Gradient-based method for the identification of multi-nodes in sugarcane

Abstract

In view of the obvious changes in color between the upper and lower leaf scar in sugarcane nodes, a method of simultaneous multi-nodes identification on a single sugarcane stem was proposed based on the analysis of gradient characteristics of sugarcane images. In combination with image processing and machine vision recognition technology, two cameras were used to acquire different parts of sugarcane images, and the two images were integrated into a complete image of sugarcane by image mosaicking. The Sobel operator is used to calculate the gradient of the sugarcane image in a horizontal direction, and the gradient image is obtained. The sugarcane gradient image was scanned by a rectangular template with a width of 14 pixels and a step length of 12 pixels. The features of average gradient and variance gradient were used to identify sugarcane nodes for the first time. The experimental results showed that the recognition accuracy was 96.8952%, and there were fewer false detected sugarcane segments. The detection efficiency could be improved by detecting multi-nodes on a single sugarcane stem at the same time.