[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3129":3,"related-3129":60},{"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":22,"tags":24,"search_phrases":30,"slug":33,"view_count":34,"doi":35,"paper":36,"created_at":59},3129,"Vis\u002FNIR spectroscopy coupled with wavelength-optimized Monte Carlo simulation for non-destructive SSC detection of apples of different sizes","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112448","Vis\u002FNIR spectroscopy coupled with wavelength-optimized Monte Carlo simulation for non-destructive SSC detection of apples of different sizes。Computers and Electronics in Agriculture","可见\u002F近红外光谱（Vis\u002FNIR spectroscopy）结合波长优化的蒙特卡洛模拟用于不同大小苹果可溶性固形物含量（SSC）的无损检测。《计算机与电子农业》",null,"Computers and Electronics in Agriculture","2026-09-22T00:00:00Z","论文",10,false,81,{"impact":17,"substance":18,"depth":17,"authority":19,"freshness":13,"relevant":20,"comment":21},18,21,14,1,"核心期刊论文，提出波长优化蒙特卡洛模拟方法解决不同尺寸苹果可溶性固形物无损检测难题，方法新颖、结论可靠，对果园智能分选与品质检测有直接参考价值。",[23],{"name":10,"url":6},[25,26,27,28,29],"智慧农业","无损检测","苹果","可见近红外光谱","蒙特卡洛模拟",[31,32],"苹果 SSC 无损检测","Vis NIR 光谱 苹果","苹果SSC无损检测-3129",0,"10.1016\u002Fj.compag.2026.112448",{"doi":35,"openalex_id":37,"authors":38,"venue":10,"cited_by_count":34,"oa_url":9,"card":52,"direction":56,"ingested_from":58},"W7213978130",[39,42,44,46,48,50],{"name":40,"orcid":41},"C. N. Sun","https:\u002F\u002Forcid.org\u002F0009-0003-9732-8570",{"name":43,"orcid":9},"An He",{"name":45,"orcid":9},"Lei Zhang",{"name":47,"orcid":9},"Yaxiang Peng",{"name":49,"orcid":9},"Zhiming Guo",{"name":51,"orcid":9},"Xiaobo Zou",{"tldr":53,"method":54,"finding":55,"direction":56,"opportunity":57},"结合可见\u002F近红外光谱与波长优化蒙特卡洛模拟，实现不同大小苹果可溶性固形物无损检测。","Vis\u002FNIR光谱结合波长优化蒙特卡洛模拟，建立不同尺寸苹果SSC预测模型。","波长优化蒙特卡洛模拟能校正苹果尺寸差异，提升SSC无损检测精度。","农业遥感与作物表型","可探索将尺寸自适应蒙特卡洛模拟推广至其他水果及多品质参数同步检测。","openalex","2026-09-22T23:30:01.856917Z",{"total":61,"page":20,"page_size":61,"items":62},6,[63,120,164,205,262,307],{"id":64,"title":65,"url":66,"summary":67,"summary_zh":68,"content":9,"source_name":10,"source_url":66,"published_at":69,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":70,"score_detail":71,"sources":76,"tags":78,"search_phrases":82,"slug":85,"view_count":34,"doi":86,"paper":87,"created_at":119},3133,"Dual-representation fusion of near-infrared spectra for simultaneous identification of fresh-cocoon sex and imprinted-dead cocoons during pupal metamorphosis","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112431","Automated identification of fresh-cocoon sex and imprinted-dead cocoons is essential for raw silk quality control and requires an efficient and reliable approach. Although near-infrared (NIR) spectroscopy shows potential, sex-identification performance across pupal metamorphosis remains insufficiently understood, and existing modeling approaches remain underdeveloped. This study developed a dual-representation weighted-fusion network for fresh-cocoon sex and imprinted-dead cocoon identification during pupal metamorphosis. The network comprises two ablation-optimized branches: a 1D-CNN incorporating dilated spatial attention, adaptive coordinate attention, and ConvNeXtV2 