Journal of Food, Agriculture and Environment

Analysis of acerola leaves using RGB image processing and clustering


Pedro Bastos Costa 1*, Felipe de Oliveira Baldner 2, Juliana Freitas Santos Gomes 3, Fabiana Rodrigues Leta 2

Recieved Date: 2019-01-18, Accepted Date: 2019-03-23


The modernization of agriculture allows for new techniques to be employed to achieve better results in the quality of products. With this in mind, computer vision algorithms have been widely used for measuring fruit to determining quality of crops. To determine the quality of  fruits, the acerola (Malpighia emarginata) leaves can be inspected to control whether they are healthy or not. This analysis is based on the color intensities present in the leaf. The purpose of this paper was to propose a computer vision system to analyze the health of the leaves using color measurements and the k-means algorithm to separate the extracted data into clusters. This is a first step of a metrological study show the importance of a standardization in using color for food and agriculture quality control.


Computer vision, color, leaves, quality control, k-means, metrology

Journal: Journal of Food, Agriculture and Environment
Year: 2019
Volume: 17
Issue: 2
Category: Agriculture
Pages: 31-34

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