Height estimation of sugarcane tip cutting position based on multimodal alignment and depth image fusion

Abstract

Sugarcane tip cutting is essential to reducing the rate of impurities in the harvest. To achieve adaptive regulation of the tip-cutting position by a sugarcane harvester, we propose a method for estimating the height of the tip-cutting position of sugarcane. The RGB and Binocular depth cameras are aligned to process the sugarcane tip region image. This involves threshold segmentation, morphological operations, and contour detection to identify the tip-cutting position and upper boundary contours. The depth image is segmented using contour pixel information and merged to form a colour depth image of the sugarcane's tip. This image is then transformed using depth data and triangular parallax principles to determine the height of the sugarcane tip-cutting position. The proposed method was evaluated in various sugarcane plantation environments. Comparative analysis between the proposed method and manual measurements of actual cutting position heights revealed that the RMSE ranged from 1.22 cm to 1.78 cm, and R² varied between 0.79 and 0.86. These results demonstrate the effectiveness of the proposed method in accurately extracting the height information of sugarcane tip-cutting positions, which has a specific application value for the adaptive adjustment of the tip-cutting device of the sugarcane harvester.