[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-55":3,"related-55":70},{"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":33,"secondary_topics":39,"classification":40,"search_phrases":64,"slug":67,"view_count":68,"doi":8,"paper":8,"created_at":69},55,"卫星数据+专用视觉Transformer的甜菜早期产量预测（Sentinel-2影像应用）","https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.17661","基于Sentinel-2光学影像的早期甜菜产量预测研究。通过紧耦合领域知识与机器学习，使用极小Vision Transformer patch size与全部Sentinel-2谱段，提升了模型表现。研究通过调整训练设置与基于排名的低产田检测方法，能在生长周期早期识别出大量低产田，为精准农业田间管理提供早期决策依据。",null,"[Skip to main content](https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.17661#content)[](https:\u002F\u002Farxiv.org\u002FIgnoreMe)[![Image 1: archive](https:\u002F\u002Farxiv.org\u002Fstatic\u002Fbase\u002F1.0.1\u002Fimages\u002Farxiv-logo-primary-light.svg)](https:\u002F\u002Farxiv.org\u002F)[Search](https:\u002F\u002Farxiv.org\u002Fsearch)[Submit](https:\u002F\u002Farxiv.org\u002Fuser\u002Fcreate)[Donate](https:\u002F\u002Finfo.arxiv.org\u002Fabout\u002Fdonate.html)[Log in](https:\u002F\u002Farxiv.org\u002Flogin)\n\nSearch arXiv \n\n Press Enter to search · [Advanced search](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fadvanced)\n\n# Computer Science > Computer Vision and Pattern Recognition\n\n**arXiv:2607.17661** (cs) \n\n [Submitted on 20 Jul 2026]\n\n# Title:Early Yield Prediction for Sugar Beet Fields using Satellite Data -- Learnings from Specialized Vision Transformers\n\nAuthors:[Philipp Vaeth](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Vaeth,+P), [Bhumika Laxman Sadbhave](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Sadbhave,+B+L), [Denise Dejon](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Dejon,+D), [Gunther Schorcht](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Schorcht,+G), [Magda Gregorova](https:\u002F\u002Farxiv.org\u002Fsearch\u002Fcs?searchtype=author&query=Gregorova,+M)\n\nView a PDF of the paper titled Early Yield Prediction for Sugar Beet Fields using Satellite Data -- Learnings from Specialized Vision Transformers, by Philipp Vaeth and 4 other authors\n\n[View PDF](https:\u002F\u002Farxiv.org\u002Fpdf\u002F2607.17661)[HTML (experimental)](https:\u002F\u002Farxiv.org\u002Fhtml\u002F2607.17661v1)\n> Abstract:Remote sensing has become an increasingly valuable tool for agricultural monitoring, particularly through the use of publicly available satellite imagery. However, effectively integrating domain knowledge into machine learning methods remains challenging. This study presents a real-world example of early sugar beet harvest yield forecasting from purely optical Sentinel-2 imagery, demonstrating how a tight integration of domain knowledge and machine learning can lead to synergistic gains. We empirically find that using very small vision transformer patch sizes and all available Sentinel-2 spectral bands improves our model despite being uncommon design choices in the domain. As a practical contribution, we were able to identify a large fraction of low-yield fields in a different year early on in the growth cycle through a modified training setup and a ranking-based detection of underperforming fields.\n\nSubjects:Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)\nCite as:[arXiv:2607.17661](https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.17661) [cs.CV]\n(or [arXiv:2607.17661v1](https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.17661v1) [cs.CV] for this version)\n[https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2607.17661](https:\u002F\u002Fdoi.org\u002F10.48550\u002FarXiv.2607.17661)\n\nFocus to learn more\n\n arXiv-issued DOI via DataCite (pending registration)\n\n## Submission history\n\n From: Philipp Vaeth [[view email](https:\u002F\u002Farxiv.org\u002Fshow-email\u002Ff02ba8e5\u002F2607.17661)] \n\n**[v1]** Mon, 20 Jul 2026 08:11:07 UTC (4,067 KB)\n\n[](https:\u002F\u002Farxiv.org\u002Fabs\u002F2607.17661)Full-text links:\n## 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[**Learn more about arXivLabs**](https:\u002F\u002Finfo.arxiv.org\u002Flabs\u002Findex.html).