[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-461":3,"related-461":73},{"id":4,"title":5,"url":6,"summary":7,"summary_zh":8,"content":9,"source_name":10,"source_url":8,"published_at":11,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":15,"score_detail":16,"sources":24,"tags":26,"primary_topic":31,"secondary_topics":36,"classification":37,"search_phrases":67,"slug":70,"view_count":71,"doi":8,"paper":8,"created_at":72},461,"Time-Aware Late Fusion for Multimodal Rice Growth-Rate Prediction from UAV Imagery and Weather Context","https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F523","本文针对精准农业中纯图像模型难以表征季节性上下文的问题,提出时序感知晚期融合(TALF)模型,通过CNN分支编码图像特征、MLP编码上下文特征,仅在回归头合并两条流。相对湿度、风速、累积时间作为上下文输入,在多季节公开水稻苗数据集上,MAE降至0.031(图像单一基线0.1455,早期融合0.0890),容差精准度达94.9%。结果表明,天气与时间上下文能显著提升无人机图像的水稻生长速率预测,图像与表格编码器分离至最后层的晚期融合设计具有竞争力。原文标题:Time-Aware Late Fusion for Multimodal Rice Growth-Rate Prediction from UAV Imagery and Weather Context,作者:Elhadi A O、Darwish S M、Mahdi M A(埃及Zagazig大学\u002FAlexandria大学),发表于MDPI Computers 2026年第15卷第8期523。",null,"Previous Article in Journal\n\n[Energy-Efficient Distributed Flexible Job Shop Scheduling with Machine Degradation and State-Driven Imperfect Preventive Maintenance](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F522)\n\n Previous Article in Special Issue\n\n[Bridging Two Worlds: Sensor Technologies and AI for Fruit Detection in Latin America and Beyond—A Scoping Review](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F518)\n\n## Journals\n\n[Active Journals](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fjournals)[Find a Journal](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fjournalfinder)[Journal Proposal](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fjournals\u002Fproposal)[Proceedings Series](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fproceedings)\n\n## [Topics](https:\u002F\u002Fwww.mdpi.com\u002Ftopics)\n\n## Information\n\n[For Authors](https:\u002F\u002Fwww.mdpi.com\u002Fauthors)[For Reviewers](https:\u002F\u002Fwww.mdpi.com\u002Freviewers)[For Editors](https:\u002F\u002Fwww.mdpi.com\u002Feditors)[For Librarians](https:\u002F\u002Fwww.mdpi.com\u002Flibrarians)[For Publishers](https:\u002F\u002Fwww.mdpi.com\u002Fpublishing_services)[For Societies](https:\u002F\u002Fwww.mdpi.com\u002Fsocieties)[For Conference Organizers](https:\u002F\u002Fwww.mdpi.com\u002Fconference_organizers)\n\n[Open Access Policy](https:\u002F\u002Fwww.mdpi.com\u002Fopenaccess)[Institutional Open Access Program](https:\u002F\u002Fwww.mdpi.com\u002Fioap)[Special Issues Guidelines](https:\u002F\u002Fwww.mdpi.com\u002Fspecial_issues_guidelines)[Editorial Process](https:\u002F\u002Fwww.mdpi.com\u002Feditorial_process)[Research and Publication Ethics](https:\u002F\u002Fwww.mdpi.com\u002Fethics)[Article Processing Charges](https:\u002F\u002Fwww.mdpi.com\u002Fapc)[Awards](https:\u002F\u002Fwww.mdpi.com\u002Fawards)[Testimonials](https:\u002F\u002Fwww.mdpi.com\u002Ftestimonials)\n\n## [Author Services](https:\u002F\u002Fwww.mdpi.com\u002Fauthors\u002Fservices)\n\n## Initiatives\n\n[Sciforum](https:\u002F\u002Fsciforum.net\u002F)[MDPI Books](https:\u002F\u002Fwww.mdpi.com\u002Fbooks)[Preprints.org](https:\u002F\u002Fwww.preprints.org\u002F)[Scilit](https:\u002F\u002Fwww.scilit.com\u002F)[SciProfiles](https:\u002F\u002Fsciprofiles.com\u002F)[Encyclopedia](https:\u002F\u002Fencyclopedia.pub\u002F)[JAMS](https:\u002F\u002Fjams.pub\u002F)[Proceedings Series](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fproceedings)\n\n## About\n\n[Overview](https:\u002F\u002Fwww.mdpi.com\u002Fabout)[Contact](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fcontact)[Careers](https:\u002F\u002Fcareers.mdpi.com\u002F)[News](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fannouncements)[Press](https:\u002F\u002Fwww.mdpi.com\u002Fabout\u002Fpress)[Blog](http:\u002F\u002Fblog.mdpi.com\u002F)\n\n[Sign In \u002F Sign Up](https:\u002F\u002Fwww.mdpi.com\u002Fuser\u002Flogin)\n\n## Notice\n\n You can make submissions to other journals [here](https:\u002F\u002Fsusy.mdpi.com\u002Fuser\u002Fmanuscripts\u002Fupload). \n\n[_clear_](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F523)\n\n## Notice\n\nYou are accessing a machine-readable page. 