[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-3017":3,"related-3017":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":70,"slug":73,"view_count":74,"doi":75,"paper":76,"created_at":96},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。",null,"Thai Journal of Agricultural Science","2026-09-19T00:00:00Z","论文",10,false,71,{"impact":17,"substance":18,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":22},12,21,17,9,1,"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。",[24],{"name":10,"url":6},[26,27,28,29,30],"智慧农业","农业人工智能","无损检测","图像识别","黄瓜育苗",{"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},[44,54,62],{"code":32,"score":45,"supported":46,"title":47,"abstract":48,"tags":53},11,true,[],[49,50,51,52],"目标检测","机器学习","machine learning","decision support",[27,29],{"code":55,"score":56,"supported":46,"title":57,"abstract":58,"tags":61},"I1",7,[],[59,60],"精准农业","precision agriculture",[26],{"code":63,"score":64,"supported":46,"title":65,"abstract":66,"tags":69},"B1",6,[],[67,68],"黄瓜","cucumber",[],[71,72],"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",2,"10.65764\u002Ftjas.2026.267412",{"doi":75,"openalex_id":77,"authors":78,"venue":10,"cited_by_count":87,"oa_url":6,"card":88,"direction":94,"ingested_from":95},"W7213631983",[79,81,83,85],{"name":80,"orcid":9},"Thanabodee Withunchettanan",{"name":82,"orcid":9},"Raksak Sermsak",{"name":84,"orcid":9},"Pichittra Kaewsorn",{"name":86,"orcid":9},"Kriengkri Kaewtrakulpong",0,{"tldr":89,"method":90,"finding":91,"direction":92,"opportunity":93},"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。","农业人工智能与决策模型","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","openalex","2026-09-20T23:30:26.929607Z",{"total":64,"page":21,"page_size":64,"items":98},[99,205,266,323,377,440],{"id":100,"title":101,"url":102,"summary":103,"summary_zh":104,"content":9,"source_name":105,"source_url":102,"published_at":106,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":107,"score_detail":108,"sources":112,"tags":114,"primary_topic":117,"secondary_topics":122,"classification":124,"search_phrases":179,"slug":182,"view_count":87,"doi":183,"paper":184,"created_at":204},5582,"AI-Based RGB Imaging and 14-Channel Multispectral Food Quality Scanner","https:\u002F\u002Fdoi.org\u002F10.22214\u002Fijraset.2026.85255","Food-quality assessment plays an important role in food safety, agricultural processing, storage, and supply-chain management. Conventional inspection methods depend on visual judgement or laboratory-based testing, which may require considerable time, specialised equipment, and manual effort. Recent developments in computer vision, multispectral sensing, and artificial intelligence (AI) provide opportunities for automated, non-destructive food-quality assessment. This paper reviews the application of RGB imaging, multispectral sensing, machine learning, and deep learning for fruit-ripeness estimation, surfacedefect detection, grain classification, and food-authenticity assessment. The reviewed literature demonstrates that RGB imaging can extract visible features such as colour, shape, and texture, whereas multispectral sensing provides wavelength-dependent information associated with food characteristics. Published studies have reported promising results in tomato-ripeness classification, apple-defect detection, rice-grain analysis, and honey-adulteration screening. Based on these findings, a compact food-quality scanner integrating an RGB camera, a 14-channel multispectral sensor, controlled illumination, and an embedded processing unit is proposed. The review discusses sensor integration, data preprocessing, feature fusion, classification methods, evaluation metrics, and implementation challenges. It also identifies the need for broader datasets, standardised illumination, independent validation, and resource-efficient AI models to improve the reliability of portable food-inspection systems.","食品质量评估在食品安全、农产品加工、储运和供应链管理中发挥着重要作用。