blocks for spectral-sequence feature extraction, and a 2D-CNN incorporating shuffle attention network, adaptive coordinate attention, and ResNet blocks for recurrence-plot-based structural feature extraction. The spectral-sequence and recurrence-plot branches capture complementary information that is integrated using an adaptive weighted-fusion strategy, with the optimized branch parameters used as pretrained weights for transfer learning. On the held-out test set, the dual-representation weighted-fusion network achieved the highest overall accuracy of 95.39%, outperforming the optimized 1D-CNN (93.55%) and 2D-CNN (94.59%), with an average per-sample network inference time of 0.921 ms. Sex-identification performance varied with pupal metamorphosis, showing generally lower and more variable accuracy during the early period, improved performance during the middle period, and some decline during the late period. The fusion network maintained high accuracy across the three periods and achieved its highest mean sex-identification accuracy of 97.22% during the middle period (days 5–7). Overall, the proposed dual-representation NIR framework provides a promising approach for non-destructive fresh-cocoon sex identification and imprinted-dead cocoon screening, while highlighting the pupal developmental period as an important consideration for practical sex sorting.","鲜茧性别与印痕死茧的自动化识别对生丝质量控制至关重要，需要一种高效可靠的方法。尽管近红外（NIR）光谱显示出潜力，但蛹期变态过程中的性别识别性能仍未得到充分理解，现有建模方法也尚不成熟。本研究开发了一种双表示加权融合网络，用于蛹期变态过程中鲜茧性别和印痕死茧的识别。该网络包含两个经消融优化的分支：一个一维CNN分支，融合了膨胀空间注意力、自适应坐标注意力和ConvNeXtV2模块，用于光谱序列特征提取；以及一个二维CNN分支，融合了混洗注意力网络、自适应坐标注意力和ResNet模块，用于基于递归图的结构特征提取。光谱序列分支和递归图分支捕获互补信息，通过自适应加权融合策略进行整合，并将优化后的分支参数用作迁移学习的预训练权重。在留出测试集上，双表示加权融合网络达到了95.39%的最高总体准确率，优于优化后的一维CNN（93.55%）和二维CNN（94.59%），平均单样本网络推理时间为0.921 ms。性别识别性能随蛹期变态而变化，早期准确率普遍较低且波动较大，中期性能提升，晚期有所下降。融合网络在三个时期均保持较高准确率，并在中期（第5–7天）达到了97.22%的最高平均性别识别准确率。总体而言，所提出的双表示近红外框架为非破坏性鲜茧性别识别和印痕死茧筛查提供了一种有前景的方法，同时凸显了蛹发育时期作为实际性别分选的重要考虑因素。","2026-09-21T00:00:00Z",82,{"impact":17,"substance":72,"depth":73,"authority":19,"freshness":74,"relevant":20,"comment":75},22,19,9,"该研究提出双表征加权融合近红外光谱网络，实现鲜茧性别与印痕死茧的高精度无损识别，方法新颖、数据可靠，对蚕桑智能化分选具有实用价值。",[77],{"name":10,"url":66},[25,79,26,80,81],"农业人工智能","蚕桑产业","近红外光谱",[83,84],"近红外光谱 蚕茧 雌雄鉴别","鲜茧 性别识别 无损检测","近红外光谱蚕茧雌雄鉴别-3133","10.1016\u002Fj.compag.2026.112431",{"doi":86,"openalex_id":88,"authors":89,"venue":10,"cited_by_count":34,"oa_url":66,"card":113,"direction":117,"ingested_from":58},"W7213904433",[90,93,95,97,99,101,103,106,108,111],{"name":91,"orcid":92},"Haibo He","https:\u002F\u002Forcid.org\u002F0000-0002-5247-9370",{"name":94,"orcid":9},"Bing Lu",{"name":96,"orcid":9},"Mingqing Zhong",{"name":98,"orcid":9},"Wantong Xie",{"name":100,"orcid":9},"Yong Chen",{"name":102,"orcid":9},"Zongmeng Yang",{"name":104,"orcid":105},"Hua Huang","https:\u002F\u002Forcid.org\u002F0000-0002-2817-2950",{"name":107,"orcid":9},"Xiaoling Tong",{"name":109,"orcid":110},"Tianfu Zhao","https:\u002F\u002Forcid.org\u002F0000-0001-8189-7638",{"name":112,"orcid":9},"Shiping