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Elhadi\n\n![Image 9](https:\u002F\u002Fwww.mdpi.com\u002Fbundles\u002Fmdpisciprofileslink\u002Fimg\u002Funknown-user.png)Alaa O. Elhadi\n\n[SciProfiles](https:\u002F\u002Fsciprofiles.com\u002Fprofile\u002Fauthor\u002FM24rbk9MRmNOWXRpWjBQNTNvYTk3a3RJSXQ1bVkxVDlaMkdYdXRsYnM5WT0=?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name)[Scilit](https:\u002F\u002Fscilit.com\u002Fscholars?q=Alaa%20O.%20Elhadi)[Preprints.org](https:\u002F\u002Fwww.preprints.org\u002Fsearch?condition_blocks=[{%22value%22:%22Alaa+O.+Elhadi%22,%22type%22:%22author%22,%22operator%22:null}]&sort_field=relevance&sort_dir=desc&page=1&exact_match=true)[Google Scholar](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=Alaa+O.+Elhadi)\n\n 1[](mailto:a.elhady22@fci.zu.edu.eg)[![Image 10: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1786452582)](https:\u002F\u002Forcid.org\u002F0009-0000-0023-1632), \n\n Saad M. Darwish\n\n![Image 11](https:\u002F\u002Fwww.mdpi.com\u002Fprofiles\u002F1128316\u002Fthumb\u002FSaad_Mohamed_Darwish.jpg)Saad M. Darwish\n\n[SciProfiles](https:\u002F\u002Fsciprofiles.com\u002Fprofile\u002F1128316?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name)[Scilit](https:\u002F\u002Fscilit.com\u002Fscholars?q=Saad%20M.%20Darwish)[Preprints.org](https:\u002F\u002Fwww.preprints.org\u002Fsearch?condition_blocks=[{%22value%22:%22Saad+M.+Darwish%22,%22type%22:%22author%22,%22operator%22:null}]&sort_field=relevance&sort_dir=desc&page=1&exact_match=true)[Google Scholar](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=Saad+M.+Darwish)\n\n 2[](mailto:saad.darwish@alexu.edu.eg)[![Image 12: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1786452582)](https:\u002F\u002Forcid.org\u002F0000-0003-2723-1549) and \n\n Mahmoud A. Mahdi\n\n![Image 13](https:\u002F\u002Fwww.mdpi.com\u002Fbundles\u002Fmdpisciprofileslink\u002Fimg\u002Funknown-user.png)Mahmoud A. Mahdi\n\n[SciProfiles](https:\u002F\u002Fsciprofiles.com\u002Fprofile\u002F892372?utm_source=mdpi.com&utm_medium=website&utm_campaign=avatar_name)[Scilit](https:\u002F\u002Fscilit.com\u002Fscholars?q=Mahmoud%20A.%20Mahdi)[Preprints.org](https:\u002F\u002Fwww.preprints.org\u002Fsearch?condition_blocks=[{%22value%22:%22Mahmoud+A.+Mahdi%22,%22type%22:%22author%22,%22operator%22:null}]&sort_field=relevance&sort_dir=desc&page=1&exact_match=true)[Google Scholar](https:\u002F\u002Fscholar.google.com\u002Fscholar?q=Mahmoud+A.+Mahdi)\n\n 1,*[](mailto:mamahdi@zu.edu.eg)[![Image 14: ORCID](https:\u002F\u002Fpub.mdpi-res.com\u002Fimg\u002Fdesign\u002Forcid.png?0465bc3812adeb52?1786452582)](https:\u002F\u002Forcid.org\u002F0000-0002-7810-7006)\n\n1\n\nFaculty of Computers and Informatics, Zagazig University, Zagazig 44519, Egypt\n\n2\n\nInstitute of Graduate Studies and Research, Alexandria University, Alexandria 21568, Egypt\n\n*\n\nAuthor to whom correspondence should be addressed. \n\n_Computers_**2026**, _15_(8), 523; [https:\u002F\u002Fdoi.org\u002F10.3390\u002Fcomputers15080523](https:\u002F\u002Fdoi.org\u002F10.3390\u002Fcomputers15080523)\n\nSubmission received: 13 June 2026 \u002F Revised: 17 July 2026 \u002F Accepted: 6 August 2026 \u002F Published: 12 August 2026\n\n (This article belongs to the Special Issue [Intelligent Computing and Sensing Systems for Sustainable Precision Agriculture](https:\u002F\u002Fwww.mdpi.com\u002Fjournal\u002Fcomputers\u002Fspecial_issues\u002F5RD2OE34S0))\n\n[Download _keyboard\\_arrow\\_down_](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F523)\n\n[Download PDF](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F523\u002Fpdf?version=1786547887)\n\n[Browse Figure](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F523#)\n\n[](https:\u002F\u002Fpub.mdpi-res.com\u002Fcomputers\u002Fcomputers-15-00523\u002Farticle_deploy\u002Fcomputers-15-00523-ag.jpeg?1786547888 \"\n                        \u003Cstrong>Graphical abstract\u003C\u002Fstrong>\u003Cbr\u002F>\n                                                \")\n\n[Versions Notes](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F523\u002Fnotes)\n\n## Abstract\n\n Accurate crop-growth estimation from unmanned aerial vehicle (UAV) imagery is important for precision agriculture, but image-only models can struggle to represent seasonal context. This study evaluates whether combining UAV imagery with weather variables and elapsed time improves continuous rice growth-rate prediction in a single-site, publicly available rice-seedling