[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4834":3,"related-4834":96},{"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":38,"classification":39,"search_phrases":69,"slug":72,"view_count":73,"doi":74,"paper":75,"created_at":95},4834,"Comparative Analysis of Spatial Statistical Methods for Modelling Rice Yield Variability in Uniformity Trials","https:\u002F\u002Fdoi.org\u002F10.37394\u002F232028.2026.6.9","Spatial heterogeneity is a fundamental characteristic of agricultural landscapes, and accurately modelling its impact on crop yield is critical for precision agriculture. This study presents a comparative analysis of spatial statistical and machine learning approaches to model yield variability in a field exhibiting strong spatial dependence. Initial geostatistical analysis confirmed significant positive spatial autocorrelation (Moran's I = 0.7355, p \u003C 2.2e-16). A series of models were evaluated, progressing from linear and polynomial trend surfaces to spatial regression (Lag and Error), mixed effects, and a Random Forest ensemble. The results demonstrate that while traditional spatial models effectively captured and explained the spatial autocorrelation, a Random Forest model significantly outperformed all others in predictive accuracy, achieving a 31% lower Root Mean Square Error (36.05) than the best spatial regression model and explaining 71.02% of the variance. Among the spatial models, a quadratic trend surface with a row × column interaction provided the most parsimonious fit, revealing an accelerating yield increase along rows and a U-shaped pattern across columns. The Random Forest model's residuals showed no statistically significant remaining spatial autocorrelation (Moran's I p-value = 0.97); however, we caution that failure to reject spatial randomness does not prove its absence. Equivalence testing (TOST) and power analysis support the conclusion that spatial dependence was adequately captured. We conclude that machine learning ensembles like Random Forest offer superior predictive power for yield mapping, though spatial regression models remain valuable for providing parsimonious, interpretable insights into the underlying structural gradients driving yield variability.","空间异质性是农业景观的基本特征，准确模拟其对作物产量的影响对于精准农业至关重要。本研究对空间统计方法与机器学习方法进行了比较分析，以模拟具有强空间依赖性的田块中的产量变异性。初步地统计分析确认了显著的正空间自相关（Moran's I = 0.7355，p \u003C 2.2e-16）。研究评估了一系列模型，从线性趋势面和多项式趋势面逐步推进到空间回归（滞后模型和误差模型）、混合效应模型以及随机森林集成模型。结果表明，尽管传统空间模型能够有效捕捉并解释空间自相关，但随机森林模型在预测精度上显著优于所有其他模型，其均方根误差（36.05）比最佳空间回归模型低31%，并解释了71.02%的方差。在空间模型中，带有行×列交互项的二次趋势面提供了最简约的拟合，揭示了沿行方向产量增加的加速趋势以及沿列方向的U型模式。随机森林模型的残差未显示出统计学上显著的剩余空间自相关（Moran's I p值 = 0.97）；然而，我们提醒，未能拒绝空间随机性并不能证明其不存在。等效性检验（TOST）和功效分析支持空间依赖性已被充分捕捉的结论。我们得出结论：诸如随机森林之类的机器学习集成方法在产量制图方面具有更优越的预测能力，尽管空间回归模型对于提供简约且可解释的见解——揭示驱动产量变异性的潜在结构性梯度——仍然具有重要价值。",null,"International Journal of Computational and Applied Mathematics & Computer Science","2026-10-12T00:00:00Z","论文",10,false,68,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},8,21,17,13,9,1,"方法学扎实的作物产量空间建模对比研究，对精准农业与产量遥感建模有参考价值，但属细分领域学术成果，公共影响有限。",[25],{"name":10,"url":6},[27,28,29,30,31],"智慧农业","农业人工智能","精准农业","遥感估产","空间统计",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},"A1","rice","水稻","\u002Ftopic\u002Ffield-crops\u002Frice","A",[],{"status":40,"locked":14,"version":41,"method":42,"evidence":43},"classified","2026-10-22.2","rules",{"candidates":44,"agricultural_evidence":46},[45,50,56,64],{"code":33,"score":17,"supported":46,"title":47,"abstract":48,"tags":49},true,[34],[],[],{"code":51,"score":17,"supported":46,"title":52,"abstract":53,"tags":55},"I1",[],[29,54],"precision