FRUIT RIPENESS & QUALITY GRADING USING THE CNN MODEL

Abstract

Fruit ripeness and quality assessment play a crucial role in agriculture, food
processing, and supply chain management. Traditional methods of grading fruits rely
heavily on manual inspection, which is often time-consuming, inconsistent, and prone
to human error due to factors such as subjective judgment, varying lighting conditions,
and operator fatigue. To address these limitations, there is a growing need for an
automated and reliable system capable of accurately evaluating fruit ripeness and
quality.
This project presents a deep learning-based approach for fruit ripeness detection and
quality grading using Convolutional Neural Networks (CNNs). The system is
designed to classify fruit images into categories such as ripe, unripe, and rotten. A
dataset consisting of approximately 7,000+ fruit images is utilized, which is divided
into training and testing sets to develop and validate the model. Data preprocessing
and augmentation techniques, including rotation, flipping, zooming, and brightness
adjustments, are applied to improve model generalization and performance.