Ripeness Identification of Pisang Raja based on Shape and Texture Extraction using K-Means Clustering

Abstract

This research introduces a Pisang Raja (King Banana) ripeness identification method using K-Means Clustering based on shape and texture extraction. Pisang Raja undergoes visual changes as it ripens. The method involves image capture, preprocessing, shape and texture feature extraction, and K-Means Clustering for classification. Shape attributes (perimeter, area) and texture features (GLCM, LBP) are extracted and used for clustering Pisang Raja samples into ripeness categories. A diverse dataset is employed for training and evaluation, showing the efficacy of the approach in ripeness identification. The study contributes an automated technique for Pisang Raja ripeness assessment, with potential in the agricultural and food industries