Zhu",{"tldr":114,"method":115,"finding":116,"direction":117,"opportunity":118},"提出双表征加权融合近红外光谱网络，同时识别鲜茧性别与印痕死茧。","1D-CNN与2D-CNN双分支，结合注意力、递归图与迁移学习。","融合网络总体准确率95.39%，中期性别识别最高达97.22%。","农业人工智能与决策模型","可探索蛹期动态建模与轻量化边缘部署，提升实际分选鲁棒性。","2026-09-22T23:30:02.331612Z",{"id":121,"title":122,"url":123,"summary":124,"summary_zh":125,"content":9,"source_name":10,"source_url":123,"published_at":126,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":127,"sources":129,"tags":131,"search_phrases":134,"slug":137,"view_count":34,"doi":138,"paper":139,"created_at":163},3054,"Assessment of mechanical damage in canola using X-ray radiography and deep learning","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112350","Mechanical damage during post-harvest handling and processing can drastically impair the quality, germination potential, and market value of canola ( Brassica napus L.) seeds. Traditional visual inspection methods are subjective and limited to detecting external defects, often missing internal damage. This study presents a non-destructive approach for classifying mechanical damage in canola seeds using X-ray radiography combined with artificial intelligence techniques. Seeds at three moisture contents (6 %, 8 %, and 12 %) were subjected to four levels of impact energy (0, 3, 3.7, and 4.3 mJ), generating 3,029 radiographic images labelled into four levels of varying mechanical damage (no damage, low damage, medium damage, and high damage). Two analytical pipelines were developed. The first involved machine learning with 37 extracted features, including intensity-based metrics, texture descriptors, frequency-domain features, and Fast Fourier Transform (FFT) features. Support vector machine (SVM) and random forest (RF) models were developed and evaluated; the SVM achieved the highest accuracy of 74.01 % after feature selection. The second pipeline implemented deep learning via transfer learning on five pre-trained convolutional neural networks: MobileNetV2, EfficientNet-B0, Xception, ResNet50, and DenseNet121. Among these, MobileNetV2 achieved the highest accuracy of 91.42 %, outperforming all traditional machine learning models. The results demonstrate the potential of integrating radiographic imaging with artificial intelligence methods for rapid, reliable, and non-destructive classification of mechanical damage in canola seeds, paving the way for intelligent quality control systems in the oilseed supply chain.","油菜（Brassica napus L.）种子在采后处理和加工过程中的机械损伤会严重损害其品质、发芽潜力和市场价值。传统的视觉检测方法主观性强，且仅限于检测外部缺陷，往往无法发现内部损伤。本研究提出了一种利用X射线成像结合人工智能技术对油菜种子机械损伤进行无损分类的方法。将三种含水率（6%、8%和12%）的种子分别施加四个水平的冲击能量（0、3、3.7和4.3 mJ），共生成3,029张射线图像，并标注为四个不同程度的机械损伤等级（无损伤、低损伤、中等损伤和高损伤）。研究开发了两条分析流程。第一条采用机器学习方法，提取了37个特征，包括基于强度的指标、纹理描述符、频域特征以及快速傅里叶变换（FFT）特征，开发并评估了支持向量机（SVM）和随机森林（RF）模型，其中SVM在特征选择后达到了74.01%的最高准确率。第二条流程通过迁移学习在五个预训练卷积神经网络上实现深度学习：MobileNetV2、EfficientNet-B0、Xception、ResNet50和DenseNet121。其中，MobileNetV2达到了91.42%的最高准确率，优于所有传统机器学习模型。