[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-4994":3,"related-4994":97},{"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":23,"tags":25,"primary_topic":31,"secondary_topics":37,"classification":38,"search_phrases":71,"slug":74,"view_count":75,"doi":76,"paper":77,"created_at":96},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。",null,"Frontiers in Plant Science","2026-10-13T00:00:00Z","论文",10,false,82,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":20,"relevant":21,"comment":22},18,23,14,9,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","计算机视觉","产量预测","芒果",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},"I2","agricultural-ai","农业AI与大模型","\u002Ftopic\u002Fdigital-agriculture\u002Fagricultural-ai","I",[],{"status":39,"locked":14,"version":40,"method":41,"evidence":42},"classified","2026-10-22.2","rules",{"candidates":43,"agricultural_evidence":46},[44,54,61,66],{"code":32,"score":45,"supported":46,"title":47,"abstract":49,"tags":53},19,true,[48],"deep learning",[50,28,48,51,52],"深度学习","computer vision","YOLO",[27,28],{"code":55,"score":56,"supported":46,"title":57,"abstract":58,"tags":60},"B2",7,[],[59,30],"果树",[30],{"code":62,"score":21,"supported":14,"title":63,"abstract":64,"tags":65},"I1",[],[],[26],{"code":67,"score":21,"supported":14,"title":68,"abstract":69,"tags":70},"I3",[],[],[29],[72,73],"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",0,"10.3389\u002Ffpls.2026.1943711",{"doi":76,"openalex_id":78,"authors":79,"venue":10,"cited_by_count":75,"oa_url":87,"card":88,"direction":94,"ingested_from":95},"W7221685456",[80,82,85],{"name":81,"orcid":9},"Víctor Yoel Huamán Asencio",{"name":83,"orcid":84},"Heber I. Mejía-Cabrera","https:\u002F\u002Forcid.org\u002F0000-0002-0007-0928",{"name":86,"orcid":9},"Juan Carlos Arcila Diaz","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1943711\u002Fpdf",{"tldr":89,"method":90,"finding":91,"direction":92,"opportunity":93},"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","农业人工智能与决策模型","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","openalex","2026-10-15T23:30:11.559035Z",{"total":98,"page":21,"page_size":98,"items":99},6,[100,157,232,286,340,387],{"id":101,"title":102,"url":103,"summary":104,"summary_zh":105,"content":9,"source_name":106,"source_url":103,"published_at":107,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":108,"score_detail":109,"sources":114,"tags":116,"primary_topic":118,"secondary_topics":119,"classification":120,"search_phrases":137,"slug":140,"view_count":75,"doi":141,"paper":142,"created_at":156},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":110,"substance":17,"depth":111,"authority":112,"freshness":13,"relevant":21,"comment":113},8,17,12,"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。",[115],{"name":106,"url":103},[26,27,28,29,117],"传感器校准",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":121},{"candidates":122,"agricultural_evidence":46},[123,129,133],{"code":32,"score":124,"supported":46,"title":125,"abstract":127,"tags":128},24,[126,51],"artificial