传统检测方法依赖人工目视判断或实验室检测，往往需要较长时间、专用设备和人工投入。近年来，计算机视觉、多光谱传感和人工智能（AI）的发展为自动化、无损食品质量评估提供了新的机遇。本文综述了RGB成像、多光谱传感、机器学习和深度学习在果实成熟度估计、表面缺陷检测、谷物分类和食品真实性评估中的应用。所综述的文献表明，RGB成像能够提取颜色、形状和纹理等可见特征，而多光谱传感则可提供与食品特性相关的波长依赖信息。已发表研究在番茄成熟度分类、苹果缺陷检测、稻米籽粒分析和蜂蜜掺假筛查等方面取得了良好结果。基于上述研究结果，本文提出一种集成了RGB相机、14通道多光谱传感器、受控照明和嵌入式处理单元的紧凑型食品质量扫描仪。综述讨论了传感器集成、数据预处理、特征融合、分类方法、评价指标和实现挑战，并指出需要更广泛的数据集、标准化照明、独立验证和资源高效的AI模型，以提高便携式食品检测系统的可靠性。","International Journal for Research in Applied Science and Engineering Technology","2026-10-21T00:00:00Z",65,{"impact":17,"substance":109,"depth":110,"authority":20,"freshness":13,"relevant":21,"comment":111},18,16,"综述性论文，提出集成RGB与14通道多光谱的便携食品质量扫描仪方案，方法框架清晰但缺乏自有实验数据，属细分领域技术进展，可作参考但非重大突破。",[113],{"name":105,"url":102},[26,27,28,115,116],"多光谱成像","食品质量检测",{"code":118,"slug":119,"name":120,"path":121,"parent_code":36},"I3","remote-sensing","遥感与农情监测","\u002Ftopic\u002Fdigital-agriculture\u002Fremote-sensing",[123],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":125},{"candidates":126,"agricultural_evidence":46},[127,132,141,148,155,161,168,175],{"code":118,"score":17,"supported":46,"title":128,"abstract":130,"tags":131},[129],"multispectral",[129],[115],{"code":32,"score":13,"supported":46,"title":133,"abstract":134,"tags":140},[],[135,50,136,51,137,138,139],"深度学习","计算机视觉","deep learning","artificial intelligence","computer vision",[27],{"code":142,"score":56,"supported":46,"title":143,"abstract":144,"tags":147},"E4",[],[145,146],"食品安全","food safety",[28],{"code":149,"score":64,"supported":46,"title":150,"abstract":151,"tags":154},"A1",[],[152,153],"稻米","rice",[],{"code":63,"score":64,"supported":46,"title":156,"abstract":157,"tags":160},[],[158,159],"番茄","tomato",[],{"code":162,"score":64,"supported":46,"title":163,"abstract":164,"tags":167},"B2",[],[165,166],"苹果","apple",[],{"code":169,"score":170,"supported":46,"title":171,"abstract":172,"tags":174},"E1",3,[],[173],"农产品加工",[],{"code":55,"score":21,"supported":14,"title":176,"abstract":177,"tags":178},[],[],[26],[180,181],"RGB成像 多光谱 食品质量","14通道 多光谱 扫描仪","RGB成像多光谱食品质量-5582","10.22214\u002Fijraset.2026.85255",{"doi":183,"openalex_id":185,"authors":186,"venue":105,"cited_by_count":87,"oa_url":102,"card":199,"direction":94,"ingested_from":95},"W7223139905",[187,189,191,193,195,197],{"name":188,"orcid":9},"Eshwari Take",{"name":190,"orcid":9},"Avanish Raut",{"name":192,"orcid":9},"Vedant Joshi",{"name":194,"orcid":9},"Shravani Akolkar",{"name":196,"orcid":9},"R. M. Gharat",{"name":198,"orcid":9},"Shruti Dive",{"tldr":200,"method":201,"finding":202,"direction":92,"opportunity":203},"综述RGB与多光谱成像结合AI的食品品质无损检测，并提出一款集成14通道多光谱传感器的紧凑型扫描仪方","综述RGB成像、多光谱传感与机器学习\u002F深度学习在食品检测中的应用，并提出传感器集","RGB与多光谱融合在番茄成熟度、苹果缺陷、稻米分类和蜂蜜掺假检测中表现良好。","可研究多源光谱特征融合与轻量化模型，构建标准化数据集和便携设备独立验证体系。","2026-10-22T11:20:48.684398Z",{"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":213,"score_detail":214,"sources":217,"tags":219,"primary_topic":221,"secondary_topics":226,"classification":228,"search_phrases":245,"slug":248,"view_count":87,"doi":249,"paper":250,"created_at":265},5387,"LIGHTWEIGHT FEATURE FUSED ATTENTION DRIVEN MULTI LEVEL HYBRID MOBILENET V2 FRAMEWORK FOR EARLY TOMATO LEAF DISEASE DETECTION","https:\u002F\u002Fdoi.org\u002F10.62441\u002Factainnovations.vi.1918","It