Open AccessFeasibility study of sugarcane stalks separation from trash using PCA based on color space of digital photos
Author(s) -
Lalita Panduangnate,
Khwantri Saengprachathanarug,
Jetsada Posom,
Arthit Phuphaphud,
Chanreaksa Chea,
Eizo Taira
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/301/1/012050
Subject(s) - rgb color model , hsl and hsv , color space , cane , mathematics , thresholding , artificial intelligence , stalk , principal component analysis , sugar , computer vision , computer science , horticulture , food science , biology , image (mathematics) , virus , virology
Percentage of trash in the harvested sugarcane is one of the key quality indicators and needed to be examined since it affects sugar production efficiency. Currently, in Thailand, measurements of trash are done by randomly sampling by labors, which leads to delay and high labor cost this article aims to study the possibility of using the Principle Component Analysis (PCA) on the digital photos to detect harvested cane out of the trash. The component values of 5 color spaces (RGB, HSV, LAB, XYZ, and YIQ) of sugarcane stalks and trash images were obtained and then analyzed using PCA. The thresholding equation of each space was determined and applied for detecting sugarcane stalk pixel. The results revealed that the quality percentage (QP) of detection of all 5 color spaces was in a range from 82.27% to 96.19%, while those of LAB and RGB were the best at 96.06% and 94.90% respectively.