agriculture",[27,29],{"code":57,"score":58,"supported":46,"title":59,"abstract":60,"tags":63},"I2",7,[],[61,62],"机器学习","machine learning",[28],{"code":65,"score":22,"supported":14,"title":66,"abstract":67,"tags":68},"I3",[],[],[30],[70,71],"水稻产量 空间统计 随机森林","水稻产量变异 空间自相关","水稻产量空间统计随机森林-4834",0,"10.37394\u002F232028.2026.6.9",{"doi":74,"openalex_id":76,"authors":77,"venue":10,"cited_by_count":73,"oa_url":86,"card":87,"direction":93,"ingested_from":94},"W7221516422",[78,80,82,84],{"name":79,"orcid":9},"Adebayo O. P",{"name":81,"orcid":9},"Ahmed I",{"name":83,"orcid":9},"Garba I. M",{"name":85,"orcid":9},"Mustaph A. D","https:\u002F\u002Fwseas.com\u002Fjournals\u002Fcamcs\u002F2026\u002Fa18camcs-009(2026).pdf",{"tldr":88,"method":89,"finding":90,"direction":91,"opportunity":92},"比较空间统计与随机森林模型对水稻产量空间变异的建模效果。","基于均匀试验田产量数据，用Moran's I、空间回归、混合效应与随机森林对比。","随机森林预测精度最高，RMSE比最优空间回归低31%，解释71%方差。","农业遥感与作物表型","可探索机器学习与空间统计耦合的混合模型，兼顾预测精度与空间可解释性。","农业人工智能与决策模型","openalex","2026-10-13T23:30:41.841849Z",{"total":97,"page":22,"page_size":97,"items":98},6,[99,155,241,310,382,459],{"id":100,"title":101,"url":102,"summary":103,"summary_zh":9,"content":104,"source_name":105,"source_url":9,"published_at":106,"category":107,"cover_url":9,"hotness":13,"is_selected":14,"score":108,"score_detail":109,"sources":112,"tags":114,"primary_topic":116,"secondary_topics":117,"classification":118,"search_phrases":150,"slug":153,"view_count":22,"doi":9,"paper":9,"created_at":154},3101,"AI 接管稻田：四川眉山永丰村 300 亩 AI 试点水稻亩产 826.8-863.6 公斤","https:\u002F\u002Fai-damn.com\u002Fai-takes-over-the-rice-fields-863-6-kg-per-mu-in-sichuan-pilot-1789513373662","9-14 四川省眉山市东坡区太和镇永丰村千亩高标准农田 300 亩 AI 试点田通过专家组测产验收：\"华浙优 210\"（高产优质杂交稻）亩产 826.8 公斤、\"胜两优 222\"（超高产籼粳杂交稻）亩产 863.6 公斤、\"全优 169\"（超高产杂交籼稻）亩产 858.8 公斤。AI 系统通过无人机巡检采集数据，对种植、水肥调控和病虫害早期预警提供精准建议。四川农业大学水稻栽培专家马均教授表示，结合良种、良法与 AI 精准管理可有效释放水稻增产潜力，为大规模单产提升提供可复制技术路径；今年永丰村共有 240 余个新品种在产量\u002F株型\u002F米质上表现良好，智能精准播种技术与 AI 应用已初步见效。","## AI Takes Over the Rice Fields: 863.6 kg per Mu in Sichuan Pilot\n\nIn the rolling fields of Yongfeng Village, Tahe Town, Dongpo District, Meishan City, Sichuan Province, something unusual happened this harvest season. On September 14, as combines rolled through the thousand-mu high-standard farmland, 300 mu of it had been managed not by traditional farming wisdom alone, but by an **AI model** specifically designed for rice cultivation.\n\nGone are the days of \"judging fields by experience.\" Now, it's all about **making decisions based on data**.\n\n### From Experience to Data\n\nThe embankments were crowded with agricultural experts and curious farmers, all gathered to witness a field test. An expert group organized by the Sichuan Provincial Science and Technology Department was evaluating a project led by Sichuan Agricultural University: the \"Integrated Demonstration and Application of High-quality, High-yield, and Efficient Production Technologies for Rice-Vegetable (Medicinal) Crops in the Chengdu Plain.\"\n\nSo how does it work? The AI system collects data through **drone inspections**, then provides precise recommendations on planting, water and fertilizer regulation, and pest and disease early warning.\n\nLocal large-scale grain farmer Zhao Youyong put it simply: \"Before, farming relied on experience for field inspections. Now, using drones and the AI system, we get timely information about pests and diseases, so we can handle them directly. Farming has become more convenient.