Detection of apple defect using laser-induced light backscattering imaging and convolutional neural network

Abstract The similarity of gray characteristics and shapes of the defects, stems, and calyxes in apple images is a difficult challenge for automatic identification and detection of apple defects in the machine vision field. This paper attempts towards finding an automatic and efficient way to identify apple defects. An automatic detection method is proposed for apple defects based on laser-induced light backscattering imaging and convolutional neural network (CNN) algorithm. Laser backscattering spectroscopic images of apples are obtained using semiconductor laser. We take preprocessing steps to get the finest image dataset for CNN. An AlexNet model with an 11-layer structure is established and trained to identify apple defects. We analyze how well the model does with the recognition effects of apple defects. The proposed CNN model for the detection of apple defects achieves a higher recognition rate of 92.5%, and the accuracy is better than conventional machine learning algorithms. The method based on laser backscattering imaging analysis and CNN theory provides an idea and theoretical basis for efficient, non-destructive, and online detection of fruits quality.