Identification of damaged corn seeds using air-coupled ultrasound

Corn, an important staple in many countries around the world, is subject to a very inefficient germination rate due to worm-damaged seeds. However, air-coupled ultrasound is a rapid, safe and widely accepted method for the early detection of such damage. In this study, the current effectiveness and future prospects of this technique for identifying damaged seeds were explored. The presented procedure started with drawing a sample of 810 seed particles, consisting of 400 that were intact, 400 manually damaged and 10 damaged by worms. Then the principal component analysis (PCA) method was used to reduce the dimensions of air-coupling ultrasonic information and extract the top ten principal components. Finally, a KNN decision tree by using SIMCA software and a Fisher recognition model by using MATLAB software were constructed. The pattern recognition was established by using KNN, which has the most accurate recognition rate. The correct recognition rate of modeling for the front and back data of the intact particles was 98% and 100%, respectively; and for the manually damaged particles, 99% and 97%, respectively. The results show that the model developed by using air-coupled ultrasonic data can classify corn seed particles both with and without holes to provide a basis for the development of a seed selection system, which has a significant role in improving the clarity and the germination rate.