dataset spanning multiple growing seasons. A Time-Aware Late Fusion (TALF) model is introduced in which a convolutional branch encodes image features, a multilayer perceptron encodes contextual features, and the two streams are merged only at the regression head. Relative humidity, wind speed, and elapsed time are used as contextual inputs after season-aware preprocessing. Evaluation is reported on a chronological multi-season split using internal ablations rather than external generalization claims. TALF achieved a mean absolute error (MAE) of 0.031, compared with 0.1455 for the optimized image-only baseline and 0.0890 for an early-fusion image-and-weather baseline. A secondary tolerance-based metric reached 94.9% under the reported threshold. The results indicate that weather and elapsed-time context improve prediction on this dataset and that separating image and tabular encoders until the final layers is a competitive multimodal learning design under the reported protocol. \n\n**Keywords:**artificial intelligence; deep learning; computer vision; multimodal learning; late fusion; UAV phenotyping; rice growth prediction; precision agriculture[artificial intelligence](https:\u002F\u002Fwww.mdpi.com\u002Fsearch?q=artificial%20intelligence); [deep learning](https:\u002F\u002Fwww.mdpi.com\u002Fsearch?q=deep%20learning); [computer vision](https:\u002F\u002Fwww.mdpi.com\u002Fsearch?q=computer%20vision); [multimodal learning](https:\u002F\u002Fwww.mdpi.com\u002Fsearch?q=multimodal%20learning); [late fusion](https:\u002F\u002Fwww.mdpi.com\u002Fsearch?q=late%20fusion); [UAV phenotyping](https:\u002F\u002Fwww.mdpi.com\u002Fsearch?q=UAV%20phenotyping); [rice growth prediction](https:\u002F\u002Fwww.mdpi.com\u002Fsearch?q=rice%20growth%20prediction); [precision agriculture](https:\u002F\u002Fwww.mdpi.com\u002Fsearch?q=precision%20agriculture)\n\n[![Image 15](https:\u002F\u002Fpub.mdpi-res.com\u002Fcomputers\u002Fcomputers-15-00523\u002Farticle_deploy\u002Fcomputers-15-00523-ag.jpeg?1786547888)](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F523#)\n\n[](https:\u002F\u002Fpub.mdpi-res.com\u002Fcomputers\u002Fcomputers-15-00523\u002Farticle_deploy\u002Fcomputers-15-00523-ag.jpeg?1786547888 \"\u003Cstrong>Graphical Abstract\u003C\u002Fstrong>\")\n\nGraphical Abstract\n\n## [](https:\u002F\u002Fwww.mdpi.com\u002F2073-431X\u002F15\u002F8\u002F523)Share and Cite\n\n[](mailto:?&subject=From%20MDPI%3A%20%22Time-Aware%20Late%20Fusion%20for%20Multimodal%20Rice%20Growth-Rate%20Prediction%20from%20UAV%20Imagery%20and%20Weather%20Context%22&body=https:\u002F\u002Fwww.mdpi.com\u002F4028776%3A%0A%0ATime-Aware%20Late%20Fusion%20for%20Multimodal%20Rice%20Growth-Rate%20Prediction%20from%20UAV%20Imagery%20and%20Weather%20Context%0A%0AAbstract%3A%20Accurate%20crop-growth%20estimation%20from%20unmanned%20aerial%20vehicle%20%28UAV%29%20imagery%20is%20important%20for%20precision%20agriculture%2C%20but%20image-only%20models%20can%20struggle%20to%20represent%20seasonal%20context.%20This%20study%20evaluates%20whether%20combining%20UAV%20imagery%20with%20weather%20variables%20and%20elapsed%20time%20improves%20continuous%20rice%20growth-rate%20prediction%20in%20a%20single-site%2C%20publicly%20available%20rice-seedling%20dataset%20spanning%20multiple%20growing%20seasons.%20A%20Time-Aware%20Late%20Fusion%20%28TALF%29%20model%20is%20introduced%20in%20which%20a%20convolutional%20branch%20encodes%20image%20features%2C%20a%20multilayer%20perceptron%20encodes%20contextual%20features%2C%20and%20the%20two%20streams%20are%20merged%20only%20at%20the%20regression%20head.%20Relative%20humidity%2C%20wind%20speed%2C%20and%20elapsed%20time%20are%20used%20as%20contextual%20inputs%20after%20season-aware%20preprocessing.%20Evaluation%20is%20reported%20on%20a%20chronological%20multi-season%20split%20using%20internal%20ablations%20rather%20than%20external%20generalization%20claims.