intelligence",[28,51],[27,28],{"code":62,"score":21,"supported":14,"title":130,"abstract":131,"tags":132},[],[],[26],{"code":67,"score":21,"supported":14,"title":134,"abstract":135,"tags":136},[],[],[29],[138,139],"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","10.5281\u002Fzenodo.23544195",{"doi":141,"openalex_id":143,"authors":144,"venue":106,"cited_by_count":75,"oa_url":103,"card":151,"direction":92,"ingested_from":95},"W7223104380",[145,147,149],{"name":146,"orcid":9},"Tigist Alemu Gebremariam",{"name":148,"orcid":9},"Hawi Tesfaye Desta",{"name":150,"orcid":9},"Mulugeta Bekele Wolde",{"tldr":152,"method":153,"finding":154,"direction":92,"opportunity":155},"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。","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。","2026-10-22T11:21:01.554995Z",{"id":158,"title":159,"url":160,"summary":161,"summary_zh":162,"content":9,"source_name":163,"source_url":160,"published_at":164,"category":12,"cover_url":9,"hotness":165,"is_selected":14,"score":166,"score_detail":167,"sources":170,"tags":178,"primary_topic":181,"secondary_topics":187,"classification":189,"search_phrases":215,"slug":218,"view_count":219,"doi":220,"paper":221,"created_at":231},3912,"An Integrated Computer Vision and Machine Learning Framework for Sorghum Disease Severity Assessment and Yield Prediction","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23054047","An Integrated Computer Vision and Machine Learning Framework for Sorghum Disease Severity Assessment and Yield Prediction presents a technology-driven approach for automated crop health monitoring and yield estimation in sorghum cultivation. The study integrates computer vision techniques and machine learning algorithms to identify and quantify disease severity from crop images and to predict potential yield based on relevant visual and agronomic characteristics. The framework aims to reduce the limitations of conventional manual disease assessment, which can be time-consuming and subject to human variability. Image processing and feature extraction techniques are employed to capture disease-related characteristics, while machine learning models support classification, severity estimation, and yield prediction. By combining disease assessment with yield forecasting, the proposed framework provides a comprehensive approach to precision agriculture and data-driven crop management. The work demonstrates the potential of artificial intelligence and computer vision to support timely decision-making, improve crop monitoring, and contribute to sustainable and efficient sorghum production.","一个集成的计算机视觉与机器学习框架用于高粱病害严重度评估与产量预测，提出了一种技术驱动的方法，用于高粱种植中的自动化作物健康监测与产量估算。该研究整合了计算机视觉技术和机器学习算法，从作物图像中识别和量化病害严重度，并基于相关视觉和农艺特征预测潜在产量。该框架旨在减少传统人工病害评估的局限性，传统方法可能耗时且受人为变异影响。研究采用图像处理和特征提取技术捕捉病害相关特征，同时机器学习模型支持分类、严重度估计和产量预测。通过将病害评估与产量预测相结合，所提出的框架为精准农业和数据驱动的作物管理提供了一种综合方法。该工作展示了人工智能和计算机视觉在支持及时决策、改善作物监测以及促进可持续和高效高粱生产方面的潜力。","International Journal of Emerging Technologies and Innovative Research","2026-09-30T00:00:00Z",55,56,{"impact":112,"substance":168,"depth":19,"authority":98,"freshness":110,"relevant":21,"comment":169},16,"论文提出计算机视觉与机器学习结合的高粱病害严重度评估与产量预测框架，方法有一定新意但期刊权威性一般，属细分领域技术进展。",[171,172,174,176],{"name":163,"url":160},{"name":163,"url":173},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23054048",{"name":163,"url":175},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23053752",{"name":163,"url":177},"https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23053753",[26,27,28,29,179,180],"高粱","病害识别",{"code":182,"slug":183,"name":184,"path":185,"parent_code":186},"A5","tubers-grains","薯类与杂粮","\u002Ftopic\u002Ffield-crops\u002Ftubers-grains","A",[188],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":190},{"candidates":191,"agricultural_evidence":46},[192,199,205,211],{"code":32,"score":193,"supported":46,"title":194,"abstract":196,"tags":198},28,[195,51],"machine