is crucial to find and treat tomato crop diseases early and accurately to improve crop efficiency and make sure there is no scarcity of crop. However, computerized analysis is provocative due to lighting differences, compound backgrounds, and graphic likenesses among infection regions. Research work proposed a lightweight Attention Driven Multi Level Hybrid MobileNet-V2 algorithm that fuses the handcrafted texture structures with Deep Learning (DL) features, then image preprocessing is incorporated using Gaussian, Bilateral, and Guided filters to minimize noise by preserving the disease lesions. LAB color model along with Contrast Limited Adaptive Histogram Equalization (CLAHE) are used to improve the contrast. Otsu thresholding is involved to effectively separate the leaf segment from diseased parts. Local Binary Pattern (LBP) and Gray Level Co-occurrence Matrix (GLCM) descriptors are used to obtain fine-grained features, while high level features are gained by pretrained MobileNet-V2 model. Features fusion is fed to the fully connected layers of deep learning model for training and validation purpose. Results have revealed that the suggested model attains better classification performance as compared to existing models, making it an effective explanation for real time tomato leaf disease detection for smart agriculture applications.","早期准确发现并治疗番茄作物病害，对于提高作物效率、确保作物不短缺至关重要。然而，由于光照差异、复杂背景以及感染区域之间的图形相似性，计算机化分析具有挑战性。研究提出了一种轻量级注意力驱动多层混合MobileNet-V2算法，该算法将手工纹理结构与深度学习（DL）特征相融合，然后结合高斯、双边和引导滤波器进行图像预处理，以在保留病害病变的同时最小化噪声。采用LAB颜色模型和对比度受限自适应直方图均衡化（CLAHE）来提高对比度。引入Otsu阈值分割以有效分离叶片片段与病害部分。使用局部二值模式（LBP）和灰度共生矩阵（GLCM）描述符获取细粒度特征，同时通过预训练的MobileNet-V2模型获取高层特征。特征融合后输入深度学习模型的全连接层进行训练和验证。结果表明，与现有模型相比，所提出的模型取得了更好的分类性能，使其成为智慧农业应用中实时番茄叶片病害检测的有效解决方案。","Acta Innovations","2026-10-18T00:00:00Z",64,{"impact":17,"substance":109,"depth":110,"authority":13,"freshness":215,"relevant":21,"comment":216},8,"提出融合手工纹理特征与MobileNet-V2的轻量番茄叶病检测方法，有一定方法新颖性，但属单篇论文、应用范围有限，未达每日精选门槛。",[218],{"name":211,"url":208},[26,27,135,220,29],"番茄病害",{"code":63,"slug":222,"name":223,"path":224,"parent_code":225},"vegetables","蔬菜","\u002Ftopic\u002Fhorticulture\u002Fvegetables","B",[227],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":229},{"candidates":230,"agricultural_evidence":46},[231,236,240],{"code":63,"score":232,"supported":46,"title":233,"abstract":234,"tags":235},15,[159],[158,159],[220],{"code":32,"score":20,"supported":46,"title":237,"abstract":238,"tags":239},[],[135,137],[27,29,135],{"code":55,"score":56,"supported":46,"title":241,"abstract":242,"tags":244},[],[26,243],"smart agriculture",[26],[246,247],"MobileNet-V2 番茄叶片病害","番茄叶病 深度学习 检测","MobileNet-V2番茄叶片病害-5387","10.62441\u002Factainnovations.vi.1918",{"doi":249,"openalex_id":251,"authors":252,"venue":211,"cited_by_count":87,"oa_url":258,"card":259,"direction":264,"ingested_from":95},"W7222977034",[253,255],{"name":254,"orcid":9},"Mrs. Vijetalaxmi A Hegde",{"name":256,"orcid":257},"Abhay Ramakant Kasetwar","https:\u002F\u002Forcid.org\u002F0000-0002-5800-8764","https:\u002F\u002Factainnovations.com\u002Findex.php\u002Fpub\u002Farticle\u002Fdownload\u002F1918\u002F981",{"tldr":260,"method":261,"finding":262,"direction":92,"opportunity":263},"提出轻量注意力多级混合MobileNet-V2框架，融合手工纹理与深度学习特征实现番茄叶片病害早期检","高斯\u002F双边\u002F引导滤波去噪，LAB+CLAHE增强，Otsu分割，LBP与GLCM","所提模型分类性能优于现有模型，可有效用于智慧农业实时番茄叶片病害检测。","可探索多作物跨域泛化、田间复杂光照下轻量模型部署与可解释性，及病害早期分级检测。","智慧农业 \u002F 