Classifying Healthy and Defective Fruits with a Multi-Input Architecture and CNN Models

TL;DR

This paper explores a Multi-Input CNN architecture combining RGB and silhouette images to improve fruit classification accuracy, achieving perfect results with MobileNetV2 for identifying healthy and defective fruits.

Contribution

It introduces a novel Multi-Input CNN approach using both RGB and silhouette images, demonstrating significant accuracy improvements over traditional methods.

Findings

MobileNetV2 achieved 100% accuracy in fruit classification.

Combining silhouette with RGB images enhances model performance.

Multi-Input architecture outperforms single-input models.

Abstract

This study presents an investigation into the utilization of a Multi-Input architecture for the classification of fruits (apples and mangoes) into healthy and defective states, employing both RGB and silhouette images. The primary aim is to enhance the accuracy of CNN models. The methodology encompasses image acquisition, preprocessing of datasets, training, and evaluation of two CNN models: MobileNetV2 and VGG16. Results reveal that the inclusion of silhouette images alongside the Multi-Input architecture yields models with superior performance compared to using only RGB images for fruit classification, whether healthy or defective. Specifically, optimal results were achieved using the MobileNetV2 model, achieving 100\% accuracy. This finding suggests the efficacy of this combined methodology in improving the precise classification of healthy or defective fruits, which could have significant implications for applications related to external quality inspection of fruits.