Early spoilage detection of apple based on generative adversarial network and Mask R-CNN

Abstract

[Objective] To improve the detection accuracy of early apple spoilage zone. [Methods] An apple spoilage detection method was proposed based on generative adversarial network and convolutional neural network. The Pix2PixHD model was used to generate near-infrared imaging data of stored apples in the early postharvest metamorphic area. The Mask R-CNN model was used to segment the generated near Infrared image to detect the deterioration zone in the apple. Based on generative adversarial network and convolutional neural network technology, the early deterioration region segmentation and prediction of postharvest apples were implemented by using the generated near-infrared imaging data on a low-cost embedded system with artificial intelligence function. [Results] The average accuracy of this method was 1.825%～10.435% higher than that of the other nine methods. The Pix2PixHD generated a visible NIR image from an RGB image at 17 frames per second, and the Mask R-CNN was able to segment spoilage areas in an apple image at 4.2 frames per second. [Conclusion] The proposed method is expected to facilitate the development of low-cost food quality controllers.

Keywords

• apple
• early spoilage detection
• generative adversarial network
• convolutional neural network
• image conversion