农业物联网","2026-10-20T23:30:10.424511Z",{"id":267,"title":268,"url":269,"summary":270,"summary_zh":271,"content":9,"source_name":272,"source_url":269,"published_at":273,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":274,"score_detail":275,"sources":277,"tags":279,"primary_topic":282,"secondary_topics":283,"classification":284,"search_phrases":305,"slug":308,"view_count":21,"doi":309,"paper":310,"created_at":322},4833,"From Handcrafted Descriptors to Deep Embeddings: A Comparative Study for Regional Hazelnut Image Classification","https:\u002F\u002Fdoi.org\u002F10.15832\u002Fankutbd.1946462","Automated regional hazelnut image classification is relevant for agricultural traceability, origin aware sorting, and decision support in food processing systems. This study presents a unified comparative benchmark for three class hazelnut image classification using Van, Ordu, and Giresun categories. The experimental framework integrates classical handcrafted descriptors, deep transfer learning models, and reduced space hybrid classical and quantum compatible classifiers. In the classical branch, HOG and RGB color histogram features were combined and evaluated using KNN and SVM based classifiers. In the deep learning branch, ResNet50 and EfficientNetB0 were trained through a two stage transfer learning strategy, and Grad-CAM was employed to provide qualitative interpretability. In the hybrid branch, 1280 dimensional embeddings extracted from the GAP layer of the best EfficientNetB0 model were compressed into low dimensional spaces for reduced space SVC and QSVC experiments. Under the present image level split protocol, the strongest handcrafted baseline, PCA + SVM, achieved 98.90% accuracy and 0.9888 macro-F1, while ResNet50 reached 99.45% accuracy and 0.9945 macro-F1. EfficientNetB0 achieved 100% test accuracy, while the reduced space classical SVC trained on compressed EfficientNetB0 embeddings attained 99.63% accuracy and 0.9961 macro F1 on the full test set. Under the fair subset protocol, classical SVC achieved 98.33% accuracy, whereas the best QSVC configuration reached 90.83% accuracy. This result should be interpreted as a strong within dataset benchmark rather than definitive evidence of object level or deployment level generalization. Grad-CAM visualizations further suggested that the deep models focused primarily on semantically meaningful hazelnut regions rather than on background patterns. The findings indicate that representation quality is a central factor in this task, while the reported near ceiling performance should be interpreted in light of the image level partitioning strategy","区域榛子图像的自动化分类对于农业可追溯性、产地感知分选以及食品加工系统中的决策支持具有重要意义。本研究提出了一个统一的比较基准，用于对Van、Ordu和Giresun三个类别的榛子图像进行分类。实验框架整合了经典手工设计描述子、深度迁移学习模型以及降维空间下的经典与量子兼容混合分类器。在经典分支中，将HOG与RGB颜色直方图特征进行组合，并使用基于KNN和SVM的分类器进行评估。在深度学习分支中，通过两阶段迁移学习策略训练ResNet50和EfficientNetB0，并采用Grad-CAM提供定性可解释性。在混合分支中，将从最佳EfficientNetB0模型GAP层提取的1280维嵌入压缩至低维空间，用于降维空间SVC和QSVC实验。在当前图像级划分协议下，最强的手工特征基线PCA + SVM达到了98.90%的准确率和0.9888的宏F1值，而ResNet50达到了99.45%的准确率和0.9945的宏F1值。EfficientNetB0达到了100%的测试准确率，而在压缩后的EfficientNetB0嵌入上训练的降维空间经典SVC在完整测试集上达到了99.63%的准确率和0.9961的宏F1值。在公平子集协议下，经典SVC达到了98.33%的准确率，而最佳QSVC配置达到了90.83%的准确率。该结果应被解释为数据集内的强基准，而非对象级或部署级泛化能力的确定性证据。Grad-CAM可视化进一步表明，深度模型主要关注具有语义意义的榛子区域，而非背景模式。