[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2874":3,"related-2874":56},{"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,"search_phrases":31,"slug":34,"view_count":35,"doi":36,"paper":37,"created_at":55},2874,"Laboratory Evaluation of Multispectral Grape Quality Grade Prediction with Compensation for Controlled Illumination Variation","https:\u002F\u002Fdoi.org\u002F10.1088\u002F2631-8695\u002Faea952","Abstract Accurate fruit quality assessment is important for selective harvesting in agricultural robotics, where multispectral sensing provides a non-destructive approach but remains sen- sitive to variability in illumination conditions. This study presents a controlled laboratory evaluation conducted in a dark box under halogen illumination with fixed sensor and sam- ple geometry, in which illumination variation was simulated by adjusting lamp intensity. Prediction models for visually assigned grape quality grades were first established under fixed illumination using an AS7265x multispectral sensor with 18 channels from 410 to 940 nm, covering the visible and part of the near-infrared range. Spectral data were prepro- cessed using standard normal variate transformation, standardization, variable importance in projection feature selection, and mixup interpolation. Three machine learning meth- ods, partial least squares regression, support vector regression, and random forest, were evaluated with nested cross-validation, out-of-fold prediction, and 2000-iteration bootstrap resampling. Support vector regression achieved the best performance, with R² = 0.9084 [95% CI: 0.851–0.945], RMSE = 0.3352, and MAE = 0.2374. To address illumination vari- ability, three compensation methods, direct standardization, extended multiplicative scatter correction, and a polynomial per-band fitting method, were implemented and compared across five lamp intensity levels from 20% to 100% of rated power. The three methods and the uncompensated condition reached similar mean accuracy across the five levels, with the exception of extended multiplicative scatter correction, which was lower at every level. Di- rect standardization reduced the spread of the five predictions obtained for the same berry by about 31% relative to no compensation, and gave the highest grade-level accuracy of the four conditions. These results provide laboratory feasibility evidence that direct standard- ization can improve the consistency of grade prediction across controlled illumination levels, and establish a basis for further validation under natural field illumination toward future robotic harvesting applications.","摘要 准确的水果品质评估对于农业机器人中的选择性采收至关重要，其中多光谱传感提供了一种非破坏性方法，但仍对光照条件的变化较为敏感。本研究提出了一种在暗箱中、卤素灯照明下、传感器与样品几何构型固定的受控实验室评估方法，通过调节灯强度来模拟光照变化。首先在固定光照条件下，使用AS7265x多光谱传感器（具有410至940 nm范围内的18个通道，覆盖可见光及部分近红外波段）建立了视觉 assigned 葡萄品质等级的预测模型。光谱数据采用标准正态变量变换、标准化、投影变量重要性特征选择以及mixup插值进行预处理。采用嵌套交叉验证、折外预测和2000次迭代自助重采样，对偏最小二乘回归、支持向量回归和随机森林三种机器学习方法进行了评估。支持向量回归取得了最佳性能，R² = 0.9084 [95% CI: 0.851–0.945]，RMSE = 0.3352，MAE = 0.2374。为解决光照变化问题，实施了直接标准化、扩展多元散射校正和逐波段多项式拟合三种补偿方法，并在额定功率20%至100%的五个灯强度水平下进行了比较。三种方法及未补偿条件在五个水平下达到了相似的平均准确率，但扩展多元散射校正除外，其在每个水平下均较低。