Chokanan Mango Fruit Maturity Detection using K-Nearest Neighbor

Abstract

This research introduces a technique utilizing machine learning, specifically the K-Nearest Neighbors (KNN) algorithm in Python, to determine the maturity of mango fruit. The main goal is to create a system capable of precisely evaluating mango fruit maturity, which is essential for improving post-harvest processes and ensuring top-notch produce for consumers. The proposed method involves extracting pertinent features from mango fruit images and training the KNN classifier with labeled data. Subsequently, the trained model is deployed to categorize unseen mango samples based on their maturity stages. Experimental findings validate the efficacy of the developed system in accurately assessing mango fruit maturity, achieving high classification accuracy. This study significantly contributes to fruit quality assessment methodologies, offering a practical solution for the fruit industry to enhance operational efficiency and meet consumer expectations.