Research on Detection of Moldy Corn Kernels Based on Machine Vision

Corn plays an important role in many fields, but the level of intelligent detection for moldy corn is low. This article proposes a method for identifying moldy corn kernels based on machine vision. First, the image is processed, and then the MSR method with color restoration in the Retinex image enhancement algorithm is used to enhance the image. The threshold is set in the HSV space, and the moldy corn kernels are segmented through morphological processing. The two processing results are combined through logical AND operation to obtain the result. The final detection result shows that the probability of effectively recognizing moldy corn kernels can reach around 95%. This proves that the algorithm proposed in the article can efficiently and quickly detect moldy individuals in corn kernels, providing technical support and theoretical basis for the future development of intelligent detection of moldy individuals in corn.