与未补偿相比，直接标准化使同一浆果五次预测结果的离散度降低了约31%，并在四种条件中给出了最高的等级准确率。这些结果为直接标准化能够提高受控光照水平下等级预测的一致性提供了实验室可行性证据，并为未来机器人采收应用中在自然田间光照下进一步验证奠定了基础。",null,"Engineering Research Express","2026-09-17T00:00:00Z","论文",10,false,66,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":17,"relevant":21,"comment":22},8,20,17,13,1,"实验室条件下多光谱葡萄品质分级与光照补偿研究，方法扎实、结论可靠，但属实验室可行性验证，产业影响有限。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","葡萄","多光谱检测","水果品质分级",[32,33],"多光谱 葡萄 品质分级","AS7265x 多光谱传感器","多光谱葡萄品质分级-2874",0,"10.1088\u002F2631-8695\u002Faea952",{"doi":36,"openalex_id":38,"authors":39,"venue":10,"cited_by_count":35,"oa_url":9,"card":47,"direction":53,"ingested_from":54},"W7213509882",[40,42,44],{"name":41,"orcid":9},"Guodong Xiu",{"name":43,"orcid":9},"Sára Strakošová",{"name":45,"orcid":46},"Ikuo Mizuuchi","https:\u002F\u002Forcid.org\u002F0000-0003-4657-2613",{"tldr":48,"method":49,"finding":50,"direction":51,"opportunity":52},"在暗箱中评估多光谱葡萄品质分级预测，并比较三种光照补偿方法。","AS7265x多光谱传感器、SNV与VIP预处理、SVR\u002FRF建模、直接标准化等","SVR预测最佳（R²=0.9084）；直接标准化使同果预测离散度降低约31%。","农业遥感与作物表型","可延伸至自然田间光照下的验证，并融合机器人采摘的实时多光谱分级系统。","农业人工智能与决策模型","openalex","2026-09-18T23:30:52.790038Z",{"total":57,"page":21,"page_size":57,"items":58},6,[59,105,153,182,211,239],{"id":60,"title":61,"url":62,"summary":63,"summary_zh":64,"content":9,"source_name":65,"source_url":62,"published_at":66,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":67,"score_detail":68,"sources":74,"tags":76,"search_phrases":78,"slug":81,"view_count":21,"doi":82,"paper":83,"created_at":104},1429,"RGB-based phenotyping of grapevine leaves reveals stronger cultivar discrimination on the abaxial surface","https:\u002F\u002Fdoi.org\u002F10.1556\u002F446.2026.00335","Abstract Grapevine leaves have dorsiventral anatomy with distinct adaxial (upper) and abaxial (lower) surfaces. Although morphological descriptor lists and ampelographic literature provide information on both the upper and lower side characteristics, in practice, the color traits of the upper side have become the focus of scientific publications. This study introduces the practical application of the recently developed LeafLaminaMap software and the use of trichromatic color indices in grapevine characterization. We aimed to compare colorimetric information on the adaxial and abaxial leaf surfaces as well as to explore the potential of machine learning models in classification. Five statistical descriptors (mean, standard deviation, contrast, energy, and entropy) were calculated for 25 RGB-based color indices on both leaf surfaces of 120 samples collected from four grapevine cultivars (‘Chardonnay’, ‘Pinot noir’, ‘Sauvignon blanc’, and ‘Syrah’). Data was subjected to multivariate statistical analysis and machine learning classifiers. Results showed that the abaxial leaf surface had stronger cultivar-specific color signatures, supporting its suitability for cultivar discrimination. These findings suggest that RGB-based analysis of both adaxial and abaxial leaf surfaces has potential for grapevine cultivar discrimination, offering a new perspective for cost-efficient plant phenotyping.","葡萄叶片具有背腹型解剖结构，其近轴面（上表面）与远轴面（下表面）特征明显不同。