Apple Defect Detection Based on Deep Convolutional Neural Network

Abstract

Fruit detection and classification are a challenging task in image processing. This paper presents a novel approach to fruit detection using deep convolutional neural networks. The aim is to build an accurate, fast, and reliable fruit detection system, which is a vital element of an autonomous agricultural artificial intelligence platform; it is a key element for fruits yield detection and automated fruits processing industry. Recent work in deep neural network has led to the development of a state-of-the-art object detection and classification. This paper demonstrates the design and implementation of deep learning-based automated categorization of the apple images captured from fruits processing industry. The system is based on a convolutional neural network (CNN) followed by the selection of proposed regions. The training of the classifier is performed, with a dataset derived from the set of images taken from the agricultural-based industrial fruit sorting process. In the preparation of the CNN architecture model, initializing the parameter configuration accelerates the network training process. The results of the experiments using CNN algorithm showed the performance of defect detection on the apple fruit of 96%.