective solution for real-time corn growth stage monitoring in precision agriculture, which could be extended to other crops.","识别作物生长阶段对于全面评估、促进及时精准的农艺管理至关重要。然而，传统的田间人工巡查劳动强度大、耗时且缺乏可扩展性。部署在便携式边缘计算设备上的数字田间图像与先进模型为实时作物监测和决策支持提供了替代方案。因此，本研究基于从10个PhenoCam（近地面[近端]遥感网络相机）站点采集的影像中观察到的不同冠层视觉外观模式，将标准的8个玉米生长阶段定义为8个类别。这些类别并不直接对应于标准的农艺营养生长和生殖生长阶段。在建模方面，开发了四种轻量级深度学习（DL）模型——ELiteCrop0、ELiteCrop1、ELiteCrop4和MobNetCropV2，并利用超级计算机（NDSU CCAST）在五种图像垂直裁剪水平（0–40%）下进行了评估。随后，优化后的模型被部署到单板Raspberry Pi 5计算机上进行边缘推理。模型训练占总CPU时间的大部分，超过97%，而测试时间为0.01–0.12分钟，支持实时应用。在各模型中，ELiteCrop0实现了最均衡的性能，混淆矩阵对角线比率（CMDR）为0.93，其次是ELiteCrop1（CMDR = 0.92）。总体而言，模型性能随垂直裁剪比例的增加而下降；因此，建议采用适度的图像裁剪（0–10%）以提高计算效率。使用超级计算机的分析得出站内（相同训练站点）准确率为0.90–0.93（Raspberry Pi：0.78–0.81），站间（未见测试站点）准确率为0.48–0.50（Raspberry Pi：0.41–0.43），表明模型泛化面临挑战。Raspberry Pi在68 °C下处理约1000张图像\u002F分钟，在8小时工作日50%容量下，PhenoCam与Raspberry Pi的处理比为1:2500。总体而言，本研究为精准农业中的实时玉米生长阶段监测提供了一种可扩展且经济高效的解决方案，并可扩展至其他作物。","Remote Sensing","2026-09-29T00:00:00Z",{"impact":19,"substance":132,"depth":19,"authority":20,"freshness":133,"relevant":22,"comment":209},"该研究将轻量深度学习模型部署于树莓派实现玉米生育期实时识别，方法新颖、数据扎实，对精准农业边缘监测有参考价值。",[211],{"name":206,"url":203},[27,28,213,29,30],"边缘计算",{"code":33,"slug":34,"name":29,"path":35,"parent_code":36},[216],{"code":56,"slug":217,"name":218,"path":219,"parent_code":145},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai",{"status":39,"locked":14,"version":40,"method":41,"evidence":221},{"candidates":222,"agricultural_evidence":45},[223,230,234,241,245],{"code":56,"score":19,"supported":45,"title":224,"abstract":226,"tags":229},[225],"deep learning",[227,225,228],"深度学习","decision support",[28],{"code":33,"score":17,"supported":45,"title":231,"abstract":232,"tags":233},[47],[29,47],[29],{"code":51,"score":235,"supported":45,"title":236,"abstract":237,"tags":240},7,[],[238,239],"精准农业","precision agriculture",[27],{"code":61,"score":235,"supported":45,"title":242,"abstract":243,"tags":244},[],[30,155],[30],{"code":246,"score":69,"supported":45,"title":247,"abstract":248,"tags":249},"I5",[],[213],[213],[251,252],"Raspberry Pi 玉米 生长阶段","PhenoCam 玉米 深度学习","RaspberryPi玉米生长阶段-3842",5,"10.3390\u002Frs18193337",{"doi":255,"openalex_id":257,"authors":258,"venue":206,"cited_by_count":111,"oa_url":203,"card":283,"direction":116,"ingested_from":118},"W7214787420",[259,262,265,268,271,274,277,280],{"name":260,"orcid":261},"Anne Joice","https:\u002F\u002Forcid.org\u002F0000-0001-7499-9222",{"name":263,"orcid":264},"Humeera Tazeen","https:\u002F\u002Forcid.org\u002F0000-0003-2411-0725",{"name":266,"orcid":267},"Talha Tufaique","https:\u002F\u002Forcid.org\u002F0000-0002-4490-5341",{"name":269,"orcid":270},"Mathala Juliet Gupta","https:\u002F\u002Forcid.org\u002F0000-0002-3886-2429",{"name":272,"orcid":273},"Cannayen Igathinathane","https:\u002F\u002Forcid.org\u002F0000-0001-8884-7959",{"name":275,"orcid":276},"Nitin Rai","https:\u002F\u002Forcid.org\u002F0000-0002-9350-9601",{"name":278,"orcid":279},"Craig W. Whippo","https:\u002F\u002Forcid.org\u002F0000-0002-7626-6919",{"name":281,"orcid":282},"David Walter Archer","https:\u002F\u002Forcid.org\u002F0000-0002-4816-7040",{"tldr":284,"method":285,"finding":286,"direction":116,"opportunity":287},"用PhenoCam图像训练轻量深度学习模型，在树莓派上实时分类玉米8个生长阶段。","基于10个PhenoCam站点图像，开发4种轻量DL模型，在树莓派5上边缘推理。","ELiteCrop0表现最佳（CMDR=0.93），站点内精度0.90-0.93，但站点间泛化差（0","站点间泛化差是主要瓶颈，可研究跨站点迁移学习或域适应以提升模型鲁棒性。","2026-09-30T23:30:29.168194Z",{"id":290,"title":291,"url":292,"summary":293,"summary_zh":294,"content":9,"source_name":10,"source_url":292,"published_at":295,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":130,"score_detail":296,"sources":298,"tags":300,"primary_topic":302,"secondary_topics":303,"classification":304,"search_phrases":324,"slug":327,