Automatic defect identification and grading system for ‘Jonagold’ apples

2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) Pub Date : 2016-08-01 DOI:10.1109/CCIP.2016.7802851

Shyla Raj, Vinod DS

引用次数: 4

Abstract

A method to grade `Jonagold' apples based on features extracted from defects is described. Database consisting of multi-spectral images of Jonagold apples is used for the work. Fuzzy C-Means (FCM) clustering method is used for defect segmentation, features from defect part is extracted using Histogram of Oriented Gradients (HOG) method and Apple classification is performed by using Multi-Class Support Vector Machine (MSVM) with accuracy of 97.5% for two category grading (healthy and defected) and 94.66% for multi-category grading (healthy apples, slightly defected apples and seriously defected apples).

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乔纳金 "苹果缺陷自动识别和分级系统

本文介绍了一种根据从缺陷中提取的特征对 "Jonagold "苹果进行分级的方法。这项工作使用的数据库由乔纳金苹果的多光谱图像组成。使用模糊 C-Means (FCM) 聚类方法进行缺陷分割，使用定向梯度直方图 (HOG) 方法提取缺陷部分的特征，使用多类支持向量机 (MSVM) 进行苹果分类，两类分级（健康和缺陷）的准确率为 97.5%，多类分级（健康苹果、轻微缺陷苹果和严重缺陷苹果）的准确率为 94.66%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)

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