FRUIT DETECTION AND ITS THREE-STAGE MATURITY GRADING

Abstract: Fruit maturity grading plays a crucial role in agricultural quality control, supply chain management, and food processing industries. Traditional manual grading methods are subjective, time-consuming, and prone to human error due to variations in lighting conditions, fatigue, and individual perception. This paper presents an automated fruit detection and three-stage maturity grading system using deep learning and image processing techniques. The proposed system classifies fruits into three maturity stages—unripe, ripe, and overripe -by analyzing visual features such as color, texture, and surface patterns. A Convolutional Neural Network (CNN) model is trained using the Fruits-360 dataset, enhanced with additional maturity-stage images. Image preprocessing techniques including resizing, normalization, background removal, and noise reduction are applied to improve classification accuracy. Experimental results demonstrate that the proposed system achieves high accuracy and consistency, significantly reducing dependence on manual inspection. The system provides a scalable and efficient solution for intelligent agricultural applications

Keywords: Fruit Detection, Maturity Grading, Convolutional Neural Networks, Image Processing, Deep Learning, Smart Agriculture.