\"\n\n### The Numbers That Matter\n\nThe expert group's standardized yield test delivered solid results. All three core varieties in the 300-mu AI pilot fields performed impressively:\n\n*   **\"Huazheyous 210\"** (high-yield, high-quality hybrid rice): 826.8 kg per mu\n*   **\"Shengliangyou 222\"** (super-high-yield indica-japonica hybrid rice): **863.6 kg per mu**\n*   **\"Quanyou 169\"** (super-high-yield hybrid indica rice): 858.8 kg per mu\n\nMa Jun, a rice cultivation expert at Sichuan Agricultural University, explained that these yields prove that combining quality seeds with appropriate methods and AI precision management can **effectively release the potential for rice yield increase**. It offers a replicable technical path for large-scale yield improvement.\n\nHe also noted that more than 240 new varieties demonstrated good performance in yield, plant shape, and rice quality in Yongfeng Village this year. The application of intelligent precision sowing technology and AI has already shown initial results.\n\n### What This Means for the Future\n\nThis pilot isn't just about one good harvest. It's a glimpse into how **AI can transform traditional agriculture**. By moving from experience-based to data-driven farming, growers can make more informed decisions, reduce risks, and potentially achieve higher yields sustainably.\n\nAs Ma Jun pointed out, the combination of quality seeds, appropriate methods, and AI precision management provides a technical path that can be replicated on a larger scale. For a country that feeds 20% of the world's population with less than 10% of its arable land, such innovations are more than welcome—they're essential.\n\n### Key Points\n\n*   **AI-managed pilot field** in Sichuan achieved rice yields up to **863.6 kg per mu**.\n*   **Drones and data** replaced traditional experience-based farming for planting, fertilization, and pest control.\n*   **Three rice varieties** all exceeded 826 kg per mu, proving the effectiveness of AI precision management.\n*   **Experts say** this approach offers a replicable path for large-scale yield improvement.\n*   **The future of farming** is shifting from \"judging fields by experience\" to \"making decisions based on data.\"","AI DAMN","2026-09-14T10:00:00Z","报道",76,{"impact":110,"substance":110,"depth":19,"authority":21,"freshness":97,"relevant":22,"comment":111},22,"AI精准管理水稻试点实测亩产数据具体、多方信源，具备可复制的智慧农业示范价值，值得入选每日精选。",[113],{"name":105,"url":102},[27,28,35,29,115],"无人機巡田",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":119},{"candidates":120,"agricultural_evidence":46},[121,128,135,141,146],{"code":33,"score":122,"supported":46,"title":123,"abstract":125,"tags":127},23,[35,124],"稻田",[35,126],"杂交稻",[35],{"code":129,"score":130,"supported":46,"title":131,"abstract":132,"tags":134},"A8",3,[],[133],"单产提升",[],{"code":136,"score":130,"supported":46,"title":137,"abstract":138,"tags":140},"H1",[],[139],"高标准农田",[],{"code":51,"score":142,"supported":14,"title":143,"abstract":144,"tags":145},2,[],[],[27,29],{"code":57,"score":22,"supported":14,"title":147,"abstract":148,"tags":149},[],[],[28],[151,152],"四川眉山 永丰村 AI水稻","四川农业大学 水稻 AI试点","四川眉山永丰村AI水稻-3101","2026-09-22T00:05:33.996455Z",{"id":156,"title":157,"url":158,"summary":159,"summary_zh":160,"content":9,"source_name":161,"source_url":158,"published_at":162,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":108,"score_detail":163,"sources":166,"tags":168,"primary_topic":170,"secondary_topics":171,"classification":177,"search_phrases":200,"slug":203,"view_count":22,"doi":204,"paper":205,"created_at":240},2512,"Deep