\n\n[Which authors of this paper are endorsers?](https:\u002F\u002Farxiv.org\u002Fauth\u002Fshow-endorsers\u002F2607.17661) | [Disable MathJax](javascript:setMathjaxCookie()) ([What is MathJax?](https:\u002F\u002Finfo.arxiv.org\u002Fhelp\u002Fmathjax.html)) \n\n We gratefully acknowledge support from our **major funders**, [**member institutions**](https:\u002F\u002Finfo.arxiv.org\u002Fabout\u002Fourmembers.html), , and all contributors. \n\n[About](https:\u002F\u002Finfo.arxiv.org\u002Fabout)·[Help](https:\u002F\u002Finfo.arxiv.org\u002Fhelp)·[Contact](https:\u002F\u002Finfo.arxiv.org\u002Fhelp\u002Fcontact.html)·[Subscribe](https:\u002F\u002Finfo.arxiv.org\u002Fhelp\u002Fsubscribe)·[Copyright](https:\u002F\u002Finfo.arxiv.org\u002Fhelp\u002Flicense\u002Findex.html)·[Privacy](https:\u002F\u002Finfo.arxiv.org\u002Fhelp\u002Fpolicies\u002Fprivacy_policy.html)·[Accessibility](https:\u002F\u002Finfo.arxiv.org\u002Fhelp\u002Fweb_accessibility.html)·[Operational Status (opens in new tab)](https:\u002F\u002Fstatus.arxiv.org\u002F)\n\nMajor funding support from\n\n[![Image 4: Simons Foundation](https:\u002F\u002Farxiv.org\u002Fstatic\u002Fbase\u002F1.0.1\u002Fimages\u002Ffunders\u002Fsimons-foundation.png)](https:\u002F\u002Fwww.simonsfoundation.org\u002F)[![Image 5: Simons Foundation International](https:\u002F\u002Farxiv.org\u002Fstatic\u002Fbase\u002F1.0.1\u002Fimages\u002Ffunders\u002Fsimons-foundation-international.png)](https:\u002F\u002Fwww.sfi.org.bm\u002F)[![Image 6: Schmidt Sciences](https:\u002F\u002Farxiv.org\u002Fstatic\u002Fbase\u002F1.0.1\u002Fimages\u002Ffunders\u002Fschmidt-sciences.png)](https:\u002F\u002Fwww.schmidtsciences.org\u002F)","arXiv:2607.17661v1 [cs.CV] · 2026-07-20","2026-07-20T00:11:00Z","论文",10,false,76,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},20,22,18,12,4,1,"利用Sentinel-2卫星影像和专用视觉Transformer进行甜菜早期产量预测，方法新颖，有实际应用价值，但发表于8天前，时效性较低。",[25],{"name":10,"url":6},[27,12,28,29,30,31,32],"农业遥感","甜菜","产量预测","Sentinel-2","视觉Transformer","预印本",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":41,"locked":14,"version":42,"method":43,"evidence":44},"classified","2026-10-22.2","rules",{"candidates":45,"agricultural_evidence":49},[46,54,59],{"code":47,"score":48,"supported":49,"title":50,"abstract":51,"tags":53},"I1",3,true,[],[52],"精准农业",[],{"code":34,"score":48,"supported":49,"title":55,"abstract":56,"tags":58},[],[57],"机器学习",[],{"code":60,"score":48,"supported":14,"title":61,"abstract":62,"tags":63},"I3",[],[],[30,29,27],[65,66],"产量预测 农业遥感 预印本 视觉Transformer","产量预测 农业遥感","产量预测农业遥感预印本视觉Transformer-55",14,"2026-07-28T23:53:31.313063Z",{"total":71,"page":22,"page_size":71,"items":72},6,[73,136,189,264,328,429],{"id":74,"title":75,"url":76,"summary":77,"summary_zh":78,"content":8,"source_name":79,"source_url":76,"published_at":80,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":81,"score_detail":82,"sources":86,"tags":88,"primary_topic":93,"secondary_topics":94,"classification":95,"search_phrases":113,"slug":116,"view_count":117,"doi":118,"paper":119,"created_at":135},5587,"Calibration-Shift Inference for Computer Vision Agriculture Yield Models Under Retrofit Sensor Replacement: An Artificial Intelligence Post-Deployment Recalibration Guarantee","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23544195","Computer vision yield models deployed in agricultural fields are routinely paired with retrofit sensor packages that replace or supplement the original imaging hardware, and the resulting calibration shift can silently invalidate the model's predictive guarantees. The analysis characterises the operating characteristics of the design under a declared variance structure with between-cluster standard deviation 6.0, residual standard deviation 4.5 and intraclass correlation 0.64, using 2000 replications per design cell across cluster counts of 8, 16, 24 and 32 and effect sizes of 3, 5 and 8 percentage points. The design attains 80% power at a 5 percentage point effect with 54 clusters. The paper states a formal guarantee: under stated assumptions on the stability of the calibration map, the before-after-control-impact estimator yields a consistent and asymptotically normal estimate of the calibration shift, and the associated confidence interval covers the true shift at the nominal rate. The contribution is a design-theoretic account of how retrofit-induced calibration shift can be detected and bounded without observational data, together with the sample-size requirements that follow.","部署在农田中的计算机视觉产量模型通常与改装传感器组件配合使用，这些组件用于替换或补充原始成像硬件，由此产生的校准偏移可能悄然使模型的预测保证失效。该分析在声明的方差结构下刻画了设计的工作特性，其中组间标准差为6.0、残差标准差为4.5、组内相关系数为0.64，每个设计单元采用2000次重复，聚类数分别为8、16、24和32，效应量为3、5和8个百分点。该设计在54个聚类、5个百分点效应下达到80%的检验力。论文给出了一项形式化保证：在关于校准映射稳定性的既定假设下，前后对照影响（before-after-control-impact）估计量给出校准偏移的一致且渐近正态的估计，且相关置信区间以名义覆盖率覆盖真实偏移。