尽管形态描述符列表和葡萄品种志文献提供了上下两面特性的信息，但在实际应用中，上表面颜色性状已成为科学研究关注的焦点。本研究介绍了新开发的LeafLaminaMap软件的实际应用以及三色颜色指数在葡萄品种鉴定中的使用。我们旨在比较叶片近轴面与远轴面的比色信息，并探索机器学习模型在分类中的潜力。对来自四个葡萄品种（‘霞多丽’、‘黑比诺’、‘长相思’和‘西拉’）的120个样本，计算了基于RGB的25个颜色指数在叶片两面的五个统计描述符（均值、标准差、对比度、能量和熵）。数据经过多元统计分析和机器学习分类器处理。结果表明，远轴面具有更强的品种特异性颜色特征，支持其适用于品种鉴别。这些发现表明，基于RGB的叶片近轴面和远轴面分析在葡萄品种鉴别方面具有潜力，为经济高效的植物表型分析提供了新视角。","Progress in Agricultural Engineering Sciences","2026-09-01T00:00:00Z",63,{"impact":69,"substance":70,"depth":71,"authority":13,"freshness":72,"relevant":21,"comment":73},12,18,16,7,"研究提出基于RGB图像的葡萄叶片双面表型分析方法，机器学习分类效果良好，为低成本作物表型鉴定提供新思路。",[75],{"name":65,"url":62},[26,27,28,77],"作物表型",[79,80],"农业人工智能 作物表型 智慧农业 葡萄","农业人工智能 作物表型","农业人工智能作物表型智慧农业葡萄-1429","10.1556\u002F446.2026.00335",{"doi":82,"openalex_id":84,"authors":85,"venue":65,"cited_by_count":35,"oa_url":62,"card":99,"direction":51,"ingested_from":54},"W7204971124",[86,89,91,94,97],{"name":87,"orcid":88},"Péter Bodor-Pesti","https:\u002F\u002Forcid.org\u002F0000-0001-8346-4975",{"name":90,"orcid":9},"Gábor Vértes",{"name":92,"orcid":93},"Lien Le Phuong Nguyen","https:\u002F\u002Forcid.org\u002F0000-0002-5975-9906",{"name":95,"orcid":96},"László Baranyai","https:\u002F\u002Forcid.org\u002F0000-0001-6177-7364",{"name":98,"orcid":9},"Diána Ágnes Nyitrainé Sárdy",{"tldr":100,"method":101,"finding":102,"direction":51,"opportunity":103},"比较葡萄叶片上下表面颜色特征，发现下表面更能区分品种。","使用LeafLaminaMap软件和RGB颜色指数，结合统计描述符与机器学习分类","叶片下表面颜色特征品种特异性更强，更适合品种鉴别。","可探索其他作物叶片双面表型分析，或结合高光谱成像提升品种鉴别精度。","2026-09-02T23:30:47.440706Z",{"id":106,"title":107,"url":108,"summary":109,"summary_zh":110,"content":9,"source_name":111,"source_url":108,"published_at":112,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":113,"score_detail":114,"sources":118,"tags":120,"search_phrases":122,"slug":125,"view_count":35,"doi":126,"paper":127,"created_at":152},1278,"ERG-Mask: An edge–region collaborative instance segmentation model for greenhouse table-grape clusters","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112369","ERG-Mask: An edge–region collaborative instance segmentation model for greenhouse table-grape clusters。Computers and Electronics in Agriculture","ERG-Mask：面向温室鲜食葡萄果串的边缘-区域协同实例分割模型。《Computers and Electronics in Agriculture》","Computers and Electronics in Agriculture","2026-08-31T00:00:00Z",62,{"impact":69,"substance":70,"depth":71,"authority":115,"freshness":116,"relevant":21,"comment":117},14,2,"针对温室葡萄簇的实例分割新方法，发表于核心期刊，技术有创新性，但影响范围限于细分领域，时效性较低。",[119],{"name":111,"url":108},[26,27,121,28],"设施农业",[123,124],"农业人工智能 智慧农业 设施农业 葡萄","农业人工智能 智慧农业","农业人工智能智慧农业设施农业葡萄-1278","10.1016\u002Fj.compag.2026.112369",{"doi":126,"openalex_id":128,"authors":129,"venue":111,"cited_by_count":35,"oa_url":9,"card":147,"direction":53,"ingested_from":54},"W7204777248",[130,133,136,139,141,144],{"name":131,"orcid":132},"Zhengtong Ning","https:\u002F\u002Forcid.org\u002F0000-0002-9218-6425",{"name":134,"orcid":135},"Jian Li","https:\u002F\u002Forcid.org\u002F0000-0002-4187-317X",{"name":137,"orcid":138},"Haidong Li","https:\u002F\u002Forcid.org\u002F0000-0002-6634-9593",{"name":140,"orcid":9},"Liangkuan