K-Nearest Neighbor-Based Detection of Chokanan Mango Maturity for Agricultural Technology Applications

Abstract

This study presents a novel application of machine learning, specifically the K-Nearest Neighbors (KNN) algorithm implemented in Python, to detect the maturity of Chokanan mangoes. The research addressed a critical need in agriculture and food technology by providing an automated system for evaluating fruit maturity, thereby enhancing post-harvest processes and ensuring high-quality production for consumers. The proposed method involved the extraction of key features from mango images and training a KNN classifier with labeled data. The trained model was then utilized to classify mango samples into distinct maturity stages. The experimental results demonstrated the system's high accuracy in classifying mango maturity, showcasing its potential as a practical solution for fruit quality assessment. By integrating this approach, the fruit industry can improve their operational efficiency, reduce waste, and better meet consumer expectation for premium-quality fruits.