GPU based parallel image processing for plant growth analysis

Plant growth analysis is hard to do automatic. The burden of technique makes harder to process the algorithm. Thresholding and segmentation parts are huge part of the approaches. In this study 15 different thresholding algorithms were implemented and compared with images from field for plant growth analysis. To decrease execution time, the algorithm was implemented on GPU (Graphics Processing Unit) with CUDA (Compute Unified Device Architecture) language. Also, thresh-olding methods was applied on GPU. These are Huang's fuzzy, Intermodes, Isodata, Li's Minimum Cross Entropy, Kapur-Sahoo-Wong (Maximum Entropy), Mean, Minimum Error, Minimum, Moments, Otsu, Percentile, RenyiEntropy, Shanbhag, Triangle, and Yen thresholding algorithms. Each method investigated the thresholds on HSV histograms to find proper color values. After all process, threshold results for dynamic and constant values were listed and compared. Moreover, performance metrics were measured.