Open AccessDetermination of RGB and Grayscale Value On Palm Oil (Elaeis guineensis Jacq.) Fresh Fruit Bunch (FFB) Images Using MATLAB
Author(s) -
Muhammad Ansori Nasution,
Haikal Nando Winata,
Fadlin Qisthi Nasution,
Henny Lydiasari,
Rivaldy Yustianto Pasaribu,
Arjanggi Nasution,
Ayu Wulandari
Publication year - 2022
Publication title -
jurnal penelitian kelapa sawit
Language(s) - English
Resource type - Journals
eISSN - 2614-8889
pISSN - 0853-196X
DOI - 10.22302/iopri.jur.jpks.v30i1.159
Subject(s) - rgb color model , elaeis guineensis , grayscale , palm oil , mathematics , matlab , computer science , hue , artificial intelligence , computer vision , agricultural engineering , environmental science , engineering , pixel , agroforestry , operating system
The Color and appearance are the most important indicators for a farmer to determine the condition of oil palm fresh fruit bunches (FFB) in the harvesting process. As the main attributes, the color and appearance become the guidelines for the initial assessment of fresh fruit bunches (FFB) of oil palm condition that is suitable or not for harvesting. In Facts, the FFB assessment activities are still carried out manually by utilizing the farmers visual, which is it will be prone to errors during the assessment. Due to this problem, it is important to automate the assessment of the color characteristics of oil palm FFB in order to minimize errors by farmers. The purpose of this study was to determine the efficiency of RGB color imaging techniques on oil palm FFB. The method used is processing and color analysis on 30 FFB images based on smartphone cameras to find the correlations for each color. Each color channel R, G and B in the FFB images was extracted and converted into grayscale using MATLAB R2021 software. The results show that the correlation value of R channel and grayscale has the highest value with R2 = 0.9569. This correlation is expected to be an initial study and suitable alternative for automating the assessment of the condition of oil palm FFB.