Recognition of Weeds in Wheat Fields Based on the Fusion of RGB Images and Depth Images

Abstract

Due to the low recognition rate of weeds in wheat fields and the inability to accurately locate weeds, we propose a recognition method for weeds in natural wheat fields based on the fusion of RGB image features and depth features. The method breaks through the limitations of the two-dimensional spatial features extracted from RGB images when recognizing grass weeds similar to wheat. According to the species, distribution of weeds in wheat fields, we extracted the color, position, texture, and depth features of weeds in wheat fields from RGB and depth images during the tillering and jointing stages. And then used the AdaBoost algorithm for the integrated learning of multiple classifiers, thereby achieving the recognition of weeds in wheat fields. The experimental results revealed that the recognition speed of weeds during the tillering stage was 0.2 s and the accuracy rate was 88%. The recognition speed of weeds during the jointing stage was 0.69 s, and the accuracy rate of weed recognition was 81.08%. These results are significantly higher than the weed recognition rate based on features extracted from RGB images.