其贡献在于从设计理论角度说明了如何在没有观测数据的情况下检测并界定改装引起的校准偏移，以及随之而来的样本量要求。","Zenodo (CERN European Organization for Nuclear Research)","2026-10-21T00:00:00Z",65,{"impact":83,"substance":19,"depth":84,"authority":20,"freshness":13,"relevant":22,"comment":85},8,17,"方法学扎实、统计设计严谨的预印本论文，但偏理论设计层面，缺乏田间实证数据，产业影响有限，适合作为技术参考而非每日精选头条。",[87],{"name":79,"url":76},[89,90,91,29,92],"智慧农业","农业人工智能","计算机视觉","传感器校准",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":96},{"candidates":97,"agricultural_evidence":49},[98,105,109],{"code":34,"score":99,"supported":49,"title":100,"abstract":103,"tags":104},24,[101,102],"artificial intelligence","computer vision",[91,102],[90,91],{"code":47,"score":22,"supported":14,"title":106,"abstract":107,"tags":108},[],[],[89],{"code":60,"score":22,"supported":14,"title":110,"abstract":111,"tags":112},[],[],[29],[114,115],"计算机视觉 产量预测 传感器校准","农业人工智能 传感器校准 计算机视觉 产量预测","计算机视觉产量预测传感器校准-5587",0,"10.5281\u002Fzenodo.23544195",{"doi":118,"openalex_id":120,"authors":121,"venue":79,"cited_by_count":117,"oa_url":76,"card":128,"direction":132,"ingested_from":134},"W7223104380",[122,124,126],{"name":123,"orcid":8},"Tigist Alemu Gebremariam",{"name":125,"orcid":8},"Hawi Tesfaye Desta",{"name":127,"orcid":8},"Mulugeta Bekele Wolde",{"tldr":129,"method":130,"finding":131,"direction":132,"opportunity":133},"提出改造传感器替换后视觉产量模型校准漂移的检测与置信区间保证方法。","用前后对照影响估计量，基于方差结构做2000次重复的模拟设计分析。","5个百分点效应下54个集群可达80%检验力，估计量一致且渐近正态。","农业人工智能与决策模型","可将该设计理论扩展到多传感器异构替换与在线校准监测，填补真实田间验证空白。","openalex","2026-10-22T11:21:01.554995Z",{"id":137,"title":138,"url":139,"summary":140,"summary_zh":141,"content":8,"source_name":79,"source_url":139,"published_at":80,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":142,"score_detail":143,"sources":145,"tags":147,"primary_topic":150,"secondary_topics":151,"classification":156,"search_phrases":172,"slug":175,"view_count":117,"doi":176,"paper":177,"created_at":188},5570,"Evaluation of Counterfactual Explanation Robustness for Computer Vision Agriculture Yield-Forecast Models Under Satellite Cloud-Mask Gaps: An Interpretability Reliability Bound for Cooperative Storage Decisions","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23545079","Counterfactual explanations are increasingly proposed as the interface through which cooperative storage managers interrogate computer-vision yield-forecast models, yet their reliability under the satellite cloud-mask gaps that routinely afflict agricultural remote sensing has not been bounded. The bound is derived from a Lipschitz continuity assumption on the forecast model and a masking-operator perturbation analysis, and it is expressed as a function of the cloud-mask gap fraction, the model's local Lipschitz constant, and the counterfactual search tolerance. A replication-based evaluation of the before-after-control-impact estimator was conducted to characterise the operating characteristics of the design under a declared variance structure (between-cluster SD = 6.0, residual SD = 4.5, intraclass correlation = 0.64), with power estimated across 2000 design cells. The reliability bound degrades non-linearly as the gap fraction rises, and that the intraclass correlation of 0.64 implies that cooperative-level aggregation of counterfactual recourse is necessary to preserve explanation stability. The contribution is a decision-relevant reliability bound that storage cooperatives can use to set a maximum tolerable cloud-mask gap before counterfactual recourse is suspended.","反事实解释日益被提出作为合作式仓储管理者质询计算机视觉产量预测模型的接口，然而在农业遥感中经常出现的卫星云掩膜缺口条件下，其可靠性尚未得到界定。该界基于预测模型上的Lipschitz连续性假设和掩膜算子扰动分析推导得出，并表示为云掩膜缺口比例、模型局部Lipschitz常数和反事实搜索容差的函数。对前后对照影响估计量进行了基于重复的评估，以表征该设计在给定方差结构（组间标准差=6.0，残差标准差=4.5，组内相关系数=0.64）下的运行特征，并在2000个设计单元中估计了检验效能。可靠性界随缺口比例上升呈非线性退化，且组内相关系数0.64意味着需要对反事实救济进行合作社层面的聚合，以保持解释的稳定性。本文的贡献在于提供了一个与决策相关的可靠性界，仓储合作社可据此设定在暂停反事实救济之前可容忍的最大云掩膜缺口。",67,{"impact":83,"substance":17,"depth":84,"authority":20,"freshness":13,"relevant":22,"comment":144},"该论文提出反事实解释在卫星云掩膜缺口下的可靠性边界，方法新颖且结论可用于合作社决策，但属细分方法学研究，产业影响有限。",[146],{"name":79,"url":139},[89,90,29,148,149],"遥感","可解释性",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[152],{"code":60,"slug":153,"name":154,"path":155,"parent_code":38},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":41,"locked":14,"version":42,"method":43,"evidence":157},{"candidates":158,"agricultural_evidence":49},[159,163,168],{"code":34,"score":20,"supported":49,"title":160,"abstract":161,"tags":162},[102],[91],[90],{"code":60,"score":83,"supported":49,"title":164,"abstract":165,"tags":167},[],[148,166],"remote