learning",[197,28,195,126,51],"机器学习",[27,180,28],{"code":182,"score":200,"supported":46,"title":201,"abstract":203,"tags":204},15,[202],"sorghum",[179,202],[179],{"code":62,"score":56,"supported":46,"title":206,"abstract":207,"tags":210},[],[208,209],"精准农业","precision agriculture",[26],{"code":67,"score":21,"supported":14,"title":212,"abstract":213,"tags":214},[],[],[29],[216,217],"高粱 病害识别 产量预测","计算机视觉 作物病害评估","高粱病害识别产量预测-3912",3,"10.5281\u002Fzenodo.23054047",{"doi":220,"openalex_id":222,"authors":223,"venue":163,"cited_by_count":75,"oa_url":160,"card":226,"direction":92,"ingested_from":95},"W7214900332",[224],{"name":225,"orcid":9},"Anantjit Publication",{"tldr":227,"method":228,"finding":229,"direction":92,"opportunity":230},"融合计算机视觉与机器学习，实现高粱病害严重度评估与产量预测。","图像处理与特征提取结合机器学习分类回归模型。","该框架可自动量化病害严重度并预测产量，减少人工评估偏差。","可探索多作物迁移学习与田间实时轻量化部署，提升泛化性。","2026-10-01T23:30:48.742698Z",{"id":233,"title":234,"url":235,"summary":236,"summary_zh":237,"content":9,"source_name":106,"source_url":235,"published_at":107,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":238,"score_detail":239,"sources":243,"tags":245,"primary_topic":248,"secondary_topics":249,"classification":250,"search_phrases":266,"slug":269,"view_count":75,"doi":270,"paper":271,"created_at":285},5584,"Distributionally-Robust Evaluation of Computer Vision Agriculture Disease Classifiers Under Climate-Driven Pathogen Range Shifts: An Artificial Intelligence Worst-Case Generalization Bound","https:\u002F\u002Fdoi.org\u002F10.5281\u002Fzenodo.23544150","Computer vision classifiers for crop disease are evaluated almost exclusively on held-out samples drawn from the same distribution as their training data, yet climate-driven pathogen range shifts mean that deployment environments are systematically out of distribution. The framework treats the deployment distribution as an adversarial perturbation of the training distribution within a Wasserstein ball whose radius is calibrated to observed climate displacement. Using NASA POWER monthly reanalysis for Harare, Zimbabwe (2000-2023, 312 monthly observations), the analysis estimates a mean air temperature of 18.914 C, a standard deviation of 3.107 C, and a warming trend of 0.2001 C per decade. These estimates anchor the radius of the ambiguity set and the resulting bound. The bound decomposes worst-case risk into an empirical risk term, a Wasserstein radius term scaled by the Lipschitz constant of the classifier, and a concentration term. Under plausible Lipschitz constants for convolutional backbones, the radius term dominates the empirical risk term once the climate displacement exceeds a threshold that the Harare record already approaches. The contribution is a formal evaluation criterion that replaces the i. i. d. assumption with a climate-calibrated ambiguity set, together with the analytical machinery needed to compute it.","作物病害的计算机视觉分类器几乎完全在与训练数据同分布的留出样本上进行评估，然而气候驱动的病原体分布范围变化意味着部署环境系统性地处于分布之外。