learning-based classification of wet direct seeded rice, broadcasted direct seeded rice and transplanted rice using drone imagery for precision agriculture","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffrsen.2026.1698781","Accurate estimation of crop area using classification techniques applied to drone imagery plays an important role in precision agriculture. Traditional machine learning (ML) approaches have been widely used for agricultural image classification; however, advanced deep learning (DL) models generally provide superior feature extraction and classification capability for complex crop patterns. Differentiating various rice establishment methods is essential for precise area estimation. In this study, advanced deep learning models were employed to classify three types of rice cultivation: (i) Broadcasted Direct Seeded Rice (DSR), (ii) Wet Direct Seeded Rice (Wet DSR), and (iii) Transplanted Rice (TR) using drone imagery. The drone imagery was collected from an experimental field at Praanadhaara Organised Agro Forestry Private Limited, Bapatla District, Andhra Pradesh, India. The images were captured in the visible spectrum (Red, Green, and Blue bands) on three dates, viz., 15 October 2023, 27 October 2023, and 01 December 2023, from an altitude of 40 m and were used to train and test classification models. Classification was performed using six ResNet-50 based hybrid models, namely, ResNet-50+K-Nearest Neighbor (ResNet-50+KNN), ResNet-50+Support Vector Machine (ResNet-50+SVM), ResNet-50+Decision Trees (ResNet-50+DT), ResNet-50+Random Forest (ResNet-50+RF), ResNet-50+Naïve Bayes (ResNet-50+NB), and ResNet-50+Neural Network (ResNet-50+NN), along with two additional DL architectures, namely, You Only Look Once version 5 (YOLOv5) and You Only Look Once version 8 (YOLOv8). Model performance was evaluated using overall accuracy (OA), precision (P), recall (R), kappa coefficient (K), F1-score (F1), and mean Average Precision (mAP). Among the tested classifiers, the ResNet-50+NN model consistently achieved the highest average overall accuracies of 93.16%, 95.81%, and 93.31% at T 1 , T 2 , and T 3 , respectively, outperforming all other models, whose accuracies ranged from 78.05% to 92.07%, 86.11%–95.24%, and 77.47%–92.09% across the respective time intervals. The ResNet-50+NN model also recorded the highest precision (0.93–0.97), recall (0.92–0.97), F1-score (0.92–0.97), and kappa coefficient (0.89–0.96), demonstrating superior and consistent classification performance across all observation dates. The methodology developed in this work enables precise identification of rice establishment methods, improving crop mapping and monitoring. This identification enhances resource efficiency, optimizes input use, and supports site-specific management, contributing to sustainable precision agriculture.","利用分类技术对无人机影像进行作物面积精确估算，在精准农业中发挥着重要作用。传统机器学习（ML）方法已广泛用于农业图像分类；然而，先进的深度学习（DL）模型通常对复杂作物模式具有更优越的特征提取和分类能力。区分不同的水稻种植方式对于精确估算面积至关重要。本研究采用先进的深度学习模型，利用无人机影像对三种水稻种植类型进行分类：（i）撒播直播稻（DSR），（ii）湿润直播稻（Wet DSR），以及（iii）移栽稻（TR）。无人机影像采集自印度安得拉邦巴帕特拉县Praanadhaara Organised Agro Forestry Private Limited的试验田。图像在可见光谱（红、绿、蓝波段）下于三个日期拍摄，即2023年10月15日、2023年10月27日和2023年12月1日，飞行高度为40 m，用于训练和测试分类模型。分类采用六种基于ResNet-50的混合模型，即ResNet-50+K近邻（ResNet-50+KNN）、ResNet-50+支持向量机（ResNet-50+SVM）、ResNet-50+决策树（ResNet-50+DT）、ResNet-50+随机森林（ResNet-50+RF）、ResNet-50+朴素贝叶斯（ResNet-50+NB）和ResNet-50+神经网络（ResNet-50+NN），以及两种额外的深度学习架构，即You Only Look Once第5版（YOLOv5）和You Only Look Once第8版（YOLOv8）。