sensing",[29,148],{"code":47,"score":22,"supported":14,"title":169,"abstract":170,"tags":171},[],[],[89],[173,174],"农业人工智能 产量预测 可解释性 智慧农业","农业人工智能 产量预测","农业人工智能产量预测可解释性智慧农业-5570","10.5281\u002Fzenodo.23545079",{"doi":176,"openalex_id":178,"authors":179,"venue":79,"cited_by_count":117,"oa_url":139,"card":182,"direction":186,"ingested_from":134},"W7223114002",[180],{"name":181,"orcid":8},"Kadiatou Barry",{"tldr":183,"method":184,"finding":185,"direction":186,"opportunity":187},"推导卫星云掩膜缺口下反事实解释可靠性的理论界，并评估其对合作社储粮决策的影响。","基于Lipschitz连续性假设与掩膜扰动分析推导可靠性界，并用2000个设计单","缺口比例上升使可靠性非线性下降；ICC=0.64表明需合作社层级聚合以保持解释稳定。","农业遥感与作物表型","可研究不同作物区云掩膜统计特性下的自适应解释暂停阈值与聚合策略。","2026-10-22T11:20:39.240791Z",{"id":190,"title":191,"url":192,"summary":193,"summary_zh":194,"content":8,"source_name":195,"source_url":192,"published_at":196,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":197,"score_detail":198,"sources":202,"tags":204,"primary_topic":208,"secondary_topics":209,"classification":211,"search_phrases":236,"slug":239,"view_count":22,"doi":240,"paper":241,"created_at":263},5165,"Proximal Remote Sensing of Oil Palm Basal Stem Rot Using Deep Learning Vision Algorithms and Ground-based Images","https:\u002F\u002Fdoi.org\u002F10.22266\u002Fijies2026.1130.31","Basal Stem Rot (BSR), caused by Ganoderma boninense, threatens oil palm productivity in Southeast Asia.This study develops and benchmarks a low-cost remote sensing framework for BSR severity diagnosis from smartphone-captured RGB trunk images, evaluating two pathways for future field deployment.The first pairs YOLO detection with a Random Forest classifier: YOLO26n gave the best accuracy-to-cost trade-off among three nanovariants for localizing BSR symptoms (mushroom, rot, white-button), and the end-to-end YOLO26n+RF pipeline achieved 0.83 accuracy and macro-F1 of 0.79 across Healthy, Mild, Moderate, and Severe classes.Rot was consistently the hardest symptom to localize, owing to its diffuse, boundary-ambiguous appearance.The second pathway trained four image classifiers (EfficientNet-B0, MobileNetV2, Swin-Tiny, and ConvNeXt-V2-Tiny) directly on severity labels, with ConvNeXt-V2-Tiny performing best (0.83 accuracy, 0.83 macro-F1).Both pathways performed well at severity extremes but struggled with Moderate, reflecting visual overlap between stages.SHAP analysis identified rot-area ratio and fruiting-body count as top predictors.These offline results show accurate BSR diagnosis is potentially achievable from smartphone imagery, laying groundwork for future on-device deployment.","由灵芝（Ganoderma boninense）引起的基茎腐病（Basal Stem Rot, BSR）威胁着东南亚油棕的生产力。本研究开发并基准测试了一个低成本遥感框架，用于从智能手机拍摄的RGB树干图像中诊断基茎腐病的严重程度，并评估了未来田间部署的两条技术路径。第一条路径将YOLO检测与随机森林分类器相结合：在三种纳米变体中，YOLO26n在定位基茎腐病症状（蘑菇、腐烂、白钮）方面实现了最佳的精度-成本权衡，端到端的YOLO26n+RF流程在健康、轻度、中度和重度四个类别上达到了0.83的准确率和0.79的宏F1值。腐烂始终是最难定位的症状，原因在于其弥散且边界模糊的外观。第二条路径直接在严重程度标签上训练了四个图像分类器（EfficientNet-B0、MobileNetV2、Swin-Tiny和ConvNeXt-V2-Tiny），其中ConvNeXt-V2-Tiny表现最佳（准确率0.83，宏F1值0.83）。两条路径在严重程度极端类别上表现良好，但在中度类别上表现欠佳，反映出各阶段之间存在视觉重叠。SHAP分析确定腐烂面积比和子实体数量为最重要的预测因子。这些离线结果表明，利用智能手机图像有望实现准确的基茎腐病诊断，为未来设备端部署奠定了基础。","International journal of intelligent engineering and systems","2026-10-16T00:00:00Z",69,{"impact":20,"substance":17,"depth":199,"authority":20,"freshness":200,"relevant":22,"comment":201},16,9,"该研究利用智能手机图像与深度学习实现油棕基腐病严重度诊断，方法新颖、结论可靠，但属细分作物研究，产业影响有限，适合作为技术类精选。",[203],{"name":195,"url":192},[89,27,205,206,207],"深度学习","油棕","病害诊断",{"code":60,"slug":153,"name":154,"path":155,"parent_code":38},[210],{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},{"status":41,"locked":14,"version":42,"method":43,"evidence":212},{"candidates":213,"agricultural_evidence":49},[214,219,225,232],{"code":60,"score":215,"supported":49,"title":216,"abstract":217,"tags":218},15,[166],[148,166],[27],{"code":34,"score":20,"supported":49,"title":220,"abstract":222,"tags":224},[221],"deep learning",[223],"YOLO",[205],{"code":226,"score":71,"supported":49,"title":227,"abstract":228,"tags":231},"B5",[],[229,230],"蘑菇","mushroom",[],{"code":47,"score":22,"supported":14,"title":233,"abstract":234,"tags":235},[],[],[89],[237,238],"油棕 基腐病 遥感","Ganoderma boninense 智能手机 检测","油棕基腐病遥感-5165","10.22266\u002Fijies2026.1130.31",{"doi":240,"openalex_id":242,"authors":243,"venue":195,"cited_by_count":117,"oa_url":192,"card":258,"direction":186,"ingested_from":134},"W7222626135",[244,247,250,252,255],{"name":245,"orcid":246},"Wan Siu Hong","https:\u002F\u002Forcid.org\u002F0000-0002-8254-4962",{"name":248,"orcid":249},"Jonathan