[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"item-2650":3},{"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":24,"tags":26,"view_count":33,"doi":34,"paper":35,"created_at":49},2650,"Automated Cocoa Bean Classification and Defect Detection Based on ASEAN Standard Using Image Processing and Feature-Based Analysis","https:\u002F\u002Fdoi.org\u002F10.64823\u002Fijeee.2601005","This study presents an automated cocoa bean classification and defect detection system developed in accordance with the ASEAN Standard for Cocoa Bean (ASEAN Stan 34:2014). Implemented in MATLAB, the system utilizes digital image processing and computer vision techniques to segment individual cocoa beans, extract geometric, color, and texture features, and classify them into quality categories: Extra Class, Class I, Class II, and Non-Compliant. The image processing workflow integrates color space transformations (RGB to HSV), adaptive thresholding, and morphological filtering to ensure accurate segmentation. Feature extraction targets parameters such as area, length, aspect ratio, texture entropy, and color uniformity to identify specific defects including moldy, slaty, insect-damaged, and germinated beans. Validation against independent expert grading achieved an overall reliability rate of 96.2%, with high precision (0.93) and recall (0.91), and an average processing speed of 1.3 seconds per image. The developed analyzer provides an objective, rapid, and repeatable tool to support standardization and postharvest cocoa quality control across the ASEAN region. Keywords: Defect Detection; MATLAB; cocoa bean grading; ASEAN Standard; digital image processing; computer vision","本研究提出了一套依据《东盟可可豆标准》（ASEAN Stan 34:2014）开发的自动化可可豆分类与缺陷检测系统。该系统在MATLAB中实现，利用数字图像处理与计算机视觉技术对单颗可可豆进行分割，提取几何、颜色和纹理特征，并将其分为优质级（Extra Class）、一级（Class I）、二级（Class II）和不合格（Non-Compliant）四个质量类别。图像处理流程整合了颜色空间变换（RGB转HSV）、自适应阈值分割和形态学滤波，以确保分割的准确性。特征提取针对面积、长度、长宽比、纹理熵和颜色均匀性等参数，以识别霉变、僵化、虫害和发芽等特定缺陷。经与独立专家评级进行验证，系统总体可靠率达到96.2%，精确率为0.93，召回率为0.91，平均处理速度为每张图像1.3秒。所开发的分析仪为支持东盟地区可可采后质量控制的标准化提供了一种客观、快速且可重复的工具。关键词：缺陷检测；MATLAB；可可豆分级；东盟标准；数字图像处理；计算机视觉",null,"International Journal of Electrical and Electronics Engineering","2026-09-14T00:00:00Z","论文",10,false,77,{"impact":17,"substance":18,"depth":19,"authority":20,"freshness":21,"relevant":22,"comment":23},18,21,17,13,8,1,"基于东盟标准的可可豆自动分级与缺陷检测研究，方法成熟、验证可靠（96.2%），对热带经济作物产后质控有实用价值，但属细分领域技术进展，影响力有限。",[25],{"name":10,"url":6},[27,28,29,30,31,32],"智慧农业","农业人工智能","计算机视觉","行业标准","农产品分级","可可产业",0,"10.64823\u002Fijeee.2601005",{"doi":34,"openalex_id":36,"authors":37,"venue":10,"cited_by_count":33,"oa_url":40,"card":41,"direction":47,"ingested_from":48},"W7212670192",[38],{"name":39,"orcid":9},"Jennifer Natnat","https:\u002F\u002Fioro.org\u002Farticle\u002F529160740839\u002Fpdf",{"tldr":42,"method":43,"finding":44,"direction":45,"opportunity":46},"基于图像处理与特征分析，按东盟标准自动分类可可豆并检测缺陷。","MATLAB图像处理，RGB转HSV、自适应阈值、形态学滤波，提取几何颜色纹理特","系统分类准确率96.2%，精度0.93，召回0.91，单图处理1.3秒。","农业人工智能与决策模型","可扩展至多作物、多标准实时分级，结合深度学习提升复杂缺陷识别鲁棒性。","智慧农业 \u002F 农业物联网","openalex","2026-09-16T23:30:13.899726Z"]