"view_count":69,"doi":328,"paper":329,"created_at":359},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":19,"substance":132,"depth":19,"authority":20,"freshness":133,"relevant":22,"comment":297},"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。",[299],{"name":10,"url":292},[27,28,29,30,301],"高通量表型",{"code":33,"slug":34,"name":29,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":305},{"candidates":306,"agricultural_evidence":45},[307,311,316,320],{"code":33,"score":17,"supported":45,"title":308,"abstract":309,"tags":310},[34],[29,34],[29],{"code":61,"score":69,"supported":45,"title":312,"abstract":313,"tags":315},[],[314],"hyperspectral",[30],{"code":51,"score":22,"supported":14,"title":317,"abstract":318,"tags":319},[],[],[27],{"code":56,"score":22,"supported":14,"title":321,"abstract":322,"tags":323},[],[],[28],[325,326],"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":328,"openalex_id":330,"authors":331,"venue":10,"cited_by_count":111,"oa_url":292,"card":354,"direction":116,"ingested_from":118},"W7213658705",[332,334,336,338,339,340,342,345,347,349,351],{"name":333,"orcid":9},"Penglei Zhang",{"name":335,"orcid":9},"Tianbo Hao",{"name":337,"orcid":9},"Zhuoyuan Zhao",{"name":107,"orcid":9},{"name":109,"orcid":110},{"name":341,"orcid":9},"Zheng Cui",{"name":343,"orcid":344},"Ta Na","https:\u002F\u002Forcid.org\u002F0000-0002-1348-5655",{"name":346,"orcid":9},"Lang Qiao",{"name":348,"orcid":9},"Durval Dourado Neto",{"name":350,"orcid":9},"Feng Yang",{"name":352,"orcid":353},"Jingzhu Wu","https:\u002F\u002Forcid.org\u002F0000-0002-8386-1038",{"tldr":355,"method":356,"finding":357,"direction":116,"opportunity":358},"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",{"id":361,"title":362,"url":363,"summary":364,"summary_zh":365,"content":9,"source_name":366,"source_url":363,"published_at":367,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":368,"score_detail":369,"sources":373,"tags":375,"primary_topic":377,"secondary_topics":378,"classification":381,"search_phrases":402,"slug":405,"view_count":406,"doi":407,"paper":408,"created_at":427},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":19,"substance":18,"depth":370,"authority":371,"freshness":133,"relevant":22,"comment":372},16,12,"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。",[374],{"name":366,"url":363},[27,28,29,30,376],"病虫害预警",{"code":33,"slug":34,"name":29,"path":35,"parent_code":36},[379,380],{"code":56,"slug":217,"name":218,"path":219,"parent_code":145},{"code":61,"slug":142,"name":143,"path":144,"parent_code":145},{"status":39,"locked":14,"version":40,"method":41,"evidence":382},{"candidates":383,"agricultural_evidence":45},[384,388,394,398],{"code":33,"score":17,"supported":45,"title":385,"abstract":386,"tags":387},[34],[29,34],[29],{"code":56,"score":17,"supported":45,"title":389,"abstract":391,"tags":393},[390],"machine learning",[392,390],"机器学习",[28],{"code":61,"score":17,"supported":45,"title":395,"abstract":396,"tags":397},[155],[30,155],[30],{"code":51,"score":22,"supported":14,"title":399,"abstract":400,"tags":401},[],[],[27],[403,404],"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",3,"10.58578\u002Fkijst.v3i3.12026",{"doi":407,"openalex_id":409,"authors":410,"venue":366,"cited_by_count":111,"oa_url":9,"card":421,"direction":116,"ingested_from":118},"W7212022953",[411,413,415,417,419],{"name":412,"orcid":9},"Okwor Jude I.",{"name":414,"orcid":9},"Attamah Chinyere G.",{"name":416,"orcid":9},"Uchendu Christian N.",{"name":418,"orcid":9},"Onyima John O.",{"name":420,"orcid":9},"Idoko