Yong Chung Ee","https:\u002F\u002Forcid.org\u002F0000-0003-4079-8005",{"name":251,"orcid":8},"Jin Yuan Chan",{"name":253,"orcid":254},"Mohamad Izzuddin Anuar","https:\u002F\u002Forcid.org\u002F0000-0002-4929-5175",{"name":256,"orcid":257},"Mahmud Iwan Solihin","https:\u002F\u002Forcid.org\u002F0000-0002-5293-7466",{"tldr":259,"method":260,"finding":261,"direction":186,"opportunity":262},"用手机拍摄油棕树干图像，结合深度学习检测与分类，实现基腐病严重度诊断。","YOLO26n+随机森林与四种图像分类器，基于手机RGB图像，SHAP解释。","两条路径准确率均约0.83，中度病级最难区分，腐区比例与子实体数为关键预测因子。","可探索轻量化模型在手机端离线部署，并扩充多地区、多品种数据以提升中度病级辨识。","2026-10-17T23:30:28.307953Z",{"id":265,"title":266,"url":267,"summary":268,"summary_zh":269,"content":8,"source_name":270,"source_url":267,"published_at":271,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":272,"score_detail":273,"sources":276,"tags":278,"primary_topic":280,"secondary_topics":281,"classification":282,"search_phrases":305,"slug":308,"view_count":117,"doi":309,"paper":310,"created_at":327},4994,"Deep learning-based estimation of Kent mango yield by commercial export category under real field conditions in northern Peru","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1943711","Manual visual counting remains the standard procedure for estimating Kent mango ( Mangifera indica L.) yield in northern Peru, an inconsistent practice that compromises harvest planning, transport dimensioning and commercial negotiation. Existing computer vision approaches to mango detection resolve fruit-versus-background discrimination but rarely classify fruit into the commercial categories that determine its market value. This study developed and validated a deep learning method that simultaneously detects and classifies Kent mango fruit into three export categories (air freight, sea freight and reject) from RGB images acquired under real field conditions in the Motupe district, Lambayeque. A purpose-built dataset of 400 photographs was compiled from 200 trees across ten commercial farms using two opposite views per tree; 6,544 patches of 1,280 × 1,280 pixels with 20% overlap were generated, containing 66,465 manually annotated instances. Five architectures were trained under identical experimental conditions: YOLOv8m, YOLO11m, YOLO26m, RT-DETR-l and Mango-YOLO, the latter a proposed architecture built on YOLO26m through the incorporation of a CBAM attention module, a BiFPN neck and the MPDIoU regression loss. Mango-YOLO achieved the best performance across all five metrics, with a precision of 0.930, a recall of 0.940, an F1-score of 0.930, a mAP@0.5 of 0.940 and a mAP@0.5:0.95 of 0.730, at an inference time of 114.94 ms per image. Per-category analysis revealed heterogeneous behaviour, with F1-scores of 0.960, 0.845 and 0.792 for sea freight, air freight and reject respectively. The selected model was integrated into a three-tier web application that scales fruit counts from sampled trees to the whole farm and converts them into kilograms through Monte Carlo simulation with category mean weights, returning a 90% interval. Validation against harvested production recorded on the processing-plant scale across the ten farms yielded a mean absolute percentage error of 2.63% and a coefficient of determination of 0.9993, outperforming the field supervisor’s manual estimate by a factor of 3.3.","在秘鲁北部，人工目视计数仍是估算肯特芒果（Mangifera indica L.）产量的标准方法，但这种做法缺乏一致性，影响了采收计划、运输规模测算和商业谈判。现有的芒果检测计算机视觉方法解决了果实与背景的区分问题，但很少将果实归入决定其市场价值的商业类别。本研究开发并验证了一种深度学习方法，能够从兰巴耶克省莫图佩地区真实田间条件下采集的RGB图像中，同时检测肯特芒果并将其分为三个出口类别（空运、海运和拒收）。研究构建了一个专用数据集，来自十个商业果园的200棵果树，每棵树采集两个相对视角，共400张照片；生成了6,544个1,280 × 1,280像素、重叠率20%的图像块，包含66,465个人工标注实例。在相同实验条件下训练了五种架构：YOLOv8m、YOLO11m、YOLO26m、RT-DETR-l和Mango-YOLO，后者是在YOLO26m基础上引入CBAM注意力模块、BiFPN颈部和MPDIoU回归损失而提出的架构。Mango-YOLO在全部五项指标上表现最佳，精确率为0.930，召回率为0.940，F1分数为0.930，mAP@0.5为0.940，mAP@0.5:0.95为0.730，每张图像推理时间为114.94 ms。分类别分析显示出异质性表现，海运、空运和拒收类别的F1分数分别为0.960、0.845和0.792。所选模型被集成到一个三层Web应用程序中，可将采样果树的果实计数扩展至整个果园，并通过使用类别平均权重的蒙特卡洛模拟将其转换为千克，返回90%区间。