研究结果表明，表征质量是该任务的核心因素，而所报告的接近上限的性能应结合图像级划分策略来理解。","Tarım Bilimleri Dergisi","2026-10-12T00:00:00Z",63,{"impact":215,"substance":109,"depth":110,"authority":17,"freshness":20,"relevant":21,"comment":276},"方法对比扎实、结论可靠，但属数据集内基准研究，产业落地价值有限，可作技术参考而非每日精选头条。",[278],{"name":272,"url":269},[26,27,280,29,281],"农产品溯源","榛子",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":285},{"candidates":286,"agricultural_evidence":46},[287,291,297,301],{"code":32,"score":45,"supported":46,"title":288,"abstract":289,"tags":290},[],[135,137,52],[27,29],{"code":169,"score":64,"supported":46,"title":292,"abstract":293,"tags":296},[],[294,295],"食品加工","food processing",[],{"code":142,"score":21,"supported":14,"title":298,"abstract":299,"tags":300},[],[],[280],{"code":55,"score":21,"supported":14,"title":302,"abstract":303,"tags":304},[],[],[26],[306,307],"榛子 产地 图像分类","EfficientNetB0 榛子 识别","榛子产地图像分类-4833","10.15832\u002Fankutbd.1946462",{"doi":309,"openalex_id":311,"authors":312,"venue":272,"cited_by_count":87,"oa_url":316,"card":317,"direction":92,"ingested_from":95},"W7221535543",[313],{"name":314,"orcid":315},"Özgür Tomak","https:\u002F\u002Forcid.org\u002F0000-0003-2993-6913","https:\u002F\u002Fdergipark.org.tr\u002Fen\u002Fdownload\u002Farticle-file\u002F5990175",{"tldr":318,"method":319,"finding":320,"direction":92,"opportunity":321},"比较手工特征、深度迁移学习和量子兼容分类器对三个地区榛子图像的分类性能。","HOG+颜色直方图、ResNet50\u002FEfficientNetB0迁移学习、Gr","EfficientNetB0达100%准确率，深度嵌入质量是关键，但结果受图像级划分限制。","可探索对象级划分与跨域泛化，并验证量子分类器在真实农业溯源场景的实用性。","2026-10-13T23:30:38.487402Z",{"id":324,"title":325,"url":326,"summary":327,"summary_zh":328,"content":9,"source_name":329,"source_url":326,"published_at":330,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":274,"score_detail":331,"sources":333,"tags":335,"primary_topic":337,"secondary_topics":338,"classification":339,"search_phrases":350,"slug":353,"view_count":21,"doi":354,"paper":355,"created_at":376},4743,"Image Processing-Based Efficient Hybrid Feature Extraction Approach for Plant Leaf Disease Recognition","https:\u002F\u002Fdoi.org\u002F10.14445\u002F23488549\u002Fijece-v13i9p107","In this paper, a novel hybrid method for plant leaf disease recognition is presented, combining image processing techniques with Deep Learning and handcrafted feature analysis. Leaf images are processed using a Convolutional Neural Network (CNN) to automatically extract hierarchical features, while handcrafted features based on leaf shape, color, and texture provide complementary disease-related information. The combined feature set is fed into a multi-class classifier to accurately identify specific disease types. Evaluation on a diverse dataset of healthy and diseased leaves demonstrates an average accuracy of 98%, outperforming approaches that rely solely on deep learning or handcrafted features. The results highlight the effectiveness of integrating deep and handcrafted features, offering a reliable, scalable solution for precision agriculture and serving as a foundation for future research in automated plant disease detection.","本文提出了一种新颖的植物叶片病害识别混合方法，将图像处理技术与深度学习及手工特征分析相结合。利用卷积神经网络（CNN）对叶片图像进行处理，以自动提取层次化特征，同时基于叶片形状、颜色和纹理的手工特征提供了与病害相关的补充信息。将组合后的特征集输入多类分类器，以准确识别具体的病害类型。在包含健康叶片和患病叶片的多样化数据集上进行评估，结果表明平均准确率达到98%，优于仅依赖深度学习或手工特征的方法。结果凸显了融合深度特征与手工特征的有效性，为精准农业提供了一种可靠且可扩展的解决方案，并为自动化植物病害检测的未来研究奠定了基础。","International Journal of Electronics and Communication Engineering","2026-10-10T00:00:00Z",{"impact":17,"substance":109,"depth":232,"authority":13,"freshness":215,"relevant":21,"comment":332},"提出深度学习与手工特征融合的叶片病害识别方法，准确率达98%，对精准农业有参考价值，但属实验室论文，产业影响有限。",[334],{"name":329,"url":326},[26,27,336,29],"植物病害识别",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":340},{"candidates":341,"agricultural_evidence":46},[342,346],{"code":32,"score":20,"supported":46,"title":343,"abstract":344,"tags":345},[],[135,137],[27,29,336],{"code":55,"score":56,"supported":46,"title":347,"abstract":348,"tags":349},[],[59,60],[26],[351,352],"植物叶片病害识别 图像处理","CNN 特征提取 病害检测","植物叶片病害识别图像处理-4743","10.14445\u002F23488549\u002Fijece-v13i9p107",{"doi":354,"openalex_id":356,"authors":357,"venue":329,"cited_by_count":87,"oa_url":370,"card":371,"direction":264,"ingested_from":95},"W7221322423",[358,360,363,366,368],{"name":359,"orcid":9},"Arundhati Bora",{"name":361,"orcid":362},"Parismita Sarma","https:\u002F\u002Forcid.org\u002F0000-0003-1904-820X",{"name":364,"orcid":365},"Dankan Gowda V","https:\u002F\u002Forcid.org\u002F0000-0003-0724-0333",{"name":367,"orcid":9},"Manash Pratim Bhuyan",{"name":369,"orcid":9},"Jumi Sarmah","https:\u002F\u002Fwww.internationaljournalssrg.org\u002F..