该框架将部署分布视为训练分布在Wasserstein球内的对抗性扰动，球的半径根据观测到的气候位移进行校准。利用NASA POWER对津巴布韦哈拉雷的月度再分析数据（2000—2023年，312个月度观测值），分析估计平均气温为18.914°C，标准差为3.107°C，每十年升温趋势为0.2001°C。这些估计值锚定了模糊集的半径及由此得到的界。该界将最坏情况风险分解为经验风险项、由分类器Lipschitz常数缩放后的Wasserstein半径项以及集中项。在卷积骨干网络合理的Lipschitz常数下，一旦气候位移超过哈拉雷记录已接近的阈值，半径项便主导经验风险项。本文的贡献在于提出了一种形式化评估准则，以气候校准的模糊集替代独立同分布（i.i.d.）假设，并提供了计算该准则所需的分析工具。",75,{"impact":112,"substance":240,"depth":17,"authority":241,"freshness":13,"relevant":21,"comment":242},22,13,"方法新颖、数据具体，但属方法论预印论文，产业影响有限，适合作为专业精选而非头条。",[244],{"name":106,"url":235},[26,27,28,246,247],"作物病害","气候变化",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":251},{"candidates":252,"agricultural_evidence":46},[253,257,262],{"code":32,"score":124,"supported":46,"title":254,"abstract":255,"tags":256},[126,51],[28,51],[27,28],{"code":258,"score":21,"supported":14,"title":259,"abstract":260,"tags":261},"H7",[],[],[247],{"code":62,"score":21,"supported":14,"title":263,"abstract":264,"tags":265},[],[],[26],[267,268],"NASA POWER 哈拉雷 气温","Wasserstein 分布鲁棒 病害分类","NASAPOWER哈拉雷气温-5584","10.5281\u002Fzenodo.23544150",{"doi":270,"openalex_id":272,"authors":273,"venue":106,"cited_by_count":75,"oa_url":235,"card":280,"direction":92,"ingested_from":95},"W7223087892",[274,276,278],{"name":275,"orcid":9},"Kossi Afeli",{"name":277,"orcid":9},"Akouvi Amegashie",{"name":279,"orcid":9},"Komlan Sélom Dossou",{"tldr":281,"method":282,"finding":283,"direction":92,"opportunity":284},"提出气候校准的分布鲁棒评估准则，替代i.i.d.假设评价作物病害视觉分类器。","用Wasserstein球建模部署分布偏移，以NASA POWER哈拉雷气温数据","气候位移超阈值后，半径项主导经验风险，现有评估会高估模型泛化能力。","可将气候校准的鲁棒评估扩展到多作物多区域，并研究适应分布偏移的鲁棒训练方法。","2026-10-22T11:21:00.036937Z",{"id":287,"title":288,"url":289,"summary":290,"summary_zh":291,"content":9,"source_name":106,"source_url":289,"published_at":107,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":292,"score_detail":293,"sources":296,"tags":298,"primary_topic":301,"secondary_topics":302,"classification":307,"search_phrases":323,"slug":326,"view_count":75,"doi":327,"paper":328,"created_at":339},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":110,"substance":294,"depth":111,"authority":112,"freshness":13,"relevant":21,"comment":295},20,"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。",[297],{"name":106,"url":289},[26,27,29,299,300],"遥感","可解释性",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[303],{"code":67,"slug":304,"name":305,"path":306,"parent_code":36},"remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",{"status":39,"locked":14,"version":40,"method":41,"evidence":308},{"candidates":309,"agricultural_evidence":46},[310,314,319],{"code":32,"score":112,"supported":46,"title":311,"abstract":312,"tags":313},[51],[28],[27],{"code":67,"score":110,"supported":46,"title":315,"abstract":316,"tags":318},[],[299,317],"remote sensing",[29,299],{"code":62,"score":21,"supported":14,"title":320,"abstract":321,"tags":322},[],[],[26],[324,325],"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":327,"openalex_id":329,"authors":330,"venue":106,"cited_by_count":75,"oa_url":289,"card":333,"direction":337,"ingested_from":95},"W7223114002",[331],{"name":332,"orcid":9},"Kadiatou