在十个果园中，以加工厂地磅记录的采收产量进行验证，平均绝对百分比误差为2.63%，决定系数为0.9993，比田间主管的人工估算优3.3倍。","Frontiers in Plant Science","2026-10-13T00:00:00Z",82,{"impact":19,"substance":274,"depth":19,"authority":68,"freshness":200,"relevant":22,"comment":275},23,"深度学习实现芒果出口分级与产量估算，MAPE仅2.63%，方法新颖、数据扎实，对果园智能测产有参考价值。",[277],{"name":270,"url":267},[89,90,91,29,279],"芒果",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":283},{"candidates":284,"agricultural_evidence":49},[285,290,297,301],{"code":34,"score":286,"supported":49,"title":287,"abstract":288,"tags":289},19,[221],[205,91,221,102,223],[90,91],{"code":291,"score":292,"supported":49,"title":293,"abstract":294,"tags":296},"B2",7,[],[295,279],"果树",[279],{"code":47,"score":22,"supported":14,"title":298,"abstract":299,"tags":300},[],[],[89],{"code":60,"score":22,"supported":14,"title":302,"abstract":303,"tags":304},[],[],[29],[306,307],"秘鲁 Kent 芒果 产量预测","Mango-YOLO 芒果 分级","秘鲁Kent芒果产量预测-4994","10.3389\u002Ffpls.2026.1943711",{"doi":309,"openalex_id":311,"authors":312,"venue":270,"cited_by_count":117,"oa_url":320,"card":321,"direction":326,"ingested_from":134},"W7221685456",[313,315,318],{"name":314,"orcid":8},"Víctor Yoel Huamán Asencio",{"name":316,"orcid":317},"Heber I. Mejía-Cabrera","https:\u002F\u002Forcid.org\u002F0000-0002-0007-0928",{"name":319,"orcid":8},"Juan Carlos Arcila Diaz","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1943711\u002Fpdf",{"tldr":322,"method":323,"finding":324,"direction":132,"opportunity":325},"开发深度学习模型在真实田间条件下检测并分类肯特芒果为三个出口等级，实现产量估计。","构建400张田间RGB图像数据集，提出Mango-YOLO（YOLO26m+CB","Mango-YOLO最优，mAP@0.5达0.940，产量估计MAPE为2.63%，比人工估计准确3","可探索多品种、多生长阶段及不同光照条件下的泛化能力，并融合时序遥感数据提升区域产量预测精度。","智慧农业 \u002F 农业物联网","2026-10-15T23:30:11.559035Z",{"id":329,"title":330,"url":331,"summary":332,"summary_zh":333,"content":8,"source_name":270,"source_url":331,"published_at":334,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":335,"score_detail":336,"sources":338,"tags":340,"primary_topic":342,"secondary_topics":343,"classification":349,"search_phrases":402,"slug":405,"view_count":22,"doi":406,"paper":407,"created_at":428},4988,"Hierarchical cross-modal spatiotemporal attention network for crop yield prediction in complex Himalayan agroecosystems","https:\u002F\u002Fdoi.org\u002F10.3389\u002Ffpls.2026.1933033","Introduction Accurate crop yield prediction is essential for precision agriculture, climate-resilient farm management, and timely decision-making. However, yield prediction remains challenging because of complex, nonlinear, and multi-scale interactions among crop physiology, soil conditions, weather variability, and management practices. Conventional statistical, machine learning, and deep learning approaches, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and long short-term memory (LSTM) networks, often have limitations in capturing long-range temporal dependencies, hierarchical spatial relationships, heterogeneous data modalities, and predictive uncertainty. Methods This study proposes a Hierarchical Cross-Modal Spatio-Temporal Attention Network (HCM-STAN) for crop yield prediction using a unified attention-based framework for integrating heterogeneous agro-environmental data. The architecture incorporates four principal components: (i) hierarchical spatio-temporal attention operating across tree, block, orchard, and regional scales; (ii) a cross-modal attention module for adaptive fusion of IoT-based soil sensor observations, weather data, plant physiological measurements, and drone-based multispectral imagery; (iii) a soil-aware attention head that explicitly models soil memory, hysteresis effects, and root-zone dynamics; and (iv) an uncertainty-aware prediction module combining Monte Carlo dropout and quantile regression to generate calibrated prediction intervals. The framework was evaluated using a multi-source dataset collected from Himalayan apple orchards during 2023–2026, comprising 1,247 monitored trees across three commercial orchards. Results The proposed HCM-STAN achieved an R 2 of 0.89 for four-week-ahead crop yield prediction, representing a 15.6% improvement over the CNN-LSTM baseline. Furthermore, zero-shot transfer to mango orchards achieved an R 2 of 0.67, corresponding to approximately 75% of the performance obtained under full training. Discussion The results demonstrate that integrating hierarchical spatio-temporal representation learning, cross-modal attention, soil-aware modelling, and uncertainty estimation can effectively address the heterogeneous and dynamic nature of crop yield prediction in complex mountain agroecosystems. The observed zero-shot transfer performance further indicates the potential of HCM-STAN to generalize across crop systems without crop-specific retraining. Overall, the proposed framework provides a promising foundation for reliable, uncertainty-aware yield forecasting and can support precision agriculture, climate-smart farm management, and data-driven decision-making in heterogeneous agroecosystems.","引言 准确的作物产量预测对于精准农业、气候适应性农场管理以及及时决策至关重要。