%20TALF%20achie","MDPI Computers 2026-08-12","2026-08-12T00:00:00Z","论文",10,false,70,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},15,18,16,12,9,1,"论文提出时间感知多模态融合方法预测水稻生长速率，结合无人机影像与天气数据，方法新颖且数据规模可观，对精准农业有实质贡献。",[25],{"name":10,"url":6},[27,28,29,30],"智慧农业","无人机","水稻","多模态融合",{"code":32,"slug":33,"name":29,"path":34,"parent_code":35},"A1","rice","\u002Ftopic\u002Ffield-crops\u002Frice","A",[],{"status":38,"locked":14,"version":39,"method":40,"evidence":41},"classified","2026-10-22.2","rules",{"candidates":42,"agricultural_evidence":44},[43,48,55,62],{"code":32,"score":17,"supported":44,"title":45,"abstract":46,"tags":47},true,[33],[29,33],[29],{"code":49,"score":50,"supported":44,"title":51,"abstract":52,"tags":54},"I1",4,[],[53],"精准农业",[27],{"code":56,"score":57,"supported":44,"title":58,"abstract":59,"tags":61},"A7",3,[],[60],"间作",[],{"code":63,"score":22,"supported":14,"title":64,"abstract":65,"tags":66},"I2",[],[],[30],[68,69],"多模态融合 智慧农业 无人机 水稻","多模态融合 智慧农业","多模态融合智慧农业无人机水稻-461",5,"2026-08-12T23:57:24.154100Z",{"total":74,"page":22,"page_size":74,"items":75},6,[76,170,239,307,379,438],{"id":77,"title":78,"url":79,"summary":80,"summary_zh":81,"content":8,"source_name":82,"source_url":79,"published_at":83,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":84,"score_detail":85,"sources":90,"tags":92,"primary_topic":95,"secondary_topics":96,"classification":108,"search_phrases":147,"slug":150,"view_count":50,"doi":151,"paper":152,"created_at":169},3009,"AgriMAC: An Attention Based Multimodal Deep Clustering Framework for Rice Health Assessment","https:\u002F\u002Fdoi.org\u002F10.22266\u002Fijies2026.1031.06","Rice is Indonesia's staple crop, yet its productivity has declined in recent years because pest and disease outbreaks remain difficult to detect at an early stage.Existing precision agriculture approaches commonly process Internet of Things (IoT) sensor data and remote sensing imagery independently and often rely on supervised learning, requiring large amounts of labeled data.Meanwhile, multispectral drone imagery producing the Normalized Difference Vegetation Index (NDVI) provides richer information on crop physiological conditions than RGB-based vegetation indices.This study proposes Agricultural Multimodal Attention Clustering (AgriMAC), an unsupervised framework that integrates UAV derived NDVI imagery, 7-in-1 IoT soil sensor measurements, and historical weather data from the Open-Meteo API for rice field condition monitoring.Each modality is encoded using a dedicated autoencoder and fused through an entropy-regularized attention mechanism before Deep Embedded Clustering is performed.To reduce the influence of crop growth stage, the IoT representation is residualized using growth-phase statistics estimated exclusively from the training fold, enabling the discovered clusters to represent within-phase agronomic conditions rather than crop age.Experiments conducted under a grouped leave-one-field-out protocol produced a Silhouette Score of 0.465 ± 0.048, a Davies Bouldin Index of 0.807 ± 0.036, and a Calinski Harabasz Index of 195 ± 27.The learned groups also showed low normalized mutual information with growth phase (0.079 ± 0.043) and near chance phase decodability (balanced accuracy = 0.554 ± 0.042), indicating that they are only weakly associated with crop growth stage.The learned attention weights identified IoT soil measurements (0.570 ± 0.024) as the dominant modality, while NDVI imagery (0.210 ± 0.014) and weather information (0.220 ± 0.014) provided complementary spatial and temporal context.Overall, AgriMAC provides an interpretable and leakage-aware framework for multimodal clustering of rice field conditions.Although its clustering performance is comparable to that of a capacity-matched IoT-only model, it additionally quantifies the contribution of each sensing modality through attention weights and explicitly mitigates the growth-phase confound, making it suitable for field level agronomic condition monitoring and spatial decision support in precision agriculture.","水稻是印度尼西亚的主要作物，但近年来其生产力有所下降，因为病虫害暴发在早期阶段仍难以检测。现有的精准农业方法通常独立处理物联网（IoT）传感器数据和遥感影像，且往往依赖监督学习，需要大量标注数据。与此同时，生成归一化植被指数（NDVI）的多光谱无人机影像比基于RGB的植被指数能提供更丰富的作物生理状况信息。本研究提出农业多模态注意力聚类（AgriMAC），这是一个无监督框架，整合了无人机获取的NDVI影像、七合一IoT土壤传感器测量数据以及来自Open-Meteo API的历史天气数据，用于稻田状况监测。每种模态均使用专用自编码器进行编码，并通过熵正则化注意力机制进行融合，随后执行深度嵌入聚类。为减少作物生长阶段的影响，IoT表征利用仅从训练折估计的生长阶段统计量进行残差化处理，使发现的聚类能够表征阶段内的农艺状况而非作物年龄。在分组留一田块协议下进行的实验产生了0.465 ± 0.048的轮廓系数、0.807 ± 0.036的Davies-Bouldin指数和195 ± 27的Calinski-Harabasz指数。学习到的分组还显示出与生长阶段的低归一化互信息（0.079 ± 0.043）以及接近随机的阶段可解码性（平衡准确率 = 0.554 ± 0.042），表明它们与作物生长阶段仅存在弱关联。学习到的注意力权重将IoT土壤测量（0.570 ± 0.024）识别为主导模态，而NDVI影像（0.210 ± 0.014）和天气信息（0.220 ± 0.014）则提供了互补的空间和时间背景。总体而言，AgriMAC为稻田状况的多模态聚类提供了一个可解释且感知数据泄漏的框架。