Zhu",{"name":142,"orcid":143},"Qingjie Wang","https:\u002F\u002Forcid.org\u002F0009-0001-9038-7821",{"name":145,"orcid":146},"Yi Wang","https:\u002F\u002Forcid.org\u002F0000-0002-0192-3195",{"tldr":148,"method":149,"finding":150,"direction":53,"opportunity":151},"提出ERG-Mask模型，用于温室鲜食葡萄串的实例分割，提升边缘与区域协同精度。","结合边缘与区域信息的协同实例分割模型，基于深度学习。","ERG-Mask在葡萄串分割上优于现有方法，精度更高。","可探索该模型在复杂遮挡、不同光照下的鲁棒性，或扩展至其他簇状果实（如番茄、蓝莓）的检测。","2026-09-01T23:30:01.626379Z",{"id":154,"title":155,"url":156,"summary":157,"summary_zh":9,"content":9,"source_name":158,"source_url":9,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":159,"score_detail":160,"sources":164,"tags":166,"search_phrases":169,"slug":172,"view_count":35,"doi":9,"paper":173,"created_at":181},2904,"Decoupled Foundation Models:基于YOLO26m+SAM2+DINOv2的湿度诱导番茄叶坏死实例分割与检测,登MDPI Agriculture 16(18)1997","https:\u002F\u002Fwww.mdpi.com\u002F2077-0472\u002F16\u002F18\u002F1997","本研究针对温室番茄相对湿度过高引发的非生物胁迫(生理性叶坏死,与生物感染症状相似),提出多步AI管道自动化分割与分类坏死叶斑。采集218张RGB图像、3218个标注(棕色坏死斑\u002F黄色坏死斑\u002F无坏死),系统评估6种端到端实例分割管道(YOLO26m检测+SAM2零样本分割+微调DINOv2或EfficientNet-B3分类);微调DINOv2宏F1达0.926,优于EfficientNet-B3、ResNet-50、Swin-Small基线(0.886-0.901);最佳配置mAP@50=0.828,较YOLO26m单模型提升约8%。","MDPI Agriculture",78,{"impact":71,"substance":161,"depth":70,"authority":20,"freshness":162,"relevant":21,"comment":163},22,9,"方法组合新颖、数据规模与对比基线扎实，对温室番茄生理性叶坏死自动识别有实用价值，值得进入每日精选。",[165],{"name":158,"url":156},[26,27,121,167,168],"番茄","病害识别",[170,171],"番茄叶坏死 实例分割","农业人工智能 智慧农业 病害识别 设施农业","番茄叶坏死实例分割-2904",{"doi":9,"openalex_id":9,"authors":174,"venue":9,"cited_by_count":35,"oa_url":9,"card":175,"direction":53,"ingested_from":180},[],{"tldr":176,"method":177,"finding":178,"direction":53,"opportunity":179},"用YOLO26m+SAM2+DINOv2多步管道分割并分类高湿诱导的番茄叶坏死斑。","218张RGB图像、3218个标注，评估6种实例分割管道并微调DINOv2分类。","微调DINOv2宏F1达0.926，最佳配置mAP@50=0.828，较单模型提升约8%。","可探索零样本基础模型在多种非生物胁迫症状上的泛化与轻量化温室部署。","agent","2026-09-19T00:06:09.021594Z",{"id":183,"title":184,"url":185,"summary":186,"summary_zh":9,"content":9,"source_name":187,"source_url":9,"published_at":188,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":189,"score_detail":190,"sources":192,"tags":194,"search_phrases":198,"slug":201,"view_count":35,"doi":202,"paper":203,"created_at":210},2903,"基于QYmax叶绿素荧光成像和改进LCRNet模型的小麦白粉病智能识别与诊断,登Frontiers in Plant Science","https:\u002F\u002Fwww.frontiersin.org\u002Fjournals\u002Fplant-science\u002Farticles\u002F10.3389\u002Ffpls.2026.1837843\u002Ffull","本研究整合叶绿素荧光成像与深度学习算法,刻画小麦白粉病发展过程中QYmax等关键荧光参数的时序动态;构建基于QYmax的小麦白粉病叶绿素荧光图像数据集;提出LCRNet智能识别模型,通过LSAF大型选择性自适应融合和CA坐标注意力模块的协同处理机制,实现高效病害识别。在独立测试集上模型准确率达98.0%、F1-score达98.3%,显著优于对比模型,为作物病害预防控制系统的智能化与精准化绿色转型提供高精度方案。","Frontiers in Plant Science","2026-09-18T00:00:00Z",82,{"impact":70,"substance":161,"depth":70,"authority":115,"freshness":13,"relevant":21,"comment":191},"方法新颖、数据扎实且时效性强，但属细分领域论文，产业影响有限，适合进入每日精选。",[193],{"name":187,"url":185},[26,27,195,196,197],"作物病害识别","小麦白粉病","叶绿素荧光成像",[199,200],"小麦白粉病 叶绿素荧光成像 LCRNet","QYmax 小麦白粉病 