A good quality of forage is essential to feed cattle to improve its health and productivity. Quality of grass has been affected by various factors such as weather condition, leaf disease, and diverse species with texture similarity. Over multiple species of grass and variation over sessions leads quality recognition more complex and difficulty. Proposed study uses unsupervised approach to reduce dependence of annotation over multiple species of grass. Proposed work uses median and fast denoising algorithm to enhance input image. Article present novel approach with integration of Gabor filter with k-means++  model. Gabor filters helps to identify optimized features over texture similarity problem of grass. Simulation of proposed study also measures denoising input grass image with 0.72 SSIM. Simulation of proposed study also finds remarkable performance with 79.86% accuracy with median and Gabor filter. Simulation of study also explore possible variations with hybrid unsupervised approach to enhance performance of the model.