采用总体精度（OA）、精确率（P）、召回率（R）、Kappa系数（K）、F1分数（F1）和平均精度均值（mAP）评估模型性能。在测试的分类器中，ResNet-50+NN模型在T₁、T₂和T₃分别持续取得最高的平均总体精度，为93.16%、95.81%和93.31%，优于所有其他模型，后者的精度在相应时间段分别为78.05%–92.07%、86.11%–95.24%和77.47%–92.09%。ResNet-50+NN模型还记录了最高的精确率（0.93–0.97）、召回率（0.92–0.97）、F1分数（0.92–0.97）和Kappa系数（0.89–0.96），在所有观测日期均表现出优越且稳定的分类性能。本研究开发的方法能够精确识别水稻种植方式，改进作物制图和监测。这种识别增强了资源","Frontiers in Remote Sensing","2026-09-14T00:00:00Z",{"impact":164,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":165},16,"基于无人机可见光影像与ResNet-50混合模型区分三类水稻种植方式，方法对比系统、精度数据扎实，对精准农业作物制图有参考价值，但属细分技术进展，影响范围有限。",[167],{"name":161,"url":158},[27,28,35,29,169],"遥感",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[172],{"code":57,"slug":173,"name":174,"path":175,"parent_code":176},"agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",{"status":40,"locked":14,"version":41,"method":42,"evidence":178},{"candidates":179,"agricultural_evidence":46},[180,187,191,196],{"code":57,"score":181,"supported":46,"title":182,"abstract":184,"tags":186},18,[183],"deep learning",[185,61,62,183],"深度学习",[28],{"code":51,"score":164,"supported":46,"title":188,"abstract":189,"tags":190},[54],[29,54],[27,29],{"code":33,"score":192,"supported":46,"title":193,"abstract":194,"tags":195},15,[34],[35,34],[35],{"code":65,"score":22,"supported":14,"title":197,"abstract":198,"tags":199},[],[],[169],[201,202],"农业人工智能 智慧农业 精准农业 水稻","农业人工智能 智慧农业","农业人工智能智慧农业精准农业水稻-2512","10.3389\u002Ffrsen.2026.1698781",{"doi":204,"openalex_id":206,"authors":207,"venue":161,"cited_by_count":73,"oa_url":233,"card":234,"direction":239,"ingested_from":94},"W7212834743",[208,210,212,214,216,219,221,223,225,228,230],{"name":209,"orcid":9},"Amrutha Lakshmi Gubbala",{"name":211,"orcid":9},"Santosha Rathod",{"name":213,"orcid":9},"Ramesh Dasyam",{"name":215,"orcid":9},"Mahender Kumar Rapolu",{"name":217,"orcid":218},"Arun Kumar Dasari","https:\u002F\u002Forcid.org\u002F0000-0001-7398-8601",{"name":220,"orcid":9},"Pundarikakshudu Kurra",{"name":222,"orcid":9},"Hanuma Raviteja Madireddy",{"name":224,"orcid":9},"Prajwal R. Shashishekhar",{"name":226,"orcid":227},"Ravi V. Mural","https:\u002F\u002Forcid.org\u002F0000-0002-5489-9918",{"name":229,"orcid":9},"Anil Kumar",{"name":231,"orcid":232},"R. M. Sundaram","https:\u002F\u002Forcid.org\u002F0000-0002-9857-8251","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fremote-sensing\u002Farticles\u002F10.3389\u002Ffrsen.2026.1698781\u002Fpdf",{"tldr":235,"method":236,"finding":237,"direction":91,"opportunity":238},"用无人机可见光影像和深度学习模型区分直播稻、湿直播稻与移栽稻三种水稻种植方式。","无人机RGB影像三个时相，比较ResNet-50混合模型与YOLOv5\u002Fv8分类","ResNet-50+NN表现最优，三期总体精度达93.16%、95.81%、93.31%，优于其他模","可扩展到多光谱\u002F多时相与更大区域验证，并用于种植方式制图与面积精准估算。","智慧农业 \u002F 农业物联网","2026-09-15T23:30:08.600535Z",{"id":242,"title":243,"url":244,"summary":245,"summary_zh":246,"content":9,"source_name":247,"source_url":244,"published_at":248,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":249,"score_detail":250,"sources":254,"tags":256,"primary_topic":257,"secondary_topics":258,"classification":260,"search_phrases":279,"slug":281,"view_count":130,"doi":282,"paper":283,"created_at":309},1966,"Knowledge-guided deep learning improves estimation of rice aboveground biomass","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112398","Knowledge-guided deep learning improves estimation of rice aboveground biomass。Computers and Electronics in Agriculture","知识引导的深度学习提升了水稻地上生物量估算精度。