iPepper: Intelligent pepper grading and quality assurance system

D. A. Awang Iskandar, R. Baini, A. Y. Wee, Shapiee Abdul Rahman, A. H. Fauzi
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引用次数: 3

Abstract

Pepper is a key export of the state of Sarawak (Malaysian Borneo); it produces 98% of Malaysia's pepper. At present, processed pepper berries are graded manually. This process is time consuming and error prone as it is very much dependent on the experience of the pepper grader. To overcome these weaknesses, we propose a Pepper Grading System which employs image processing and machine learning approaches using image features and moisture content data of the pepper berries. For instance, from initial tests, a high correlation between the grade of pepper berries to the colour features has been detected. Using existing machine learning algorithms in WEKA, we have obtained a 100% accuracy in categorising the pepper berries into the correct grades. In addition, moisture content and colourometer readings provide another 2 other parameters which may complement the image features in accurately classifying the berries into the right grades.
iPepper:智能辣椒分级和质量保证系统
胡椒是沙捞越州(马来西亚婆罗洲)的主要出口产品;马来西亚98%的辣椒都产自这里。目前,加工过的胡椒浆果是手工分级的。这个过程既耗时又容易出错,因为它很大程度上取决于胡椒分级机的经验。为了克服这些缺点,我们提出了一种辣椒分级系统,该系统采用图像处理和机器学习方法,利用辣椒浆果的图像特征和水分含量数据。例如,从最初的测试中,发现了胡椒浆果的等级与颜色特征之间的高度相关性。使用WEKA中现有的机器学习算法,我们在将胡椒浆果分类为正确等级方面获得了100%的准确性。此外,水分含量和色度计读数提供了另外两个参数,可以补充图像特征,准确地将浆果分类为正确的等级。
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