然而，由于作物生理、土壤条件、天气变异和管理措施之间复杂、非线性且多尺度的相互作用，产量预测仍然具有挑战性。传统的统计方法、机器学习以及深度学习方法，包括卷积神经网络（CNN）、循环神经网络（RNN）和长短期记忆（LSTM）网络，在捕捉长程时间依赖关系、层次化空间关系、异构数据模态以及预测不确定性方面往往存在局限。方法 本研究提出了一种用于作物产量预测的层次化跨模态时空注意力网络（HCM-STAN），采用统一的基于注意力的框架来整合异构农业环境数据。该架构包含四个主要组成部分：（i）在树、地块、果园和区域尺度上运行的层次化时空注意力；（ii）用于自适应融合基于物联网的土壤传感器观测、天气数据、植物生理测量和无人机多光谱影像的跨模态注意力模块；（iii）显式建模土壤记忆、滞后效应和根区动态的土壤感知注意力头；（iv）结合蒙特卡洛 dropout 和分位数回归以生成校准预测区间的不确定性感知预测模块。该框架使用2023—2026年在喜马拉雅苹果园采集的多源数据集进行了评估，该数据集包含三个商业果园中1，247棵监测树木。结果 所提出的 HCM-STAN 在提前四周作物产量预测中达到了0.89的 R²，相较于 CNN-LSTM 基线提升了15.6%。此外，零样本迁移至芒果园达到了0.67的 R²，相当于完整训练下所获性能的约75%。讨论 结果表明，整合层次化时空表征学习、跨模态注意力、土壤感知建模和不确定性估计，能够有效应对复杂山地农业生态系统中作物产量预测的异构性和动态性。观察到的零样本迁移性能进一步表明，HCM-STAN 具有在无需作物特定再训练的情况下跨作物系统泛化的潜力。总体而言，该 p","2026-10-14T00:00:00Z",81,{"impact":19,"substance":18,"depth":19,"authority":68,"freshness":200,"relevant":22,"comment":337},"提出分层跨模态时空注意力网络，融合物联网土壤传感、气象、生理与无人机多光谱数据，苹果园四周产量预测R²达0.89并实现芒果园零样本迁移，方法新颖、数据扎实，对智慧农业产量预报有参考价值。",[339],{"name":270,"url":331},[89,90,29,341,148],"多模态融合",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[344,348],{"code":291,"slug":345,"name":295,"path":346,"parent_code":347},"fruit","\u002Ftopic\u002Fhorticulture\u002Ffruit","B",{"code":60,"slug":153,"name":154,"path":155,"parent_code":38},{"status":41,"locked":14,"version":42,"method":43,"evidence":350},{"candidates":351,"agricultural_evidence":49},[352,358,365,371,376,383,390,396],{"code":34,"score":353,"supported":49,"title":354,"abstract":355,"tags":357},11,[],[205,57,356,221],"machine learning",[90,341],{"code":291,"score":200,"supported":49,"title":359,"abstract":360,"tags":364},[],[361,279,362,363],"苹果","orchard","apple",[],{"code":60,"score":83,"supported":49,"title":366,"abstract":367,"tags":370},[],[368,369],"无人机多光谱","multispectral",[29,148],{"code":47,"score":292,"supported":49,"title":372,"abstract":373,"tags":375},[],[52,374],"precision agriculture",[89],{"code":377,"score":71,"supported":49,"title":378,"abstract":379,"tags":382},"H2",[],[380,381],"土壤","soil",[],{"code":384,"score":71,"supported":49,"title":385,"abstract":386,"tags":389},"I5",[],[387,388],"物联网","IoT",[],{"code":391,"score":48,"supported":49,"title":392,"abstract":393,"tags":395},"H6",[],[394],"农业生态",[],{"code":397,"score":48,"supported":49,"title":398,"abstract":399,"tags":401},"H7",[],[400],"气候适应",[],[403,404],"喜马拉雅 苹果园 产量预测","HCM-STAN 作物产量预测","喜马拉雅苹果园产量预测-4988","10.3389\u002Ffpls.2026.1933033",{"doi":406,"openalex_id":408,"authors":409,"venue":270,"cited_by_count":117,"oa_url":331,"card":423,"direction":326,"ingested_from":134},"W7221795850",[410,413,415,417,419,421],{"name":411,"orcid":412},"Shabir Ahmad Mir","https:\u002F\u002Forcid.org\u002F0000-0003-2311-218X",{"name":414,"orcid":8},"Umarfarooque Momin",{"name":416,"orcid":8},"Sudeep Kumar E",{"name":418,"orcid":8},"Itfaq Ahmad Mir",{"name":420,"orcid":8},"Ab Rouf Khan",{"name":422,"orcid":8},"Ghulam Mohammad Mir",{"tldr":424,"method":425,"finding":426,"direction":132,"opportunity":427},"提出分层跨模态时空注意力网络HCM-STAN，融合多源农业数据预测喜马拉雅山区苹果产量。","用IoT土壤、气象、生理和无人机多光谱数据，结合分层时空与跨模态注意力及不确定性","四周前产量预测R²达0.89，比CNN-LSTM提升15.6%，零样本迁移芒果R²为0.67。","可探索跨作物零样本迁移的领域自适应机制，并验证模型在更多山地农业生态系统的泛化能力。","2026-10-15T23:30:08.750140Z",{"id":430,"title":431,"url":432,"summary":433,"summary_zh":434,"content":8,"source_name":435,"source_url":432,"published_at":334,"category":12,"cover_url":8,"hotness":13,"is_selected":14,"score":436,"score_detail":437,"sources":440,"tags":442,"primary_topic":445,"secondary_topics":446,"classification":447,"search_phrases":482,"slug":485,"view_count":117,"doi":486,"paper":487,"created_at":506},4985,"Hybrid deep ensemble learning for crop disease prediction and climate-resilient disaster management in smart agriculture","https:\u002F\u002Fdoi.org\u002F10.1007\u002Fs10791-026-10579-y","Abstract Accurate crop disease prediction and climate-resilient disaster management are essential for improving agricultural productivity and ensuring food security under increasingly dynamic environmental conditions. Existing deep learning approaches typically perform crop disease detection and climate forecasting independently and often rely on deterministic CNN–LSTM architectures without effective multimodal feature fusion, predictive uncertainty estimation, or comprehensive statistical validation. Consequently, their ability to provide reliable decision support under changing climatic conditions remains limited. To address these challenges, this paper proposes a novel hybrid deep ensemble learning framework