结果表明，将射线成像与人工智能方法相结合，有望实现对油菜种子机械损伤的快速、可靠且无损的分类，为油料种子供应链中的智能质量控制系统的建立奠定了基础。","2026-09-20T00:00:00Z",{"impact":17,"substance":72,"depth":17,"authority":19,"freshness":74,"relevant":20,"comment":128},"将X射线成像与深度学习结合用于油菜籽内部机械损伤无损分级，方法新颖、数据规模可观，对油料供应链智能质控有参考价值。",[130],{"name":10,"url":123},[25,79,26,132,133],"油菜","种子质量",[135,136],"X射线 油菜籽 机械损伤","MobileNetV2 种子 无损检测","X射线油菜籽机械损伤-3054","10.1016\u002Fj.compag.2026.112350",{"doi":138,"openalex_id":140,"authors":141,"venue":10,"cited_by_count":34,"oa_url":123,"card":158,"direction":117,"ingested_from":58},"W7213778300",[142,144,147,149,152,155],{"name":143,"orcid":9},"M. Buragohain",{"name":145,"orcid":146},"L. G. Divyanth","https:\u002F\u002Forcid.org\u002F0000-0002-1121-1642",{"name":148,"orcid":9},"Taranveer Singh",{"name":150,"orcid":151},"Muhammad Mudassir Arif Chaudhry","https:\u002F\u002Forcid.org\u002F0000-0001-5316-4701",{"name":153,"orcid":154},"Jitendra Paliwal","https:\u002F\u002Forcid.org\u002F0000-0002-1665-3626",{"name":156,"orcid":157},"Mohammad Nadimi","https:\u002F\u002Forcid.org\u002F0000-0002-4550-7572",{"tldr":159,"method":160,"finding":161,"direction":117,"opportunity":162},"用X射线成像结合机器学习与深度学习，无损分类油菜籽机械损伤等级。","3029张X射线图像，提取37个特征训练SVM\u002FRF，并用五种预训练CNN迁移学","MobileNetV2准确率达91.42%，显著优于传统机器学习最高74.01%。","可拓展至多作物种子内部损伤实时检测，并融合高光谱或近红外实现便携式在线分选。","2026-09-21T23:30:02.090869Z",{"id":165,"title":166,"url":167,"summary":168,"summary_zh":169,"content":9,"source_name":170,"source_url":167,"published_at":171,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":172,"score_detail":173,"sources":177,"tags":179,"search_phrases":182,"slug":185,"view_count":34,"doi":186,"paper":187,"created_at":204},3017,"Automated seedling vigor estimation in cucumbers using digital image processing","https:\u002F\u002Fdoi.org\u002F10.65764\u002Ftjas.2026.267412","Background and Objective: Farmers often rely on experience rather than quantitative indicators to determine seedling quality, resulting in inefficiencies in resource allocation. This study aimed to (1) compare seedling vigor and growth characteristics between open-pollinated (OP) and F1 hybrid cucumber seed types, and (2) develop a non-destructive predictive model for seedling vigor estimation based on digital image analysis.Methodology: Seedling images were acquired under controlled LED lighting using a 5 MP digital camera positioned 30 cm above 14-day-old seedlings. A YOLOv8-based object detection model was applied to detect and isolate true leaf regions, from which pixel count and RGB color values were extracted as input features for a multiple linear regression model to predict the seedling vigor index (SVI).Main Results: The YOLOv8 object detection model achieved a mean average precision (mAP@0.5) of 96.00%, precision of 93.40%, and recall of 94.40% in detecting true leaves. Multiple linear regression analysis was conducted using the Scikit-learn library in Python. Scikit-learn provides regression-based machine learning algorithms, including multiple linear regression. The equation was then applied to the training and test sets, using the pixel values of true leaves as the independent variable and SVI as the dependent variable. Using multiple regression analysis, the trained model generated the equation SVI = -2.27 + (2.84 × 10-5 × pixel) + (3.48 × 10-5 × R) + (-7.49 × 10-2 × G) + (2.06 × 10-1 × B), R2 = 0.57 and RMSE = 1.10. The model achieved the test set, r = 0.79 and RMSE = 1.34. The model performance showed a correlation coefficient of 0.85 for OP data and 0.91 for F1 hybrid data.Conclusions: The results demonstrate that digital image processing combined with object detection provides a non-destructive and effective approach to estimate seedling vigor quality. The predictive models developed from OP and F1 hybrid datasets indicate potential application in precision agriculture for automated seedling quality assessment and transplanting decision support.","背景与目标：农民通常依赖经验而非定量指标来判断幼苗质量，导致资源配置效率低下。