智能识别","小麦白粉病叶绿素荧光成像LCRNet-2903","10.3389\u002Ffpls.2026.1837843\u002Ffull",{"doi":202,"openalex_id":9,"authors":204,"venue":9,"cited_by_count":35,"oa_url":9,"card":205,"direction":51,"ingested_from":180},[],{"tldr":206,"method":207,"finding":208,"direction":51,"opportunity":209},"用QYmax叶绿素荧光成像与改进LCRNet实现小麦白粉病高精度识别。","构建QYmax荧光图像数据集，提出含LSAF与CA模块的LCRNet模型。","独立测试集准确率98.0%、F1-score 98.3%，显著优于对比模型。","可探索多病害、多生育期与田间自然光下的荧光成像泛化及轻量化部署。","2026-09-19T00:06:08.843770Z",{"id":212,"title":213,"url":214,"summary":215,"summary_zh":9,"content":9,"source_name":216,"source_url":9,"published_at":217,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":159,"score_detail":218,"sources":221,"tags":223,"search_phrases":227,"slug":230,"view_count":35,"doi":9,"paper":231,"created_at":238},2902,"TSAFI-DT:可持续性感知花生产量预测数字孪生框架,登MDPI AI 7(9)364","https:\u002F\u002Fwww.mdpi.com\u002F2673-2688\u002F7\u002F9\u002F364","本研究提出TSAFI-DT可回顾验证的、数据驱动的Digital Twin原型,集成时空数据重建、可持续状态表征、分层产量预测、反事实分析与情景模拟。基于1997-2023年印度地区级花生数据,采用贝叶斯优化的XGBoost模型进行一步前瞻产量预测,RMSE=0.171 t\u002Fha,显著优于基线;结合固定效应与合成控制分析,eRAI扩展再生农业指数整合作物多样性、生产力稳定性、土地利用效率和产量趋势,预测产量在可持续性扰动下可提升12.4%。","MDPI AI","2026-09-14T00:00:00Z",{"impact":70,"substance":161,"depth":219,"authority":20,"freshness":57,"relevant":21,"comment":220},19,"方法新颖、数据规模扎实的农业数字孪生研究，对智慧农业与产量预测领域有参考价值，但属细分学术进展，非产业级事件。",[222],{"name":216,"url":214},[26,27,224,225,226],"数字孪生","可持续农业","花生产量预测",[228,229],"TSAFI-DT 花生 数字孪生","印度 花生 产量预测","TSAFI-DT花生数字孪生-2902",{"doi":9,"openalex_id":9,"authors":232,"venue":9,"cited_by_count":35,"oa_url":9,"card":233,"direction":53,"ingested_from":180},[],{"tldr":234,"method":235,"finding":236,"direction":53,"opportunity":237},"提出可持续性感知数字孪生框架TSAFI-DT，用于印度花生产量预测与情景模拟。","基于1997-2023年印度地区级数据，用贝叶斯优化XGBoost和合成控制分析","XGBoost预测RMSE为0.171 t\u002Fha，可持续性扰动下产量可提升12.4%。","可探索将数字孪生与实时物联网数据结合，实现动态可持续性评估与决策支持。","2026-09-19T00:06:08.754978Z",{"id":240,"title":241,"url":242,"summary":243,"summary_zh":9,"content":9,"source_name":244,"source_url":9,"published_at":11,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":245,"score_detail":246,"sources":248,"tags":250,"search_phrases":254,"slug":257,"view_count":35,"doi":9,"paper":258,"created_at":265},2901,"AgriScope:面向农业图像的像素级多模态理解统一框架,arXiv 2609.20325(预印本)","https:\u002F\u002Farxiv.org\u002Fabs\u002F2609.20325","Boudiaf、Alanssari、Hussain、Javed提出AgriScope,一个统一的像素级多模态农业图像理解框架,联合支持图像级、区域级、像素级理解,可实现接地描述生成、指代表达分割、多轮多模态交互等任务。集成生物专用语义表征、密集空间表征与像素解码;引入大规模像素级农业多模态指令调优数据集AgriGround,包含50万+图像和1100万+指令跟随样本,涵盖植物病害分析、作物与杂草识别、昆虫识别、细粒度植物理解。实验表明AgriScope在多项农业视觉语言任务上有效。","arXiv (preprint)",75,{"impact":70,"substance":161,"depth":70,"authority":17,"freshness":162,"relevant":21,"comment":247},"提出统一像素级农业多模态理解框架并开源50万图像、1100万指令样本的大规模数据集，方法新颖、数据规模突出，但为arXiv预印本、未经同行评审，权威性有限，值得作为前沿技术动态精选。",[249],{"name":244,"url":242},[26,27,251,252,253],"农业遥感","植物病害识别","多模态大模型",[255,256],"AgriScope 农业图像 多模态","AgriGround 像素级 农业数据集","AgriScope农业图像多模态-2901",{"doi":9,"openalex_id":9,"authors":259,"venue":9,"cited_by_count":35,"oa_url":9,"card":260,"direction":53,"ingested_from":180},[],{"tldr":261,"method":262,"finding":263,"direction":53,"opportunity":264},"提出AgriScope统一框架，实现农业图像像素级多模态理解与多任务交互。","构建AgriGround数据集（50万+图像、1100万+指令样本），融合语义与","AgriScope在接地描述、指代分割、多轮交互等农业视觉语言任务上有效。","可探索像素级多模态模型在田间实时病害诊断与精准施药决策中的落地与轻量化。","2026-09-19T00:06:08.678379Z"]