尽管其聚类性能与容量匹配的仅IoT模型相当，但它还通过注意力权重量化了每种传感模态的贡献，并明确减轻了生长阶段混杂因素，使其适用于田块级农艺状况监测和精准农业中的空间决策支持。","International journal of intelligent engineering and systems","2026-09-19T00:00:00Z",72,{"impact":20,"substance":86,"depth":87,"authority":88,"freshness":21,"relevant":22,"comment":89},21,17,13,"提出无监督多模态注意力聚类框架，融合无人机NDVI、IoT土壤与气象数据评估水稻健康，方法新颖且实验严谨，对精准农业田间监测有参考价值。",[91],{"name":82,"url":79},[27,93,29,30,94],"农业人工智能","遥感",{"code":32,"slug":33,"name":29,"path":34,"parent_code":35},[97,103],{"code":98,"slug":99,"name":100,"path":101,"parent_code":102},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing","I",{"code":104,"slug":105,"name":106,"path":107,"parent_code":102},"I5","agricultural-iot","物联网传感与边缘计算","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-iot",{"status":38,"locked":14,"version":39,"method":40,"evidence":109},{"candidates":110,"agricultural_evidence":44},[111,116,122,129,135,142],{"code":32,"score":18,"supported":44,"title":112,"abstract":113,"tags":115},[33],[29,114,33],"稻田",[29],{"code":98,"score":13,"supported":44,"title":117,"abstract":118,"tags":121},[],[94,119,120],"remote sensing","multispectral",[94],{"code":104,"score":21,"supported":44,"title":123,"abstract":124,"tags":128},[],[125,126,127],"物联网","internet of things","IoT",[],{"code":49,"score":130,"supported":44,"title":131,"abstract":132,"tags":134},7,[],[53,133],"precision agriculture",[27],{"code":136,"score":74,"supported":44,"title":137,"abstract":138,"tags":141},"H2",[],[139,140],"土壤","soil",[],{"code":63,"score":71,"supported":44,"title":143,"abstract":144,"tags":146},[],[145],"decision support",[93,30],[148,149],"AgriMAC 水稻 多模态聚类","无人机 NDVI 水稻 病害监测","AgriMAC水稻多模态聚类-3009","10.22266\u002Fijies2026.1031.06",{"doi":151,"openalex_id":153,"authors":154,"venue":82,"cited_by_count":161,"oa_url":79,"card":162,"direction":166,"ingested_from":168},"W7213619014",[155,157,159],{"name":156,"orcid":8},"Nurfadhilah Mardianti Andini",{"name":158,"orcid":8},"Mike Yuliana",{"name":160,"orcid":8},"Moch. Zen Samsono Hadi",0,{"tldr":163,"method":164,"finding":165,"direction":166,"opportunity":167},"提出无监督多模态聚类框架AgriMAC，融合无人机NDVI、IoT土壤与气象数据评估水稻健康。","各模态自编码器编码，熵正则注意力融合，深度嵌入聚类，按生长阶段残差化。","聚类性能与仅IoT模型相当，但注意力权重可解释模态贡献并弱化生长阶段混淆。","智慧农业 \u002F 农业物联网","可探索注意力融合机制在更多作物与传感器组合下的泛化性，并引入时序动态聚类。","openalex","2026-09-20T23:30:08.419613Z",{"id":171,"title":172,"url":173,"summary":174,"summary_zh":175,"content":8,"source_name":176,"source_url":173,"published_at":177,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":178,"score_detail":179,"sources":184,"tags":186,"primary_topic":189,"secondary_topics":190,"classification":192,"search_phrases":208,"slug":211,"view_count":212,"doi":213,"paper":214,"created_at":238},2663,"Research on an automated mapping method for rice aboveground biomass based on low-altitude remote sensing","https:\u002F\u002Fdoi.org\u002F10.4081\u002Fjae.2026.2061","Accurate monitoring of rice aboveground biomass (AGB) is crucial for guiding agricultural production management. This study focuses on high-precision estimation and automated mapping of rice AGB. Field experiments were conducted in Nanxun District, Huzhou City, Zhejiang Province. We collected UAV RGB and multispectral images, rice AGB, and plant height data. By integrating vegetation indices, texture features, and plant height information, the AGB estimation model was established using algorithms such as Stacking. A framework combining \"SAM + MobileNetV3-Small classification\" was proposed to achieve automated paddy field extraction and phenology recognition. The results demonstrate that the rice AGB prediction model based on the Stacking ensemble algorithm performed excellently. The introduction of plant height significantly improved model accuracy. For example, during the heading stage, R2 increased from 0.421 to 0.739, and RPIQ rose from 1.995 to 3.015. The automated paddy field extraction and phenology recognition framework developed in this study achieved a segmentation accuracy of 0.968 and a classification accuracy of 0.993 on the dataset used in this study, without requiring manual annotation. This research provides a technical reference for automated and high-precision mapping of rice AGB.","准确监测水稻地上生物量(AGB)对指导农业生产管理至关重要。本研究聚焦水稻AGB的高精度估算与自动化制图。田间试验在浙江省湖州市南浔区开展，采集了无人机RGB和多光谱影像、水稻AGB及株高数据。通过融合植被指数、纹理特征和株高信息，利用Stacking等算法构建AGB估算模型。提出了一种“SAM+MobileNetV3-Small分类”框架，以实现稻田自动化提取和物候识别。结果表明，基于Stacking集成算法的水稻AGB预测模型表现优异，株高的引入显著提高了模型精度。例如，在抽穗期，R2从0.421提升至0.739，RPIQ从1.995提升至3.015。