for integrated crop disease prediction and climate-resilient disaster management in smart agriculture. The proposed framework combines convolutional neural networks (CNNs) for spatial crop image feature extraction with long short-term memory (LSTM) networks for temporal climate forecasting. An attention-based multimodal feature fusion mechanism is employed to integrate heterogeneous crop and climate information, while Bayesian uncertainty estimation and deep ensemble learning are incorporated to improve prediction reliability, robustness, and confidence-aware decision support. Experimental evaluation is performed using multimodal datasets comprising crop images and climate observations collected from the PlantVillage, NOAA, and NASA EOS datasets. The dataset is divided into training, validation, and testing subsets using a 70:15:15 ratio. The proposed framework is evaluated using classification and regression metrics, including Accuracy, Precision, Recall, F1-score, RMSE, MAE, MAPE, and AUC-ROC. To ensure reproducibility, all experiments are repeated five times using different random seeds, and the results are reported with mean $$\\pm$$ standard deviation, 95% confidence intervals, and paired Student’s $$t$$ -tests. Experimental results demonstrate that the proposed framework consistently outperforms conventional CNN, LSTM, and CNN–LSTM hybrid models by achieving higher classification accuracy, lower climate forecasting error, improved robustness under varying climate conditions, and statistically significant performance improvements. The proposed framework provides a reliable, scalable, and uncertainty-aware solution for intelligent climate-resilient smart agriculture and early agricultural disaster management.","摘要：准确的作物病害预测和气候适应性灾害管理对于在日益动态的环境条件下提高农业生产力和保障粮食安全至关重要。现有深度学习方法通常独立执行作物病害检测和气候预测，且往往依赖确定性CNN–LSTM架构，缺乏有效的多模态特征融合、预测不确定性估计或全面的统计验证。因此，其在气候变化条件下提供可靠决策支持的能力仍然有限。为应对这些挑战，本文提出了一种新颖的混合深度集成学习框架，用于智慧农业中的作物病害预测与气候适应性灾害管理一体化。该框架将用于空间作物图像特征提取的卷积神经网络（CNN）与用于时间气候预测的长短期记忆（LSTM）网络相结合。采用基于注意力的多模态特征融合机制整合异构的作物与气候信息，同时引入贝叶斯不确定性估计和深度集成学习，以提高预测可靠性、鲁棒性和置信感知决策支持能力。实验评估使用包含作物图像和气候观测数据的多模态数据集，数据来源于PlantVillage、NOAA和NASA EOS数据集。数据集按70：15：15的比例划分为训练集、验证集和测试集。采用分类和回归指标对所提框架进行评估，包括准确率、精确率、召回率、F1分数、RMSE、MAE、MAPE和AUC-ROC。为确保可复现性，所有实验使用不同随机种子重复五次，结果以均值$$\\pm$$标准差、95%置信区间和配对Student's $$t$$检验报告。实验结果表明，所提框架在分类准确率、气候预测误差、不同气候条件下的鲁棒性以及统计显著性性能提升方面均持续优于传统CNN、LSTM和CNN–LSTM混合模型。该框架为智能气候适应性智慧农业和早期农业灾害管理提供了一种可靠、可扩展且具有不确定性感知能力的解决方案。","Discover Computing",71,{"impact":20,"substance":17,"depth":84,"authority":438,"freshness":200,"relevant":22,"comment":439},13,"提出CNN-LSTM注意力融合与贝叶斯不确定性估计的混合深度集成框架，多源数据与统计验证扎实，但属方法类论文，产业落地影响有限。",[441],{"name":435,"url":432},[89,90,27,443,444],"防灾减灾","作物病害预测",{"code":34,"slug":35,"name":36,"path":37,"parent_code":38},[],{"status":41,"locked":14,"version":42,"method":43,"evidence":448},{"candidates":449,"agricultural_evidence":49},[450,455,460,465,472,478],{"code":47,"score":215,"supported":49,"title":451,"abstract":453,"tags":454},[452],"smart agriculture",[89,452],[89],{"code":34,"score":13,"supported":49,"title":456,"abstract":457,"tags":459},[],[205,221,458],"decision support",[90],{"code":397,"score":292,"supported":49,"title":461,"abstract":462,"tags":464},[],[463,400],"气候变化",[443],{"code":466,"score":71,"supported":49,"title":467,"abstract":468,"tags":471},"A8",[],[469,470],"粮食安全","food security",[],{"code":473,"score":48,"supported":49,"title":474,"abstract":475,"tags":477},"A7",[],[476],"间作",[],{"code":60,"score":22,"supported":14,"title":479,"abstract":480,"tags":481},[],[],[27],[483,484],"PlantVillage NOAA NASA 作物病害预测","CNN LSTM 气候韧性 智慧农业","PlantVillageNOAANASA作物病害预测-4985","10.1007\u002Fs10791-026-10579-y",{"doi":486,"openalex_id":488,"authors":489,"venue":435,"cited_by_count":117,"oa_url":432,"card":501,"direction":326,"ingested_from":134},"W7221778141",[490,493,496,498],{"name":491,"orcid":492},"Deepak Motwani","https:\u002F\u002Forcid.org\u002F0000-0002-0217-7155",{"name":494,"orcid":495},"Sellappan Palaniappan","https:\u002F\u002Forcid.org\u002F0009-0009-1168-2864",{"name":497,"orcid":8},"A. Daniel",{"name":499,"orcid":500},"Azween B. Abdullah","https:\u002F\u002Forcid.org\u002F0000-0003-4425-8604",{"tldr":502,"method":503,"finding":504,"direction":132,"opportunity":505},"提出混合深度集成框架，融合CNN与LSTM并引入注意力机制，实现作物病害预测与气候韧性灾害管理。","CNN提取图像特征、LSTM预测气候，注意力多模态融合，贝叶斯不确定性估计与深度","框架在分类精度与气候预测误差上均优于CNN、LSTM及CNN-LSTM基线，且性能提升具统计显著性。","可探索轻量化边缘部署与多作物跨区域迁移，结合实时物联网数据提升不确定性感知的灾害预警时效。","2026-10-15T23:30:08.408875Z"]