Development of crop data interpolation technique using Cycle GAN to improve clustering accuracy

Abstract

In recent years, a decrease in the number of farmers and the aging of the farming population in Japan have led to a labor shortage and a decline in production efficiency. One solution is the use of artificial intelligence technology to improve work efficiency, but this requires a large amount of training data to be developed. In this study, the goal is to develop a clustering instrument to select scratches and corrosion of crops, but it is difficult to obtain a large amount of data on scratches and corrosion. In this paper, we report on our efforts to create a variety of data using Cycle GAN by interpolation techniques, while maintaining the required number of data by using the already obtained data.