本研究开发的稻田自动化提取与物候识别框架在本研究数据集上实现了0.968的分割精度和0.993的分类精度，且无需人工标注。本研究为水稻AGB的自动化高精度制图提供了技术参考。","Journal of Agricultural Engineering","2026-09-15T00:00:00Z",78,{"impact":19,"substance":180,"depth":18,"authority":181,"freshness":182,"relevant":22,"comment":183},22,14,8,"基于无人机遥感与Stacking集成模型实现水稻地上生物量自动化制图，方法新颖、数据扎实，对精准农业管理有参考价值。",[185],{"name":176,"url":173},[27,28,187,29,188],"农业遥感","作物表型",{"code":32,"slug":33,"name":29,"path":34,"parent_code":35},[191],{"code":98,"slug":99,"name":100,"path":101,"parent_code":102},{"status":38,"locked":14,"version":39,"method":40,"evidence":193},{"candidates":194,"agricultural_evidence":44},[195,200,204],{"code":32,"score":18,"supported":44,"title":196,"abstract":197,"tags":199},[33],[29,114,33,198],"paddy",[29],{"code":98,"score":88,"supported":44,"title":201,"abstract":202,"tags":203},[119],[120],[188,187],{"code":49,"score":22,"supported":14,"title":205,"abstract":206,"tags":207},[],[],[27],[209,210],"作物表型 农业遥感 智慧农业 无人机","作物表型 农业遥感","作物表型农业遥感智慧农业无人机-2663",2,"10.4081\u002Fjae.2026.2061",{"doi":213,"openalex_id":215,"authors":216,"venue":176,"cited_by_count":161,"oa_url":173,"card":232,"direction":236,"ingested_from":168},"W7213229220",[217,219,221,223,225,228,230],{"name":218,"orcid":8},"Honggang Xu",{"name":220,"orcid":8},"Xuehan Li",{"name":222,"orcid":8},"Jia Shen",{"name":224,"orcid":8},"Ziyi Li",{"name":226,"orcid":227},"Zhe Li","https:\u002F\u002Forcid.org\u002F0009-0008-6496-3697",{"name":229,"orcid":8},"Yiming Li",{"name":231,"orcid":8},"Pengcheng Nie",{"tldr":233,"method":234,"finding":235,"direction":236,"opportunity":237},"基于无人机RGB与多光谱影像，结合株高与Stacking集成算法，实现水稻地上生物量高精度自动制图。","无人机RGB\u002F多光谱影像、植被指数、纹理与株高，Stacking集成及SAM+M","引入株高显著提升精度，抽穗期R²从0.421升至0.739；自动稻田提取与物候识别精度达0.968和","农业遥感与作物表型","可探索多生育期、多品种下株高与纹理特征的迁移性，并耦合深度学习实现全自动生物量时空制图。","2026-09-16T23:30:28.929376Z",{"id":240,"title":241,"url":242,"summary":243,"summary_zh":244,"content":8,"source_name":245,"source_url":242,"published_at":246,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":178,"score_detail":247,"sources":249,"tags":251,"primary_topic":253,"secondary_topics":254,"classification":256,"search_phrases":288,"slug":291,"view_count":161,"doi":292,"paper":293,"created_at":306},5394,"Rice Yield Prediction Using Multi-Modal Regression Learning with Optimized Ensemble Models","https:\u002F\u002Fdoi.org\u002F10.3390\u002Feng7100572","Accurate rice yield estimation is important for agricultural planning, resource allocation, and food security assessment, but yield depends on interacting climatic, vegetation, remote sensing, seasonal, and crop growth factors. This study presents a Multi-Modal Regression Learning (MMRL) framework for continuous rice yield prediction. The framework integrates rainfall, temperature, humidity, normalized difference vegetation index (NDVI), VH synthetic aperture radar (SAR) backscatter, season, rice cultivation area, and leaf wetness information. Predictor relevance is assessed before model development, and multiple regression learners are trained using a common feature set. Their validation predictions are combined through non-negative ensemble weights constrained to sum to one, thereby producing a single optimized prediction. Experiments were performed on a modeled spatio-temporal agricultural dataset representing Karnataka conditions, with 10,000 observations used for the regression comparison. The proposed MMRL ensemble achieved an R2 of 0.9515, an RMSE of 0.6334 tons\u002Fha, an MAE of 0.5075 tons\u002Fha, and an MAPE of 9.81%, outperforming the evaluated individual regression models. Diagnostic plots further indicated close agreement between predicted and reference yields across most of the evaluated range. The MMRL workflow was also evaluated on a separately reconstructed synthetic dataset generated with fully specified distributions, sampling rules, noise terms, a yield generation equation, and fixed random seeds.","准确的水稻产量估算对农业规划、资源配置和粮食安全评估具有重要意义，但产量取决于气候、植被、遥感、季节和作物生长等多种因素的相互作用。本研究提出了一种用于连续水稻产量预测的多模态回归学习（Multi-Modal Regression Learning，MMRL）框架。该框架整合了降雨量、温度、湿度、归一化植被指数（NDVI）、VH合成孔径雷达（SAR）后向散射、季节、水稻种植面积和叶片湿润信息。在模型开发之前评估预测变量的相关性，并使用共同特征集训练多个回归学习器。其验证预测结果通过约束为和等于1的非负集成权重进行组合，从而生成单一优化预测。实验在代表卡纳塔克邦条件的建模时空农业数据集上进行，使用10,000个观测值进行回归比较。所提出的MMRL集成模型实现了R²为0.9515、RMSE为0.6334吨\u002F公顷、MAE为0.5075吨\u002F公顷、MAPE为9.81%，优于所评估的各个回归模型。诊断图进一步表明，在大多数评估范围内，预测产量与参考产量高度一致。