《农业计算机与电子》","Computers and Electronics in Agriculture","2026-09-08T00:00:00Z",64,{"impact":192,"substance":251,"depth":192,"authority":252,"freshness":142,"relevant":22,"comment":253},20,12,"知识引导深度学习提升水稻生物量估算，方法新颖，对精准农业有参考价值。",[255],{"name":247,"url":244},[27,28,35,30],{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[259],{"code":57,"slug":173,"name":174,"path":175,"parent_code":176},{"status":40,"locked":14,"version":41,"method":42,"evidence":261},{"candidates":262,"agricultural_evidence":46},[263,267,271,275],{"code":33,"score":192,"supported":46,"title":264,"abstract":265,"tags":266},[34],[35,34],[35],{"code":57,"score":192,"supported":46,"title":268,"abstract":269,"tags":270},[183],[185,183],[28],{"code":51,"score":22,"supported":14,"title":272,"abstract":273,"tags":274},[],[],[27],{"code":65,"score":22,"supported":14,"title":276,"abstract":277,"tags":278},[],[],[30],[280,202],"农业人工智能 智慧农业 遥感估产 水稻","农业人工智能智慧农业遥感估产水稻-1966","10.1016\u002Fj.compag.2026.112398",{"doi":282,"openalex_id":284,"authors":285,"venue":247,"cited_by_count":73,"oa_url":9,"card":304,"direction":91,"ingested_from":94},"W7211953860",[286,288,291,294,297,299,301],{"name":287,"orcid":9},"Qiyu Tian",{"name":289,"orcid":290},"Renhai Zhong","https:\u002F\u002Forcid.org\u002F0000-0001-8922-0138",{"name":292,"orcid":293},"Xingguo Xiong","https:\u002F\u002Forcid.org\u002F0000-0001-8635-8764",{"name":295,"orcid":296},"Hao Jiang","https:\u002F\u002Forcid.org\u002F0000-0002-5122-0412",{"name":298,"orcid":9},"Jingfeng Huang",{"name":300,"orcid":9},"Zhenhong Du",{"name":302,"orcid":303},"Tao Lin","https:\u002F\u002Forcid.org\u002F0000-0001-9721-5363",{"tldr":305,"method":306,"finding":307,"direction":91,"opportunity":308},"提出知识引导的深度学习模型，提高水稻地上生物量估算精度。","结合作物生长模型与深度学习，利用遥感数据估算生物量。","知识引导的深度学习显著优于传统方法，估算精度更高。","可探索将更多农学知识融入深度学习，提升其他作物性状估算的泛化能力。","2026-09-09T23:30:01.433974Z",{"id":311,"title":312,"url":313,"summary":314,"summary_zh":315,"content":9,"source_name":316,"source_url":313,"published_at":317,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":318,"score_detail":319,"sources":321,"tags":323,"primary_topic":325,"secondary_topics":326,"classification":331,"search_phrases":363,"slug":366,"view_count":73,"doi":367,"paper":368,"created_at":381},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",78,{"impact":181,"substance":110,"depth":181,"authority":252,"freshness":17,"relevant":22,"comment":320},"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。",[322],{"name":316,"url":313},[27,28,35,324,169],"产量预测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[327],{"code":65,"slug":328,"name":329,"path":330,"parent_code":176},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":40,"locked":14,"version":41,"method":42,"evidence":332},{"candidates":333,"agricultural_evidence":46},[334,338,343,349,355,359],{"code":33,"score":192,"supported":46,"title":335,"abstract":336,"tags":337},[34],[35,34],[35],{"code":65,"score":17,"supported":46,"title":339,"abstract":340,"tags":342},[],[169,341],"remote sensing",[324,169],{"code":129,"score":97,"supported":46,"title":344,"abstract":345,"tags":348},[],[346,347],"粮食安全","food security",[],{"code":350,"score":130,"supported":46,"title":351,"abstract":352,"tags":354},"J1",[],[353],"农业规划",[],{"code":51,"score":22,"supported":14,"title":356,"abstract":357,"tags":358},[],[],[27],{"code":57,"score":22,"supported":14,"title":360,"abstract":361,"tags":362},[],[],[28],[364,365],"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":367,"openalex_id":369,"authors":370,"venue":316,"cited_by_count":73,"oa_url":313,"card":376,"direction":91,"ingested_from":94},"W7222942064",[371,373],{"name":372,"orcid":9},"Nayana Venkatesh",{"name":374,"orcid":375},"Evangelin