Barry",{"tldr":334,"method":335,"finding":336,"direction":337,"opportunity":338},"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":341,"title":342,"url":343,"summary":344,"summary_zh":345,"content":9,"source_name":346,"source_url":343,"published_at":347,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":166,"score_detail":348,"sources":350,"tags":352,"primary_topic":354,"secondary_topics":355,"classification":356,"search_phrases":373,"slug":376,"view_count":75,"doi":377,"paper":378,"created_at":386},5385,"A Comprehensive Review of Deep Learning and Computer Vision Techniques for Fruit Quality Assessment and Classification","https:\u002F\u002Fdoi.org\u002F10.25258\u002Fijddt.16.75s.151","Fruit image analysis has become an essential research area in precision agriculture, enabling automated fruit classification, quality grading, maturity estimation, disease diagnosis, yield prediction, and intelligent harvesting through advances in computer vision and artificial intelligence (AI).Recent progress in imaging technologies, large-scale benchmark datasets, and deep learning has substantially improved the accuracy, robustness, and scalability of automated fruit analysis systems.This survey presents a comprehensive and systematic review of image processing and AI techniques for fruit image analysis.It covers the complete image analysis pipeline, including image acquisition, preprocessing, enhancement, color space transformation, segmentation, feature extraction, and handcrafted texture descriptors.Furthermore, conventional machine learning algorithms are reviewed alongside state-of-the-art deep learning architectures, including Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and Vision Mamba models, for applications such as fruit recognition, quality assessment, disease detection, ripeness estimation, and object detection.Publicly available benchmark datasets, evaluation metrics, and performance comparison strategies are also summarized to facilitate objective assessment of existing approaches.In addition, the survey discusses key research challenges, including illumination variations, occlusion, complex orchard environments, limited annotated datasets, computational complexity, and domain adaptation, while highlighting emerging research trends such as self-supervised learning, multimodal learning, explainable AI, lightweight deep networks, foundation models, and edge intelligence for real-time deployment.By providing a unified review of methodologies, datasets, evaluation protocols, challenges, and future research directions, this survey offers a comprehensive reference for researchers and practitioners developing intelligent and scalable fruit image analysis systems for precision agriculture.","水果图像分析已成为精准农业中的一个重要研究领域，借助计算机视觉和人工智能（AI）的进步，实现了果实自动分类、品质分级、成熟度估计、病害诊断、产量预测和智能采收。成像技术、大规模基准数据集和深度学习的最新进展显著提升了自动化水果分析系统的准确性、鲁棒性和可扩展性。本综述对用于水果图像分析的图像处理和AI技术进行了全面而系统的回顾。内容涵盖完整的图像分析流程，包括图像采集、预处理、增强、颜色空间变换、分割、特征提取和手工纹理描述符。此外，综述了传统机器学习算法以及最先进的深度学习架构，包括卷积神经网络（CNN）、视觉Transformer（ViT）和视觉Mamba模型，用于果实识别、品质评估、病害检测、成熟度估计和目标检测等应用。同时总结了公开可用的基准数据集、评价指标和性能比较策略，以便对现有方法进行客观评估。此外，本综述讨论了关键研究挑战，包括光照变化、遮挡、复杂果园环境、标注数据集有限、计算复杂度和域适应，并重点介绍了新兴研究趋势，如自监督学习、多模态学习、可解释AI、轻量级深度网络、基础模型和面向实时部署的边缘智能。