MMRL工作流程还在一个单独重建的合成数据集上进行了评估，该数据集使用完全指定的分布、采样规则、噪声项、产量生成方程和固定随机种子生成。","Eng—Advances in Engineering","2026-10-18T00:00:00Z",{"impact":18,"substance":180,"depth":18,"authority":20,"freshness":182,"relevant":22,"comment":248},"多模态回归集成方法在水稻估产上取得较高精度，方法新颖、数据规模明确，对农业信息化有参考价值，但属学术论文且数据集为模拟数据，产业影响有限。",[250],{"name":245,"url":242},[27,93,29,252,94],"产量预测",{"code":32,"slug":33,"name":29,"path":34,"parent_code":35},[255],{"code":98,"slug":99,"name":100,"path":101,"parent_code":102},{"status":38,"locked":14,"version":39,"method":40,"evidence":257},{"candidates":258,"agricultural_evidence":44},[259,263,267,274,280,284],{"code":32,"score":17,"supported":44,"title":260,"abstract":261,"tags":262},[33],[29,33],[29],{"code":98,"score":182,"supported":44,"title":264,"abstract":265,"tags":266},[],[94,119],[252,94],{"code":268,"score":74,"supported":44,"title":269,"abstract":270,"tags":273},"A8",[],[271,272],"粮食安全","food security",[],{"code":275,"score":57,"supported":44,"title":276,"abstract":277,"tags":279},"J1",[],[278],"农业规划",[],{"code":49,"score":22,"supported":14,"title":281,"abstract":282,"tags":283},[],[],[27],{"code":63,"score":22,"supported":14,"title":285,"abstract":286,"tags":287},[],[],[93],[289,290],"水稻 产量预测 多模态","NDVI SAR 水稻估产","水稻产量预测多模态-5394","10.3390\u002Feng7100572",{"doi":292,"openalex_id":294,"authors":295,"venue":245,"cited_by_count":161,"oa_url":242,"card":301,"direction":236,"ingested_from":168},"W7222942064",[296,298],{"name":297,"orcid":8},"Nayana Venkatesh",{"name":299,"orcid":300},"Evangelin Geetha Devaraj","https:\u002F\u002Forcid.org\u002F0000-0002-7253-4314",{"tldr":302,"method":303,"finding":304,"direction":236,"opportunity":305},"构建多模态回归学习框架，融合气候、遥感与作物因子，实现水稻产量连续预测。","融合降雨、温度、NDVI、SAR后向散射等多源特征，用非负权重集成多个回归器。","集成模型R²达0.9515，RMSE为0.6334吨\u002F公顷，优于各单一回归模型。","可引入真实田间多时相遥感与深度时序模型，验证合成数据结论在真实产区的泛化性。","2026-10-20T23:30:19.292634Z",{"id":308,"title":309,"url":310,"summary":311,"summary_zh":312,"content":8,"source_name":313,"source_url":310,"published_at":314,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":315,"score_detail":316,"sources":318,"tags":320,"primary_topic":323,"secondary_topics":324,"classification":325,"search_phrases":360,"slug":363,"view_count":161,"doi":364,"paper":365,"created_at":378},5388,"A digital twin framework for proactive disease management in paddy cultivation using spatio-temporal graph neural networks","https:\u002F\u002Fdoi.org\u002F10.1080\u002F20421338.2026.2707951","Rice production is highly affected by disease outbreaks, particularly in Asian agricultural systems, resulting in major yield losses and excessive pesticide usage. This study proposes a digital twin framework for paddy cultivation that integrates predictive disease modelling with real-time crop phenology simulation to support proactive disease management. The framework utilizes historical phenological records, IoT-based microclimatic sensor data, irrigation information, and field management records collected from 150 hectares of paddy fields in Maharashtra, India. A Spatio-Temporal Graph Neural Network (ST-GNN), integrating Graph Convolutional Networks (GCNs) and Long Short-Term Memory (LSTM) networks, is employed to model spatial disease propagation and temporal crop dynamics for predicting blast, brown planthopper, and bacterial leaf blight outbreaks up to 14 days in advance. Experimental results demonstrate 87.3% prediction accuracy, 23.7% reduction in pesticide usage, and 31.2% reduction in crop losses, while outperforming traditional Random Forest and SVM models in F1-score performance. The proposed framework enables real-time decision support for sustainable precision agriculture and optimized resource management.","水稻生产极易受到病害暴发的影响，在亚洲农业体系中尤为突出，造成重大产量损失和农药过度使用。本研究提出一个水稻栽培数字孪生（digital twin）框架，将预测性病害建模与实时作物物候模拟相结合，以支持主动式病害管理。该框架利用从印度马哈拉施特拉邦150公顷稻田采集的历史物候记录、基于物联网的微气候传感器数据、灌溉信息及田间管理记录。采用融合图卷积网络（Graph Convolutional Networks, GCN）与长短期记忆网络（Long Short-Term Memory, LSTM）的时空图神经网络（Spatio-Temporal Graph Neural Network, ST-GNN），对病害空间传播与作物时序动态进行建模，以提前14天预测稻瘟病、褐飞虱和白叶枯病的暴发。实验结果表明，预测准确率达87.3%，农药使用量减少23.7%，作物损失减少31.2%，且在F1分数上优于传统随机森林（Random Forest）和支持向量机（SVM）模型。