Quantitative damage detection of direct maize kernel harvest based on image processing and BP neural network

Abstract

Kernel direct harvesting is the mainstream technology of maize harvesting in the world today, it has significant impact on the maize kernel subsequent food processing. Direct harvest technology in china is not well developed due to the influence of growth environment, agronomy, etc., which leads kernel damage. The kernel damage is necessary studied in maize direct harvesting technology. Therefore, “Zheng Dan 958” was selected, the entrance clearance, export clearance, and cylinder speed as variables to carry out the kernel damage experiment. Processed the image of threshed maize kernel, extracted the crack and boundary characteristics of kernel damage, and established the BP neural network model to study the direct harvesting damage and optimize parameters. The results indicated that kernel damage increased with decreasing threshing clearance and increasing threshing intensity. In a certain threshing clearance, cylinder speed was the key factor affecting kernel damage. The R of model was above 0.95, the accuracy of damage quantitative identification was above 85%. When inlet clearance was 35 mm, outlet clearance was 15 mm, and cylinder speed was 300 rpm, kernel damage was small. Our findings will provide reference for kernel direct harvesting technology and improve harvest quality to meet food processing industry demands.

Keywords:
maize harvesting; kernel damage; image processing; BP neural network