Geetha Devaraj","https:\u002F\u002Forcid.org\u002F0000-0002-7253-4314",{"tldr":377,"method":378,"finding":379,"direction":91,"opportunity":380},"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":383,"title":384,"url":385,"summary":386,"summary_zh":387,"content":9,"source_name":388,"source_url":385,"published_at":389,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":390,"score_detail":391,"sources":393,"tags":395,"primary_topic":398,"secondary_topics":399,"classification":400,"search_phrases":441,"slug":444,"view_count":73,"doi":445,"paper":446,"created_at":458},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":181,"substance":110,"depth":181,"authority":20,"freshness":21,"relevant":22,"comment":392},"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。",[394],{"name":388,"url":385},[27,28,35,396,397],"数字孪生","病虫害预警",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[],{"status":40,"locked":14,"version":41,"method":42,"evidence":401},{"candidates":402,"agricultural_evidence":46},[403,409,414,421,428,435],{"code":51,"score":404,"supported":46,"title":405,"abstract":407,"tags":408},19,[406],"digital twin",[29,396,54,406],[396,27],{"code":33,"score":181,"supported":46,"title":410,"abstract":412,"tags":413},[411],"paddy",[35,124,34,411],[35],{"code":415,"score":97,"supported":46,"title":416,"abstract":417,"tags":420},"F4",[],[418,419],"农药","pesticide",[],{"code":422,"score":97,"supported":46,"title":423,"abstract":424,"tags":427},"H3",[],[425,426],"灌溉","irrigation",[],{"code":429,"score":97,"supported":46,"title":430,"abstract":431,"tags":434},"I5",[],[432,433],"物联网","IoT",[],{"code":57,"score":436,"supported":46,"title":437,"abstract":438,"tags":440},4,[],[439],"decision support",[28],[442,443],"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":445,"openalex_id":447,"authors":448,"venue":388,"cited_by_count":73,"oa_url":9,"card":453,"direction":239,"ingested_from":94},"W7222969403",[449,451],{"name":450,"orcid":9},"Jyoti Rani",{"name":452,"orcid":9},"Dr.  Narendra S.  Jadhav",{"tldr":454,"method":455,"finding":456,"direction":93,"opportunity":457},"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。","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":460,"title":461,"url":462,"summary":463,"summary_zh":464,"content":9,"source_name":465,"source_url":462,"published_at":466,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":467,"score_detail":468,"sources":471,"tags":473,"primary_topic":477,"secondary_topics":478,"classification":480,"search_phrases":500,"slug":503,"view_count":22,"doi":504,"paper":505,"created_at":530},4993,"Effects of within-season temporal aggregation of weather variables on performance and interpretability in rice yield machine learning models","https:\u002F\u002Fdoi.org\u002F10.1038\u002Fs41598-026-75646-2","While crop responses are inherently stage-dependent, machine learning yield models often rely on weather variables aggregated over the entire growing season. This study evaluates how within-season temporal aggregation of weather variables affects both predictive performance and interpretability in machine learning-based rice yield modeling. Using CYGMA (Crop Yield Growth Model with Assumptions on Climate and Socioeconomics)-simulated potential yield as a synthetic response variable, we minimized confounding effects from observational noise and management variability, enabling a clearer evaluation of the role of within-season weather aggregation. Daily weather variables were averaged over the rice growing period after dividing each season