通过提供方法、数据集、评价协议、挑战和未来研究方向的统一综述，本综述为开发面向精准农业的智能且可扩展的水果图像分析系统的研究人员和实践者提供了全面的参考。","International Journal of Drug Delivery Technology","2026-10-18T00:00:00Z",{"impact":110,"substance":17,"depth":168,"authority":98,"freshness":110,"relevant":21,"comment":349},"系统性综述覆盖CNN、ViT、Vision Mamba等前沿方法，信息量足，但期刊权威性一般且偏学术综述，公共价值有限，适合主题聚合而非每日精选头条。",[351],{"name":346,"url":343},[26,27,28,50,353],"水果品质检测",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":357},{"candidates":358,"agricultural_evidence":46},[359,364,368],{"code":32,"score":193,"supported":46,"title":360,"abstract":361,"tags":363},[48,51],[362,50,197,28,195,48,126,51],"目标检测",[27,50,28],{"code":62,"score":56,"supported":46,"title":365,"abstract":366,"tags":367},[],[208,209],[26],{"code":55,"score":219,"supported":46,"title":369,"abstract":370,"tags":372},[],[371],"orchard",[],[374,375],"水果品质 计算机视觉 深度学习","农业人工智能 水果品质检测 计算机视觉 智慧农业","水果品质计算机视觉深度学习-5385","10.25258\u002Fijddt.16.75s.151",{"doi":377,"openalex_id":379,"authors":380,"venue":346,"cited_by_count":75,"oa_url":343,"card":381,"direction":94,"ingested_from":95},"W7222892831",[],{"tldr":382,"method":383,"finding":384,"direction":337,"opportunity":385},"系统综述深度学习与计算机视觉在水果品质评估与分级中的全流程方法。","综述图像采集到分割、特征提取及CNN、ViT、Vision Mamba等模型与公","深度学习显著提升水果分析精度与鲁棒性，但光照、遮挡和小样本仍是主要瓶颈。","可探索自监督与轻量化模型在复杂果园遮挡、小样本场景下的边缘实时部署。","2026-10-20T23:30:10.066042Z",{"id":388,"title":389,"url":390,"summary":391,"summary_zh":9,"content":9,"source_name":392,"source_url":9,"published_at":393,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":394,"score_detail":395,"sources":397,"tags":399,"primary_topic":402,"secondary_topics":408,"classification":410,"search_phrases":426,"slug":429,"view_count":75,"doi":9,"paper":430,"created_at":438},5363,"牛肉牛区域特征计算机视觉分析:基于 YOLO 与深度估计的牛体况自动评分","https:\u002F\u002Fbioengineer.org\u002Fcameras-learn-to-read-a-cows-spine-tail-head-and-body-shape-to-score-its-condition","Islam M.N., Schrick F.N., Edwards J.L., Burns R.T., Qi H., Gan H. (2026) 开发一种基于区域特定计算机视觉特征的牛肉牛体况自动评分(BCS)方法。研究通过 YOLO 检测 + 深度估计提取脊柱、尾根、体廓等区域特征,使用 SHAP 进行可解释 AI 分析,并以 XGBoost 融合。在 5 折交叉验证中两种模式均稳定。验证集仅 72 只动物且极端值样本少,深度图来自推断而非实测。框架可作为后续发展的方法学基础。DOI: 10.1016\u002Fj.atech.2026.102628。","Smart Agricultural Technology Vol.15 Article 102628 2026-10-15","2026-10-15T00:00:00Z",68,{"impact":112,"substance":294,"depth":111,"authority":241,"freshness":98,"relevant":21,"comment":396},"方法学上有新意，但验证样本仅72头且深度图为推断值，属细分领域论文进展，未达每日精选门槛。",[398],{"name":392,"url":390},[26,27,28,400,401],"肉牛养殖","体况评分",{"code":403,"slug":404,"name":405,"path":406,"parent_code":407},"C2","ruminants","草食牲畜","\u002Ftopic\u002Flivestock\u002Fruminants","C",[409],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":411},{"candidates":412,"agricultural_evidence":46},[413,417,422],{"code":32,"score":124,"supported":46,"title":414,"abstract":415,"tags":416},[28,52],[28,52],[27,28],{"code":403,"score":112,"supported":46,"title":418,"abstract":420,"tags":421},[419],"肉牛",[419],[400],{"code":62,"score":21,"supported":14,"title":423,"abstract":424,"tags":425},[],[],[26],[427,428],"YOLO 深度估计 牛体况评分","牛肉牛 计算机视觉 BCS","YOLO深度估计牛体况评分-5363",{"doi":9,"openalex_id":9,"authors":431,"venue":9,"cited_by_count":75,"oa_url":9,"card":432,"direction":337,"ingested_from":437},[],{"tldr":433,"method":434,"finding":435,"direction":337,"opportunity":436},"用YOLO检测与深度估计提取牛体区域特征，实现肉牛体况自动评分。","YOLO检测、深度估计、SHAP可解释分析与XGBoost融合，5折交叉验证。","区域特征融合评分稳定，但验证集仅72头且深度图为推断值。","可引入实测深度传感器与更大样本，提升极端体况评分鲁棒性。","agent","2026-10-20T02:47:54.361149Z"]