本研究旨在（1）比较开放授粉（OP）与F1杂交黄瓜种子类型之间的幼苗活力和生长特性，（2）基于数字图像分析开发一种用于幼苗活力评估的无损预测模型。方法：使用500万像素数码相机置于14日龄幼苗上方30 cm处，在受控LED光照下获取幼苗图像。应用基于YOLOv8的目标检测模型检测并分离真叶区域，从中提取像素计数和RGB颜色值作为多元线性回归模型的输入特征，以预测幼苗活力指数（SVI）。主要结果：YOLOv8目标检测模型在检测真叶时达到了96.00%的平均精度均值（mAP@0.5）、93.40%的精确率和94.40%的召回率。使用Python中的Scikit-learn库进行多元线性回归分析。Scikit-learn提供基于回归的机器学习算法，包括多元线性回归。随后将该方程应用于训练集和测试集，以真叶像素值作为自变量，SVI作为因变量。通过多元回归分析，训练模型生成的方程为SVI = -2.27 + (2.84 × 10-5 × 像素) + (3.48 × 10-5 × R) + (-7.49 × 10-2 × G) + (2.06 × 10-1 × B)，R2 = 0.57，RMSE = 1.10。该模型在测试集上达到r = 0.79，RMSE = 1.34。模型性能显示，OP数据的相关系数为0.85，F1杂交数据的相关系数为0.91。结论：结果表明，数字图像处理结合目标检测为评估幼苗活力质量提供了一种无损且有效的方法。基于OP和F1杂交数据集开发的预测模型表明，其在精准农业中具有用于自动化幼苗质量评估和移栽决策支持的潜在应用。","Thai Journal of Agricultural Science","2026-09-19T00:00:00Z",71,{"impact":174,"substance":18,"depth":175,"authority":174,"freshness":74,"relevant":20,"comment":176},12,17,"基于YOLOv8与多元回归的黄瓜幼苗活力无损估测，方法具体、指标完整，对智慧育苗有参考价值，但属细分作物研究，影响范围有限。",[178],{"name":170,"url":167},[25,79,26,180,181],"图像识别","黄瓜育苗",[183,184],"黄瓜 幼苗活力 图像处理","YOLOv8 幼苗 检测","黄瓜幼苗活力图像处理-3017","10.65764\u002Ftjas.2026.267412",{"doi":186,"openalex_id":188,"authors":189,"venue":170,"cited_by_count":34,"oa_url":167,"card":198,"direction":203,"ingested_from":58},"W7213631983",[190,192,194,196],{"name":191,"orcid":9},"Thanabodee Withunchettanan",{"name":193,"orcid":9},"Raksak Sermsak",{"name":195,"orcid":9},"Pichittra Kaewsorn",{"name":197,"orcid":9},"Kriengkri Kaewtrakulpong",{"tldr":199,"method":200,"finding":201,"direction":117,"opportunity":202},"用YOLOv8检测黄瓜真叶并结合多元回归，实现幼苗活力指数无损预测。","LED下拍摄14天幼苗，YOLOv8分割真叶，提取像素与RGB做多元线性回归。","YOLOv8检测mAP@0.5达96%，模型测试r=0.79，F1杂交种相关性达0.91。","可扩展多品种、多环境数据，融合时序图像与深度学习提升活力预测泛化性。","数字乡村与农业信息化","2026-09-20T23:30:26.929607Z",{"id":206,"title":207,"url":208,"summary":209,"summary_zh":210,"content":9,"source_name":211,"source_url":208,"published_at":212,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":15,"score_detail":213,"sources":215,"tags":217,"search_phrases":220,"slug":223,"view_count":34,"doi":224,"paper":225,"created_at":261},2656,"DeepPhenoTree-Apple Edition: a multi-site apple phenology RGB annotated dataset with deep learning baseline models","https:\u002F\u002Fdoi.org\u002F10.1186\u002Fs13007-026-01591-w","Abstract In machine learning-driven plant phenotyping, well-annotated image datasets are essential for developing robust models capable of capturing phenological variability across environments. Here, we introduce DeepPhenoTree-Apple Edition , a multi-site and multi-variety RGB image dataset dedicated to the detection of key phenological stages in apple trees. The dataset includes 48,320 time-stamped RGB images acquired across four European orchards of the Apple REFPOP consortium under biological, agronomic, and environmental