into multiple equal-length segments, and several machine learning algorithms were trained and evaluated using temporally independent validation. Increasing within-season temporal resolution improved model performance up to a moderate level, after which gains diminished. Interpretation using SHAP (SHapley Additive exPlanations) and response diagnostics consistently indicated that yield sensitivity concentrates in specific windows of the season, with temperature-related effects emerging most strongly early in the season and humidity-related effects later. Finer temporal aggregation sharpened the identification of window-specific nonlinear and threshold-like responses that were obscured under coarse seasonal averages. Overall, moderate within-season aggregation provides a practical balance between predictive skill and mechanistic interpretability, supporting more reliable identification of phenology-relevant meteorological risks for climate-impact assessment and decision support.","尽管作物响应本质上具有阶段依赖性，但机器学习产量模型往往依赖于整个生长季聚合的天气变量。本研究评估了天气变量的季节内时间聚合如何影响基于机器学习的水稻产量建模的预测性能与可解释性。以CYGMA（Crop Yield Growth Model with Assumptions on Climate and Socioeconomics，气候与社会经济假设下的作物产量生长模型）模拟的潜在产量作为合成响应变量，我们最大限度地减少了观测噪声和管理变异性带来的混杂效应，从而能够更清晰地评估季节内天气聚合的作用。将每个生长季划分为多个等长时段后，对水稻生长期内的逐日天气变量进行平均，并采用时间独立验证对多种机器学习算法进行训练和评估。提高季节内时间分辨率可提升模型性能，直至达到中等水平，此后增益递减。利用SHAP（SHapley Additive exPlanations，沙普利加性解释）和响应诊断进行的解释一致表明，产量敏感性集中在季节的特定窗口，温度相关效应在季节早期最为显著，湿度相关效应则在后期出现。更精细的时间聚合增强了对窗口特异性非线性和阈值型响应的识别，而这些响应在粗粒度季节平均下被掩盖。总体而言，中等程度的季节内聚合在预测能力与机制可解释性之间提供了实用平衡，有助于更可靠地识别与物候相关的气象风险，以支持气候影响评估和决策支持。","Scientific Reports","2026-10-13T00:00:00Z",72,{"impact":252,"substance":251,"depth":181,"authority":469,"freshness":17,"relevant":22,"comment":470},14,"该研究通过CYGMA模拟数据系统评估了生长季内气象变量时间聚合对水稻产量机器学习模型性能与可解释性的影响，方法新颖、结论可靠，对农业气象风险识别与决策支持有参考价值，但属学术论文，公共影响范围有限。",[472],{"name":465,"url":462},[27,28,474,475,476],"可解释AI","农业气象","水稻产量预测",{"code":33,"slug":34,"name":35,"path":36,"parent_code":37},[479],{"code":57,"slug":173,"name":174,"path":175,"parent_code":176},{"status":40,"locked":14,"version":41,"method":42,"evidence":481},{"candidates":482,"agricultural_evidence":46},[483,487,491,496],{"code":57,"score":404,"supported":46,"title":484,"abstract":485,"tags":486},[62],[61,62,439],[28,474],{"code":33,"score":469,"supported":46,"title":488,"abstract":489,"tags":490},[34],[35,34],[],{"code":492,"score":22,"supported":14,"title":493,"abstract":494,"tags":495},"H7",[],[],[475],{"code":51,"score":22,"supported":14,"title":497,"abstract":498,"tags":499},[],[],[27],[501,502],"水稻产量 机器学习 气象","SHAP 水稻 产量预测","水稻产量机器学习气象-4993","10.1038\u002Fs41598-026-75646-2",{"doi":504,"openalex_id":506,"authors":507,"venue":465,"cited_by_count":73,"oa_url":524,"card":525,"direction":239,"ingested_from":94},"W7221657053",[508,510,512,514,516,518,521],{"name":509,"orcid":9},"Noh-Hyun Lee",{"name":511,"orcid":9},"Wonjae Jeong",{"name":513,"orcid":9},"Hyeon-Ji Yang",{"name":515,"orcid":9},"Hana Cho",{"name":517,"orcid":9},"Hyeon-Yeong Ju",{"name":519,"orcid":520},"Toshichika Iizumi","https:\u002F\u002Forcid.org\u002F0000-0002-0611-4637",{"name":522,"orcid":523},"Ki‐Tae Kim","https:\u002F\u002Forcid.org\u002F0000-0002-8249-5283","https:\u002F\u002Fwww.nature.com\u002Farticles\u002Fs41598-026-75646-2_reference.pdf",{"tldr":526,"method":527,"finding":528,"direction":93,"opportunity":529},"评估生长季内天气变量时间聚合粒度对水稻产量机器学习模型预测与可解释性的影响。","用CYGMA模拟潜在产量作响应变量，按等长时段聚合逐日天气，训练多种机器学习模型","适度提高季内时间分辨率可提升性能，产量敏感性集中在特定窗口，温度效应偏早、湿度效应偏晚。","可探索不同作物与气候区下最优聚合粒度，并将窗口特异性气象风险嵌入气候影响评估与决策支持系统。","2026-10-15T23:30:10.483881Z"]