Implementation of Fruits Recognition Classifier using Convolutional Neural Network Algorithm for Observation of Accuracies for Various Hidden Layers

TL;DR

This paper explores the impact of various hidden layer configurations in a CNN for fruit recognition, achieving up to 100% accuracy on a large dataset, demonstrating the effectiveness of deep learning in complex image classification tasks.

Contribution

It systematically evaluates different CNN architectures with various hidden layers and epochs for fruit recognition, providing insights into optimal configurations for high accuracy.

Findings

Achieved 100% test accuracy on Fruits-360 dataset.

Identified optimal hidden layer and epoch combinations for best performance.

Demonstrated CNN effectiveness in recognizing similar fruit images.

Abstract

Fruit recognition using Deep Convolutional Neural Network (CNN) is one of the most promising applications in computer vision. In recent times, deep learning based classifications are making it possible to recognize fruits from images. However, fruit recognition is still a problem for the stacked fruits on weighing scale because of the complexity and similarity. In this paper, a fruit recognition system using CNN is proposed. The proposed method uses deep learning techniques for the classification. We have used Fruits-360 dataset for the evaluation purpose. From the dataset, we have established a dataset which contains 17,823 images from 25 different categories. The images are divided into training and test dataset. Moreover, for the classification accuracies, we have used various combinations of hidden layer and epochs for different cases and made a comparison between them. The overall performance losses of the network for different cases also observed. Finally, we have achieved the best test accuracy of 100% and a training accuracy of 99.79%.