Deep Learning-Based Apple Defect Detection with Residual SqueezeNet

Abstract

Apple defect detection using hyperspectral image has become an interesting research focus since the last decade. It is important for wide range of applications, such as agricultural sector, food processing, and automatic fruits grading system. However, this task is a challenging one due to calyx and stem, their different types and orientation, and similar in their visual appearance. The proposed method based on a deep learning approach using SqueezeNet architecture. However, the apple images are extracted and fed into a deep network for training and testing. The proposed SqueezeNet architecture utilizes convolution neural network to regress a bypass connection between the fire modules across the images. It has been evaluated our own created dataset. The excremental result shows that our proposed methods are efficient and effective. The general detection rate was 92.23%. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.