Detection of early bruises on apples using hyperspectral imaging combining with YOLOv3 deep learning algorithm

The bruise on apples is a common damage mainly caused during the picking and transportation stages, but its detection robustness has not been satisfactory using the traditional machine learning method, and the precision of bruise detection with short formation time and light degree of bruise is low, and it is easily affected by the variable and complex environment, especially the bright spots. In this paper, a long-wave near infrared (LW-NIR 930-2,548 nm) hyperspectral imaging system for acquiring reflectance images of bruised apples were developed in this study. Then the regions of interest (ROIs) of sound and bruised tissues were analyzed and three specific spectral regions were determined. The segmented principal component analysis was performed to select characteristic wavelengths from the three regions. In order to verify the superiority over original hyperspectral data onto deep learning algorithms, the gray-scale images of characteristic wavelengths (dataset I) and their PC images (dataset II) were input into YOLOv3 network to develop a bruise detection model, respectively. The results showed that all 110 bruise spots of testing set were identified correctly with the YOLOv3 model of dataset I and achieved a F1-sore of 100% and FPS of 68, while the YOLOv3 model of dataset II and traditional detection method could not eliminate the interference of bright spots and misidentified them as bruises. In short, the YOLOv3 model based on the selected characteristic wavelengths has an excellent potential for on-line detection of apple bruises. Practical Applications Detection of apple bruises is essential to ensure the fruit quality of the same batch and the income of the farmers. In the past, most bruises were detected using traditional machine learning algorithms combined with hyperspectral imaging technology. This requires a series of cumbersome preprocessing and is susceptible to uneven brightness distribution of reflected image. Compared with the traditional method, our proposed detection model using hyperspectral imaging combining with YOLOv3 deep learning algorithm could obtain better detection accuracy and robustness, and avoid the problem of false detection caused by the interference of bright spots. Simultaneously, the method based on neural network provides a new idea for future multispectral online detection technology.