\u002FIJECE\u002F2026\u002FVolume13-Issue9\u002FIJECE-V13I9P107.pdf",{"tldr":372,"method":373,"finding":374,"direction":92,"opportunity":375},"提出融合CNN深度特征与手工特征的方法，用于植物叶片病害识别。","CNN自动提取特征，结合叶片形状、颜色、纹理手工特征，多类分类器。","平均准确率达98%，优于仅用深度学习或手工特征的方法。","可探索轻量化混合特征模型在移动端或边缘设备的实时病害识别应用。","2026-10-12T23:30:07.942335Z",{"id":378,"title":379,"url":380,"summary":381,"summary_zh":382,"content":9,"source_name":383,"source_url":380,"published_at":384,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":385,"score_detail":386,"sources":389,"tags":391,"primary_topic":393,"secondary_topics":394,"classification":396,"search_phrases":411,"slug":414,"view_count":21,"doi":415,"paper":416,"created_at":439},4494,"Tomato ripeness classification with weight-based and texture-adaptive pooling in CNNs","https:\u002F\u002Fdoi.org\u002F10.64960\u002Feasr.2026.266223","This research introduces a weight-based mixed pooling and adaptive fuzzy pooling mechanism based on feature map texture analysis for attention mechanism Squeeze-and-Excitation Networks (SE) and Convolutional Neural Networks (CNNs) in tomato ripeness classification. Traditional CNNs and SE networks rely on deterministic pooling strategies that can be suboptimal for capturing diverse visual features such as texture and color gradients in fruit. A novel fuzzy pooling system is proposed that dynamically adjusts the fusion ratio of min and max pooling based on real-time texture analysis of feature maps via a Laplacian filter. This mechanism was integrated into Darknet53, ResNet34, and ResNet101 architectures, both directly and within the SE attention block. The method was evaluated on a five-class tomato ripeness dataset using 10-fold cross-validation against traditional and fixed-weight pooling methods. The fuzzy and mixed pooling approach demonstrated superior performance on certain models which include ESEnet: Darknet53 + SE (Fuzzy pool) achieved an average F1 score across folds of 0.8429, outperforming Darknet53 + baseline SE (Average pool) at 0.8350; Extended Net: ResNet34 + Fuzzy pool achieved an average F1 score across folds of 0.8305, outperforming baseline ResNet34 at 0.8130 and ResNet101 + Fuzzy pool achieved an average F1 score across folds of 0.8284, outperforming baseline ResNet101 at 0.8267. This improvement was realized with minimal increase in computational overhead. Specifically, measurements of model parameters (M) and FLOPs (GMACs) showed only a slight increase across all architectures after integrating SEnet into the system, and zero increase after integrating the Laplacian filter and fuzzy logic. The study concludes that logic-based adaptive pooling is a robust strategy that improves feature representation in CNNs, achieving a balance between classification accuracy and computational cost. These findings offer practical value for developing more reliable automated harvesting systems and provide a generalizable enhancement for a wide range of computer vision classification tasks.","本研究提出了一种基于特征图纹理分析的权重混合池化与自适应模糊池化机制，用于番茄成熟度分类中的注意力机制挤压激励网络（Squeeze-and-Excitation Networks, SE）和卷积神经网络（Convolutional Neural Networks, CNNs）。