variability, including differences in genotypes, orchard architectures, phenological development, temperature, and humidity conditions. From this large corpus, a carefully curated subset of 808 representative images was manually annotated. It includes 241,600 expert annotations covering developmental stages from dormant bud to fruit maturity. Images were acquired using a standardized tractor-mounted phenotyping platform equipped with active flash illumination. Active flash illumination was used to reduce illumination variability and homogenize exposure, shadows, and sunlight differences across sites. Phenological structures were annotated following the BBCH scale, with bounding boxes adapted to organ visibility and developmental stage. In addition to the dataset, we provide deep-learning baseline experiments to illustrate detection performance and detection performance across locations.","在机器学习驱动的植物表型分析中，标注良好的图像数据集对于开发能够捕捉不同环境下物候变异的稳健模型至关重要。在此，我们介绍DeepPhenoTree-Apple Edition，一个用于检测苹果树关键物候阶段的多地点、多品种RGB图像数据集。该数据集包含48,320张带有时间戳的RGB图像，采集自Apple REFPOP联盟的四个欧洲果园，涵盖了生物学、农艺学和环境变异，包括基因型、果园结构、物候发育、温度和湿度条件的差异。从这一大型语料库中，我们精心挑选了808张具有代表性的图像子集进行人工标注。该子集包含241,600条专家标注，覆盖从休眠芽到果实成熟的发育阶段。图像使用配备主动闪光照明（active flash illumination）的标准化拖拉机搭载表型分析平台采集。主动闪光照明用于减少光照变异性，并统一不同地点的曝光、阴影和阳光差异。物候结构按照BBCH scale（BBCH尺度）进行标注，边界框根据器官可见性和发育阶段进行调整。除数据集外，我们还提供了深度学习基线实验，以展示检测性能以及不同地点的检测性能。","Plant Methods","2026-09-15T00:00:00Z",{"impact":17,"substance":72,"depth":17,"authority":19,"freshness":74,"relevant":20,"comment":214},"多站点苹果物候RGB标注数据集，规模大、标注专业并附深度学习基线，对作物表型与智慧果园研究有实质参考价值。",[216],{"name":211,"url":208},[25,79,27,218,219],"表型组学","图像数据集",[221,222],"农业人工智能 图像数据集 智慧农业 表型组学","农业人工智能 图像数据集","农业人工智能图像数据集智慧农业表型组学-2656","10.1186\u002Fs13007-026-01591-w",{"doi":224,"openalex_id":226,"authors":227,"venue":211,"cited_by_count":34,"oa_url":208,"card":256,"direction":56,"ingested_from":58},"W7213246841",[228,231,233,235,238,241,244,246,248,250,252,254],{"name":229,"orcid":230},"Herearii Metuarea","https:\u002F\u002Forcid.org\u002F0009-0008-2716-0617",{"name":232,"orcid":9},"Abdoul-Djalil Ousseini-Hamza",{"name":234,"orcid":9},"Walter Guerra",{"name":236,"orcid":237},"Francesca Zuffa","https:\u002F\u002Forcid.org\u002F0009-0009-8797-0205",{"name":239,"orcid":240},"Francesco Panzeri","https:\u002F\u002Forcid.org\u002F0009-0005-8774-5201",{"name":242,"orcid":243},"Andrea Patocchi","https:\u002F\u002Forcid.org\u002F0000-0002-0919-2702",{"name":245,"orcid":9},"Lidia Lozano",{"name":247,"orcid":9},"Shauny Van Hoye",{"name":249,"orcid":9},"François Laurens",{"name":251,"orcid":9},"Jeremy Labrosse",{"name":253,"orcid":9},"Pejman Rasti",{"name":255,"orcid":9},"David Rousseau",{"tldr":257,"method":258,"finding":259,"direction":56,"opportunity":260},"发布多站点多品种苹果物候RGB图像数据集，并给出深度学习基线检测模型。","采集4个欧洲果园48320张图像，精选808张按BBCH标框标注，用主动闪光和拖","数据集覆盖从休眠芽到果实成熟的241600个标注，基线模型可跨地点检测物候期。","可基于该多站点数据研究跨环境域适应与物候期细粒度检测，提升模型泛化能力。","2026-09-16T23:30:24.293828Z",{"id":263,"title":264,"url":265,"summary":266,"summary_zh":267,"content":9,"source_name":10,"source_url":265,"published_at":268,"category":12,"cover_url":9,"hotness":13,"is_selected":269,"score":270,"score_detail":271,"sources":276,"tags":278,"search_phrases":281,"slug":284,"view_count":20,"doi":285,"paper":286,"created_at":306},1755,"A non-destructive watermelon sweetness classification via vision transformer with cross-modal knowledge distillation","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.compag.2026.112393","A non-destructive watermelon sweetness classification via vision transformer with cross-modal knowledge distillation。