Automatic Segmentation of Crop/Background Based on Luminance Partition Correction and Adaptive Threshold

Abstract

Crop segmentation is a fundamental step of extracting the guidance line for an automated agricultural machine with a visual navigation system. However, the segmentation quality of green crop is seriously affected by the outdoor lighting conditions. To improve the accuracy of crop segmentation under complex lighting conditions, a color-index-based crop segmentation method with luminance partition correction and adaptive thresholding is proposed in this article. The method extracts the luminance component from the given RGB image and employs two adaptive thresholds to divide the luminance image into the dark, normal and bright areas. Then, a partition Gamma function is adaptively selected to improve the brightness levels of the dark and bright regions in which the Gamma correction parameter is adaptively determined based on the local distribution characteristics of illumination, and the corrected image is converted to the RGB counterpart through color saturation restoration. Finally, the ExG (excess green index) color index with Otsu thresholding is used to perform pre-segmentation in order to calculate the segmentation threshold for the final segmentation. Experimental results show that the proposed approach can effectively increase the brightness levels of the dark region and decrease the brightness levels in the bright region. In addition, compared with the traditional Otsu method employed in before and after luminance correction, the proposed method can achieve better segmentation results.