传统的CNN和SE网络依赖确定性池化策略，在捕捉果实纹理和颜色梯度等多样化视觉特征时可能并非最优。本文提出了一种新颖的模糊池化系统，通过拉普拉斯滤波器对特征图进行实时纹理分析，动态调整最小池化和最大池化的融合比例。该机制被集成到Darknet53、ResNet34和ResNet101架构中，既可直接集成，也可嵌入SE注意力模块内。该方法在五类番茄成熟度数据集上采用10折交叉验证，与传统池化及固定权重池化方法进行了对比评估。模糊池化和混合池化方法在部分模型上表现出优越性能，包括：ESEnet：Darknet53 + SE（模糊池化）在各折上的平均F1得分为0.8429，优于Darknet53 + 基线SE（平均池化）的0.8350；扩展网络：ResNet34 + 模糊池化在各折上的平均F1得分为0.8305，优于基线ResNet34的0.8130；ResNet101 + 模糊池化在各折上的平均F1得分为0.8284，优于基线ResNet101的0.8267。这一改进仅带来了极小的计算开销增加。具体而言，模型参数量（M）和浮点运算次数（GMACs）的测量结果表明，将SEnet集成到系统后所有架构仅有轻微增加，而集成拉普拉斯滤波器和模糊逻辑后则无任何增加。研究结论认为，基于逻辑的自适应池化是一种鲁棒策略，能够改善CNN中的特征表示，在分类精度和计算成本之间实现平衡。这些发现为开发更可靠的自动化收获系统提供了实用价值，并为广泛的计算机视觉分类任务提供了一种可推广的增强方法。","Engineering and Applied Science Research","2026-10-07T00:00:00Z",66,{"impact":215,"substance":387,"depth":19,"authority":17,"freshness":20,"relevant":21,"comment":388},20,"提出基于纹理自适应模糊池化的CNN改进方法，在番茄成熟度分类上取得小幅但稳定的F1提升且算力开销几乎不增，对自动采摘与农业视觉识别有参考价值，但属细分方法类论文，影响面有限。",[390],{"name":383,"url":380},[26,27,158,29,392],"自动采摘",{"code":63,"slug":222,"name":223,"path":224,"parent_code":225},[395],{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},{"status":39,"locked":14,"version":40,"method":41,"evidence":397},{"candidates":398,"agricultural_evidence":46},[399,403,407],{"code":63,"score":232,"supported":46,"title":400,"abstract":401,"tags":402},[159],[158,159],[158],{"code":32,"score":215,"supported":46,"title":404,"abstract":405,"tags":406},[],[136,139],[27,29],{"code":55,"score":21,"supported":14,"title":408,"abstract":409,"tags":410},[],[],[26],[412,413],"番茄 成熟度 分类 卷积神经网络","Darknet53 ResNet 番茄 成熟度","番茄成熟度分类卷积神经网络-4494","10.64960\u002Feasr.2026.266223",{"doi":415,"openalex_id":417,"authors":418,"venue":383,"cited_by_count":87,"oa_url":433,"card":434,"direction":264,"ingested_from":95},"W7220840158",[419,421,423,425,428,430],{"name":420,"orcid":9},"Rut Yatigul",{"name":422,"orcid":9},"Wisanu Jitviriya",{"name":424,"orcid":9},"Eiji Hayashi",{"name":426,"orcid":427},"Aran Blattler","https:\u002F\u002Forcid.org\u002F0000-0002-8684-6239",{"name":429,"orcid":9},"Watcharin Tangsuksant",{"name":431,"orcid":432},"Teppakorn Sittiwanchai","https:\u002F\u002Forcid.org\u002F0000-0001-9653-8750","https:\u002F\u002Fph01.tci-thaijo.org\u002Findex.php\u002Feasr\u002Farticle\u002Fdownload\u002F266223\u002F176569",{"tldr":435,"method":436,"finding":437,"direction":92,"opportunity":438},"提出基于纹理自适应模糊池化机制，提升CNN番茄成熟度分类性能。","用Laplacian滤波分析特征图纹理，动态调整最大最小池化融合比，集成到Dar","模糊池化在多个模型上F1提升，且计算开销几乎不增加。","可探索自适应池化在其它果蔬成熟度或田间复杂光照下的泛化能力。","2026-10-09T23:30:09.993787Z",{"id":441,"title":442,"url":443,"summary":444,"summary_zh":445,"content":9,"source_name":446,"source_url":443,"published_at":447,"category":12,"cover_url":9,"hotness":13,"is_selected":14,"score":448,"score_detail":449,"sources":452,"tags":454,"primary_topic":457,"secondary_topics":458,"classification":459,"search_phrases":475,"slug":478,"view_count":74,"doi":479,"paper":480,"created_at":502},3067,"Automated detection of visible venation patterns in cowpea leaves under agricultural engineering","https:\u002F\u002Fdoi.org\u002F10.1016\u002Fj.sasc.2026.200636","Accurate assessment of crop leaf characteristics is important for automated crop phenotyping and precision agriculture. However, manual assessment of leaf characteristics is time-consuming and may be affected by subjective interpretation. This study proposes an RGB-based convolutional neural network (CNN) framework for automated classification of surface-visible venation patterns in cowpea (Vigna unguiculata) leaves. A dataset of 2500 RGB cowpea leaf images, comprising Healthy and Structurally