所提框架可为可持续精准农业和优化资源管理提供实时决策支持。","African Journal of Science Technology Innovation and Development","2026-10-19T00:00:00Z",80,{"impact":18,"substance":180,"depth":18,"authority":88,"freshness":21,"relevant":22,"comment":317},"方法新颖、数据规模明确且量化效果显著，属智慧农业细分领域实质进展，值得进入每日精选。",[319],{"name":313,"url":310},[27,93,29,321,322],"数字孪生","病虫害预警",{"code":32,"slug":33,"name":29,"path":34,"parent_code":35},[],{"status":38,"locked":14,"version":39,"method":40,"evidence":326},{"candidates":327,"agricultural_evidence":44},[328,334,338,345,352,356],{"code":49,"score":329,"supported":44,"title":330,"abstract":332,"tags":333},19,[331],"digital twin",[53,321,133,331],[321,27],{"code":32,"score":18,"supported":44,"title":335,"abstract":336,"tags":337},[198],[29,114,33,198],[29],{"code":339,"score":74,"supported":44,"title":340,"abstract":341,"tags":344},"F4",[],[342,343],"农药","pesticide",[],{"code":346,"score":74,"supported":44,"title":347,"abstract":348,"tags":351},"H3",[],[349,350],"灌溉","irrigation",[],{"code":104,"score":74,"supported":44,"title":353,"abstract":354,"tags":355},[],[125,127],[],{"code":63,"score":50,"supported":44,"title":357,"abstract":358,"tags":359},[],[145],[93],[361,362],"水稻 病害预测 数字孪生","ST-GNN 水稻 病虫害","水稻病害预测数字孪生-5388","10.1080\u002F20421338.2026.2707951",{"doi":364,"openalex_id":366,"authors":367,"venue":313,"cited_by_count":161,"oa_url":8,"card":372,"direction":166,"ingested_from":168},"W7222969403",[368,370],{"name":369,"orcid":8},"Jyoti Rani",{"name":371,"orcid":8},"Dr.  Narendra S.  Jadhav",{"tldr":373,"method":374,"finding":375,"direction":376,"opportunity":377},"构建水稻数字孪生框架，用时空图神经网络提前14天预测病害。","融合物联网微气候、物候与管理数据，用GCN+LSTM的ST-GNN建模。","预测准确率87.3%，农药减23.7%、损失减31.2%，优于RF与SVM。","农业人工智能与决策模型","可探索多尺度时空图建模与数字孪生闭环决策，并迁移至其他作物与区域。","2026-10-20T23:30:12.636299Z",{"id":380,"title":381,"url":382,"summary":383,"summary_zh":384,"content":8,"source_name":385,"source_url":382,"published_at":386,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":315,"score_detail":387,"sources":389,"tags":391,"primary_topic":394,"secondary_topics":395,"classification":400,"search_phrases":417,"slug":420,"view_count":161,"doi":421,"paper":422,"created_at":437},4802,"A Privacy-Preserving Federated Learning Framework for Paddy Leaf Disease Detection Using an Enhanced Pretrained Novel EfficientNet-Lite4 Model","https:\u002F\u002Fdoi.org\u002F10.15832\u002Fankutbd.1837705","The correct and prompt detection of leaf diseases in paddy crops remains essential for protecting agricultural output and yield stability and reducing major losses that occur during severe disease outbreaks. The widespread use of conventional centralized machine learning techniques becomes impractical for real-world agricultural applications, as their effectiveness diminishes under three main conditions: data privacy limitations, delayed transmission of information, along with reduced performance accuracy from non-independent and identically distributed (non-IID) data properties across different agro-climatic regions. This research develops a decentralized diagnostic system which combines Federated Learning with an Enhanced Pretrained Novel EfficientNet-Lite4 model designed for paddy leaf disease classification. The Paddy Doctor dataset served as training material for the system which contained 16,225 RGB images that classified 12 disease types alongside healthy categories. The proposed model reached 98.7% classification accuracy and 98.5% precision and 98.6% F1-score while maintaining a final loss of 0.21 after 100 communication rounds. The system underwent field testing which demonstrated its ability to generate accurate diagnostic results in real time in various working conditions. The study demonstrates how privacy preserving AI-based paddy disease management works in practice and recommends local agricultural institutions to build decentralized learning systems. The suggested system will give farmers direct access to field level decision support and facilitate the creation of sustainable digital pest management systems required to ensure food supply.","正确且及时地检测水稻作物叶片病害，对于保护农业产出和产量稳定、减少病害严重暴发期间发生的重大损失仍然至关重要。传统集中式机器学习技术的广泛使用在现实农业应用中变得不切实际，因为在三种主要条件下其有效性会下降：数据隐私限制、信息传输延迟，以及不同农业气候区域间非独立同分布（non-IID）数据特性导致的性能准确率降低。本研究开发了一种去中心化诊断系统，将联邦学习与一种增强的预训练新型高效网络模型EfficientNet-Lite4相结合，用于水稻叶片病害分类。Paddy Doctor数据集作为该系统的训练材料，包含16,225张RGB图像，涵盖12种病害类型以及健康类别。所提出的模型在100轮通信后达到了98.7%的分类准确率、98.5%的精确率和98.6%的F1分数，同时最终损失保持在0.21。该系统经过实地测试，证明其能够在各种工作条件下实时生成准确的诊断结果。该研究展示了基于隐私保护人工智能的水稻病害管理如何在实践中运作，并建议地方农业机构构建去中心化学习系统。所建议的系统将为农民提供直接的田间决策支持，并促进建立确保粮食供应所需的可持续数字病虫害管理系统。","Tarım Bilimleri 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