Computers and Electronics in Agriculture","基于视觉Transformer与跨模态知识蒸馏的非破坏性西瓜甜度分类方法。计算机与农业电子学","2026-09-05T00:00:00Z",true,70,{"impact":272,"substance":273,"depth":17,"authority":272,"freshness":274,"relevant":20,"comment":275},15,20,2,"提出基于视觉Transformer与跨模态知识蒸馏的无损西瓜甜度分级方法，发表于权威期刊，方法新颖，对农产品品质检测有参考价值。",[277],{"name":10,"url":265},[25,79,26,279,280],"西瓜","模型蒸馏",[282,283],"农业人工智能 无损检测 智慧农业 模型蒸馏","农业人工智能 无损检测","农业人工智能无损检测智慧农业模型蒸馏-1755","10.1016\u002Fj.compag.2026.112393",{"doi":285,"openalex_id":287,"authors":288,"venue":10,"cited_by_count":34,"oa_url":9,"card":301,"direction":117,"ingested_from":58},"W7208829728",[289,292,295,298],{"name":290,"orcid":291},"Mustafa Kareem Hadi","https:\u002F\u002Forcid.org\u002F0000-0001-6469-3799",{"name":293,"orcid":294},"Siti Khairunniza Bejo","https:\u002F\u002Forcid.org\u002F0000-0002-4972-1701",{"name":296,"orcid":297},"Abdul Rashid Mohamed Shariff","https:\u002F\u002Forcid.org\u002F0000-0003-4626-4995",{"name":299,"orcid":300},"Nazmi Mat Nawi","https:\u002F\u002Forcid.org\u002F0000-0002-5916-5745",{"tldr":302,"method":303,"finding":304,"direction":117,"opportunity":305},"提出用视觉Transformer结合跨模态知识蒸馏，实现西瓜甜度的无损分类。","视觉Transformer与跨模态知识蒸馏，利用光谱数据辅助图像模型训练。","跨模态蒸馏可提升图像模型对西瓜甜度的分类精度，实现无损检测。","可探索将跨模态蒸馏用于其他水果内部品质（如糖度、酸度）的无损检测，或结合多传感器数据提升模型泛化性。","2026-09-06T23:30:01.479655Z",{"id":308,"title":309,"url":310,"summary":311,"summary_zh":312,"content":9,"source_name":313,"source_url":310,"published_at":314,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":172,"score_detail":315,"sources":318,"tags":320,"search_phrases":322,"slug":324,"view_count":34,"doi":325,"paper":326,"created_at":349},1536,"A Non-Invasive Approach for Detecting Water Adulteration in Orange Juice Using Computer Vision and Deep Learning","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.jfca.2026.109484","Food fraud due to water adulteration in orange juice is a significant concern for the beverage industry. This study aimed to develop a rapid and non-invasive computer-vision method based on deep learning to detect and classify water-adulteration levels ranging from 1% to 15% in three orange juice products representing freshly squeezed juice, juice from concentrate, and orange nectar. High-resolution images were acquired under two shutter-speed-based exposure conditions: the higher-exposure acquisition condition (1\u002F30 s) and the lower-exposure acquisition condition (1\u002F250 s), while maintaining constant scene illumination, and were analyzed using ResNet50 convolutional neural networks. In the image-level hold-out test, the model trained with images acquired at 1\u002F250 s achieved an accuracy of 88.3%, whereas the model trained at 1\u002F30 s reached 83.1%. To provide a more stringent assessment of sample-level