Identification of internal cracks in corn seed using convolutional neural networks

The identification of seed quality is very important for which the quality of seed is crucial to the yield and quality of crops. There are two main problems with the acquisition and identification of cracks inside corn seed. One is that most of the methods of near-infrared spectroscopy or X-ray are used to obtain images of cracks inside the seed, the acquisition equipment is expensive and the operation is complicated. The other is the identification of crack images, and the traditional image processing method is usually used which requires professionals to design different model parameters each time, resulting in poor model robustness and low model accuracy. In this study, we originally proposed a simple but effective method to obtain the picture of corn seed internal cracks, which is combined with visible light transmission and ordinary camera acquisition method. We also proposed using the transfer learning methods not only solving the problem of the small scale of our corn seed internal cracks dataset but also avoiding extracting features manually. Our proposed method achieved a promising result, which is able to correctly identify the cracked and intact corn seed 100% in our training stage and testing stage.