Improved GrabCut-based method for coffee leaf segmentation

This study proposes a GrabCut algorithm to address the segmentation challenges in coffee leaf images. The algorithm integrates the Contrast Limited Adaptive Histogram Equalization algorithm and the Gamma Correction technique. The leaf images undergo preprocessing using Contrast Limited Adaptive Histogram Equalization and Gamma Correction before being segmented with the GrabCut algorithm. Experimental evaluations are conducted using coffee leaf images, comparing results in terms of intuitive segmentation visualization and quantized indices such as PSNR, segmentation accuracy, false segmentation rate, and algorithm runtime. The findings indicate that the proposed algorithm exhibits greater robustness against variations in light conditions, resulting in higher segmentation accuracy. It outperforms classical three-class benchmark algorithms and effectively segments leaf images.