COLOR FEATURE EXTRACTION AND EUCLIDEAN DISTANCE FOR CLASSIFICATION OF ORYZA SATIVA NITROGEN ADEQUACY BASED ON LEAF COLOR CHART (LCC)
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Date
2020
Journal Title
Journal ISSN
Volume Title
Publisher
International Journal of Psychosocial Rehabilitation, Vol. 24, Issue 1
Abstract
Oryza sativa is a rice-producing plant which is one of the main commodities in various countries
including Indonesia. In the process of maintaining the quality of rice plants in order to have good growth and high
yields, an adequate supply of nitrogen (N) is needed. The most obvious and commonly seen symptom of N deficiency
is a reduction in the green color of the leaves (chlorosis) [3]. Leaf color is an indocator that is useful for indicating
the N fertilizer requirement of rice plants [3]. Currently a simple tool that can be used to measure the color of the
leaves of rice plants as a determinant of the amount of N fertilizer is the Leaf Color Chart (LCC). However, the
problem in this LCC is that the tool is still manual and the assessment / classification process is carried out using
color estimates based on eye sight. This creates uncertainty because everyone has different estimates. Based on these
problems, it is necessary to have an automatic classification system of rice leaf color that can help farmers in
determining the category of rice plants based on LCC. In this study, the color classification system of rice leaf
images was carried out by extracting RGB (Red, Green, Blue) image features of rice leaf images. While the
classification process is done by finding the color similarity between the image of rice leaves with the LCC scale
using the Euclidean Distance method. The results obtained from the color classification system of rice leaves in this
study have an accuracy rate of 75%.
Description
Keywords
Oryza Sativa, Feature Extranction, Euclidean Distance.