Color Feature Extraction for Grape Variety Identification: Naïve Bayes Approach

Abstract

The problem addressed in this research is the lack of an efficient and accurate method for automatically identifying grape varieties. Accurate identification is crucial for quality control in the agricultural and food industries, impacting product labeling, pricing, and consumer trust. The aim of this research is to develop an automated system to classify green, black, and red grapes using digital image processing technology. This research method employs Naïve Bayes classification combined with color feature extraction. Testing was conducted under two scenarios: a database scenario with predefined grape image datasets and an out-of-database scenario with images resembling grape colors. Image processing includes resizing images to 200x200 pixels, Gamma Correction, Gaussian filtering, conversion to Lab* color space, K-Means Clustering for segmentation, followed by feature extraction and Naïve Bayes classification. The results of this research are that in the database scenario, the system achieved accuracies of 98.33% with an 80:20 data split and 98.89% with a 70:30 split. In the out-of-database scenario, accuracies were 96.67% with an 80:20 split and 97.78% with a 70:30 split. The conclusion of this research is the proposed method provides a reliable and efficient solution for automatic grape variety identification, benefiting quality control in agriculture and food industries.