Stressed samples, was used to develop and evaluate the proposed framework. The images were preprocessed using standard image-processing operations and subsequently analyzed using a CNN for binary classification of the two visible-venation categories. Canny edge detection was additionally employed as a supplementary visualization technique to highlight surface-visible venation patterns and support qualitative interpretation; it was not used as the primary classification mechanism. The proposed CNN achieved 95.4% accuracy, 94.8% precision, 95.1% recall, and 95.0% F1-score on the evaluated test dataset. Comparative experiments with conventional feature-based approaches, including GLCM, SIFT, and HOG, showed lower observed classification performance. The results demonstrate the potential of RGB-based deep learning for automated analysis of surface-visible cowpea leaf venation patterns and provide a non-destructive image-based approach for crop phenotyping. The proposed framework can support scalable agricultural image analysis while avoiding claims of direct Surface-visible vein pattern anatomical characterization. The dataset is available at this link: “ https:\u002F\u002Fgithub.com\u002Fvijayachoudharyresearcher\u002FComputer-Vision–Cowpea-Leaves-Internal-Structure “.","准确评估作物叶片特征对于自动化作物表型分析和精准农业具有重要意义。然而，人工评估叶片特征耗时较长，且可能受到主观解释的影响。本研究提出了一种基于RGB的卷积神经网络（CNN）框架，用于对豇豆（Vigna unguiculata）叶片表面可见脉纹模式进行自动分类。研究使用包含健康和结构胁迫样本的2500张RGB豇豆叶片图像数据集来开发和评估所提出的框架。图像经标准图像处理操作预处理后，利用CNN对两个可见脉纹类别进行二分类分析。此外，研究还采用Canny边缘检测作为补充可视化技术，以突出表面可见脉纹模式并支持定性解释；其并未被用作主要分类机制。所提出的CNN在评估测试数据集上取得了95.4%的准确率、94.8%的精确率、95.1%的召回率和95.0%的F1分数。与传统基于特征的方法（包括GLCM、SIFT和HOG）的比较实验显示，其分类性能较低。结果表明，基于RGB的深度学习在自动化分析豇豆叶片表面可见脉纹模式方面具有潜力，并为作物表型分析提供了一种基于图像的非破坏性方法。所提出的框架可支持可扩展的农业图像分析，同时避免声称对表面可见脉纹模式进行直接的解剖学表征。数据集可通过以下链接获取：“https:\u002F\u002Fgithub.com\u002Fvijayachoudharyresearcher\u002FComputer-Vision–Cowpea-Leaves-Internal-Structure”。","Systems and Soft Computing","2026-09-18T00:00:00Z",69,{"impact":17,"substance":387,"depth":110,"authority":450,"freshness":215,"relevant":21,"comment":451},13,"基于RGB与CNN的豇豆叶脉自动识别研究，方法清晰、数据规模可观，对作物表型与精准农业有参考价值，但属细分领域技术进展，影响范围有限。",[453],{"name":446,"url":443},[26,27,455,29,456],"作物表型","豇豆",{"code":32,"slug":33,"name":34,"path":35,"parent_code":36},[],{"status":39,"locked":14,"version":40,"method":41,"evidence":460},{"candidates":461,"agricultural_evidence":46},[462,466,470],{"code":32,"score":215,"supported":46,"title":463,"abstract":464,"tags":465},[],[135,137],[27,29],{"code":55,"score":56,"supported":46,"title":467,"abstract":468,"tags":469},[],[59,60],[26],{"code":118,"score":56,"supported":46,"title":471,"abstract":472,"tags":474},[],[455,473],"crop phenotyping",[455],[476,477],"豇豆叶片 叶脉 识别","RGB CNN 作物表型","豇豆叶片叶脉识别-3067","10.1016\u002Fj.sasc.2026.200636",{"doi":479,"openalex_id":481,"authors":482,"venue":446,"cited_by_count":87,"oa_url":443,"card":496,"direction":500,"ingested_from":95},"W7213555214",[483,486,489,491,493],{"name":484,"orcid":485},"Vijaya Choudhary","https:\u002F\u002Forcid.org\u002F0000-0003-3417-1943",{"name":487,"orcid":488},"Paramita Guha","https:\u002F\u002Forcid.org\u002F0000-0001-6696-4812",{"name":490,"orcid":9},"Jai Prakash Mishra",{"name":492,"orcid":9},"Himanshu Sharma",{"name":494,"orcid":495},"Munish Sabharwal","https:\u002F\u002Forcid.org\u002F0000-0002-7338-6982",{"tldr":497,"method":498,"finding":499,"direction":500,"opportunity":501},"用RGB卷积神经网络自动分类豇豆叶片可见脉纹，区分健康与结构胁迫样本。","2500张RGB豇豆叶图像，CNN二分类，Canny边缘检测辅助可视化，对比GL","CNN达95.4%准确率，优于传统特征方法，可实现无损作物表型分析。","农业遥感与作物表型","可扩展至多作物、多胁迫类型及田间实时检测，并结合多光谱提升脉纹表型解析。","2026-09-21T23:30:18.575016Z"]