Bartholomew",{"tldr":422,"method":423,"finding":424,"direction":425,"opportunity":426},"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":429,"title":430,"url":431,"summary":432,"summary_zh":433,"content":9,"source_name":10,"source_url":431,"published_at":367,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":434,"score_detail":435,"sources":438,"tags":440,"primary_topic":442,"secondary_topics":443,"classification":444,"search_phrases":463,"slug":466,"view_count":406,"doi":467,"paper":468,"created_at":498},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",80,{"impact":19,"substance":132,"depth":19,"authority":20,"freshness":436,"relevant":22,"comment":437},8,"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。",[439],{"name":10,"url":431},[27,28,441,29,30],"产量预测",{"code":33,"slug":34,"name":29,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":445},{"candidates":446,"agricultural_evidence":45},[447,451,455,459],{"code":33,"score":17,"supported":45,"title":448,"abstract":449,"tags":450},[34],[29,34],[29],{"code":61,"score":21,"supported":14,"title":452,"abstract":453,"tags":454},[],[],[441,30],{"code":51,"score":22,"supported":14,"title":456,"abstract":457,"tags":458},[],[],[27],{"code":56,"score":22,"supported":14,"title":460,"abstract":461,"tags":462},[],[],[28],[464,465],"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":467,"openalex_id":469,"authors":470,"venue":10,"cited_by_count":111,"oa_url":9,"card":493,"direction":116,"ingested_from":118},"W7211999297",[471,473,476,479,481,483,485,486,488,490,492],{"name":472,"orcid":9},"Ruomei Zhao",{"name":474,"orcid":475},"Yanling Zhang","https:\u002F\u002Forcid.org\u002F0000-0002-2323-2600",{"name":477,"orcid":478},"Weimin Guo","https:\u002F\u002Forcid.org\u002F0000-0003-4318-7154",{"name":480,"orcid":9},"Wei Liu",{"name":482,"orcid":9},"Long Zhao",{"name":484,"orcid":9},"Xiaoyuan Tian",{"name":109,"orcid":110},{"name":487,"orcid":9},"Lulu An",{"name":489,"orcid":9},"Aiguo Wang",{"name":491,"orcid":9},"Qiang Xu",{"name":107,"orcid":9},{"tldr":494,"method":495,"finding":496,"direction":116,"opportunity":497},"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":500,"title":501,"url":502,"summary":503,"summary_zh":504,"content":9,"source_name":505,"source_url":502,"published_at":506,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":507,"score_detail":508,"sources":510,"tags":512,"primary_topic":514,"secondary_topics":515,"classification":517,"search_phrases":533,"slug":536,"view_count":111,"doi":537,"paper":538,"created_at":552},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":436,"substance":19,"depth":370,"authority":371,"freshness":133,"relevant":22,"comment":509},"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。",[511],{"name":505,"url":502},[27,28,392,29,513],"耐盐育种",{"code":33,"slug":34,"name":29,"path":35,"parent_code":36},[516],{"code":56,"slug":217,"name":218,"path":219,"parent_code":145},{"status":39,"locked":14,"version":40,"method":41,"evidence":518},{"candidates":519,"agricultural_evidence":45},[520,525,529],{"code":56,"score":521,"supported":45,"title":522,"abstract":523,"tags":524},19,[390],[392,390,228],[28,392],{"code":33,"score":17,"supported":45,"title":526,"abstract":527,"tags":528},[34],[29,34],[29],{"code":51,"score":235,"supported":45,"title":530,"abstract":531,"tags":532},[],[238,239],[27],[534,535],"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":537,"openalex_id":539,"authors":540,"venue":505,"cited_by_count":111,"oa_url":502,"card":547,"direction":425,"ingested_from":118},"W7221462188",[541,544],{"name":542,"orcid":543},"Mustafa Turan Arslan","https:\u002F\u002Forcid.org\u002F0000-0001-5498-6571",{"name":545,"orcid":546},"Gülay Zülkadir","https:\u002F\u002Forcid.org\u002F0000-0003-3488-4011",{"tldr":548,"method":549,"finding":550,"direction":425,"opportunity":551},"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"]