transferability, the previously trained and fixed 1\u002F250 s model was subsequently evaluated using 240 independently prepared and blindly coded samples obtained from subsequent purchases of the same commercial products. This independent blind validation achieved a 24-class accuracy of 86.7%. Misclassifications occurred predominantly between adjacent or closely related water-adulteration levels within the same juice product. When the independent-validation predictions were collapsed into a binary pure-versus-adulterated screening task, the model achieved 100.0% sensitivity, 93.3% specificity, 99.2% accuracy, and 96.7% balanced accuracy. These findings support the feasibility of the method as a rapid pre-screening tool for detecting visible-image patterns associated with controlled water dilution and show that detecting the presence of adulteration is more reliable than assigning an exact value to closely spaced adulteration levels. The independent validation provides evidence of transferability to newly prepared samples from subsequent purchases of the same products. However, further validation across additional brands, production batches, orange origins, seasons, and acquisition environments is required before broader applicability can be established.","橙汁中因掺水导致的食品欺诈是饮料行业关注的重要问题。本研究旨在开发一种基于深度学习的快速、非侵入性计算机视觉方法，用于检测和分类三种橙汁产品（鲜榨汁、浓缩还原汁和橙汁饮料）中1%至15%的掺水水平。在保持场景照明恒定的条件下，分别以两种基于快门速度的曝光条件采集高分辨率图像：高曝光采集条件（1\u002F30秒）和低曝光采集条件（1\u002F250秒），并使用ResNet50卷积神经网络进行分析。在图像级留出测试中，以1\u002F250秒采集图像训练的模型准确率达到88.3%，而以1\u002F30秒训练的模型准确率为83.1%。为对样本级可迁移性进行更严格的评估，先前训练并固定的1\u002F250秒模型随后被用于评估240个独立制备、盲法编码的样本，这些样本来自后续购买的同款商业产品。该独立盲法验证实现了24类别86.7%的准确率。误分类主要发生在同一果汁产品内相邻或相近的掺水水平之间。当独立验证预测结果被合并为二元纯正与掺假筛查任务时，模型实现了100.0%的灵敏度、93.3%的特异度、99.2%的准确率和96.7%的平衡准确率。这些发现支持该方法作为快速预筛查工具的可行性，用于检测与受控水稀释相关的可见图像模式，并表明检测掺假的存在比精确赋值于间隔较小的掺水水平更为可靠。独立验证提供了该方法对后续购买同款产品新制备样本具有可迁移性的证据。然而，在确立更广泛的适用性之前，仍需在更多品牌、生产批次、橙子产地、季节和采集环境中进行进一步验证。","Journal of Food Composition and Analysis","2026-09-01T00:00:00Z",{"impact":174,"substance":273,"depth":17,"authority":19,"freshness":316,"relevant":20,"comment":317},7,"研究提出基于深度学习的橙汁掺水无损检测方法，独立验证准确率高，对食品安全监管有参考价值。",[319],{"name":313,"url":310},[25,79,26,321],"食品安全",[323,283],"农业人工智能 无损检测 智慧农业 食品安全","农业人工智能无损检测智慧农业食品安全-1536","10.1016\u002Fj.jfca.2026.109484",{"doi":325,"openalex_id":327,"authors":328,"venue":313,"cited_by_count":34,"oa_url":343,"card":344,"direction":117,"ingested_from":58},"W7204957864",[329,331,333,336,338,340],{"name":330,"orcid":9},"Ana M. Pérez-Calabuig",{"name":332,"orcid":9},"Sandra Pradana‐López",{"name":334,"orcid":335},"John C. Cancilla","https:\u002F\u002Forcid.org\u002F0000-0003-3645-7224",{"name":337,"orcid":9},"María Luz Mena",{"name":339,"orcid":9},"Carlos López-Pingarrón",{"name":341,"orcid":342},"José S. Torrecilla","https:\u002F\u002Forcid.org\u002F0000-0003-1209-203X","https:\u002F\u002Fwww.sciencedirect.com\u002Fscience\u002Farticle\u002Fpii\u002FS0889157526006277\u002Fpdf",{"tldr":345,"method":346,"finding":347,"direction":117,"opportunity":348},"用深度学习计算机视觉检测橙汁掺水，实现非侵入式快速筛查。","采集不同曝光图像，用ResNet50分类掺水等级，独立盲样验证。","掺水检测准确率高，区分精确等级较难，模型可迁移至新样本。","可扩展至其他食品掺假检测，需跨品牌、批次、季节等验证，或开发便携设备。","2026-09-03T23:30:50.596809Z"]