Universal Journal of Agricultural Research Vol. 10(4), pp. 397 - 404
DOI: 10.13189/ujar.2022.100409
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Characterization and Classification of Citrus reticulata var. Keprok Batu 55 Using Image Processing and Artificial Intelligence

Aimmatuz Zakiyyah ¹, Zainuri Hanif ², Dina Wahyu Indriani ^1,3, Zaqlul Iqbal ³, Retno Damayanti ³, Dimas Firmanda Al Riza ^1,3,*
1 Bioprocess Engineering Study Program, Faculty of Agricultural Technology, Universitas Brawijaya, Jl. Veteran, Malang, Indonesia
² Research Center for Behavioral and Circular Economics, National Research and Innovation Agency, Jalan Jend. Gatot Subroto, No.10 South Jakarta 12710, Indonesia
³ Agricultural Engineering Department, Faculty of Agricultural Technology, Universitas Brawijaya, Jl. Veteran, Malang, Indonesia

ABSTRACT

Citrus reticulata var. Keprok Batu 55 is one of the superior varieties of citrus originating from Batu City, East Java, which has a slightly sour-sweet taste with a sweetness level of 10-12^obrix. Prediction of citrus maturity as a monitoring activity for pre- and post-harvest quality management is still done manually, whereas human judgment of the maturity level is subjective. One alternative to increase the monitoring productivity is the development of a portable system with image processing and destructive measurements of physico-chemical properties such as hardness, brix, and pH. This study aims to develop an image-based classification model and characterize the quality parameters of citrus. Measurement of maturity on Citrus reticulata var. Keprok Batu 55 has been carried out for image analysis with color index (RGB, L*a*b. and HSV). The image of citrus will be taken with the camera, which will later be taken partially (cropping) on the skin, which will then extract the color characteristics and calculate the level of color content from RGB and then converted it to HSV. A sufficient number of images with various conditions are needed to train the artificial intelligent model so that it can perform segmentation, calculation, and grade classification. A prediction model then was developed using color features and several machine learning modeling approaches.

KEYWORDS
Brix, Classification, Hardness, Machine Learning, Maturity, pH

Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] Aimmatuz Zakiyyah , Zainuri Hanif , Dina Wahyu Indriani , Zaqlul Iqbal , Retno Damayanti , Dimas Firmanda Al Riza , "Characterization and Classification of Citrus reticulata var. Keprok Batu 55 Using Image Processing and Artificial Intelligence," Universal Journal of Agricultural Research, Vol. 10, No. 4, pp. 397 - 404, 2022. DOI: 10.13189/ujar.2022.100409.

(b). APA Format:
Aimmatuz Zakiyyah , Zainuri Hanif , Dina Wahyu Indriani , Zaqlul Iqbal , Retno Damayanti , Dimas Firmanda Al Riza (2022). Characterization and Classification of Citrus reticulata var. Keprok Batu 55 Using Image Processing and Artificial Intelligence. Universal Journal of Agricultural Research, 10(4), 397 - 404. DOI: 10.13189/ujar.2022.100409.