Grading method of fresh corn ear maturity based on pressure and image

In order to realize objective evaluation of maturity grading of fresh corn ear, a method using pressure sensor and computer vision was presented. Maturity grading detection device was developed. The inertia moment of texture information and the maximum pressure obtained from pressure detection device were used as maturity grading characteristic parameters of fresh corn ear. Maturity was classified into 3 grades through system cluster method. Eleven color characteristics were optimized and screened by principal components analysis. The first and the second principal components were applied to represent the eleven color characteristics in the grading, so dimension reduction was implemented. Results showed that inertia moment, maximum pressure, the first and the second principal component values of color characteristics were used as inputs of the probabilistic neural network developed for maturity grading of fresh corn ear, with grading accuracy 96.67%. Fresh corn ear maturity grading can be implemented accurately by combination of pressure sensor and computer vision technology.