Recognition system for fruit classification based on 8-layer convolutional neural network

Abstract

INTRODUCTION: Automatic fruit classification is a challenging task. The types, shapes, and colors of fruits are all essential factors affecting classification. OBJECTIVES: This paper aimed to use deep learning methods to improve the overall accuracy of fruit classification, thereby improving the sorting efficiency of the fruit factory. METHODS: In this study, our recognition system is based on an 8-layer convolutional neural network(CNN) combined with the RMSProp optimization algorithm to classify fruits. It is verified through 10 times 10-fold crossover validation. CONCLUSION: Our method achieves an accuracy of 91.63%, which is superior to the other four state-of-the-art methods.

Comparative Analysis of Classical Machine Learning and Deep Learning Methods for Fruit Image Recognition and Classification

“…In response to these identified gaps, the primary objective of this work is to have picture features extracted and preprocessed from the comprehensive Fruit-360 dataset, utilizing Principal Component Analysis (PCA), color, and texture features. Subsequently, a combination of classical machine learning methods (SVM, KNN, and DT) and the deep learning classification network AlexNet is employed to classify diverse fruit varieties [9,10]. The analysis of classification results aims to identify the most effective ML and DL models for the Fruit-360 dataset, thereby addressing the pressing challenges in fruit recognition and classification.…”