The use of optical sensors in assessing crop health, plant fertilizer demand, and yield prediction is the new method in the agricultural sector. Grennseeker optical sensor is one of the latest tools that has recently gotten much attention from worldwide farmers and researchers. Detailed examination of the variables mentioned above and measuring canopy coverage can improve production management and minimize input consumption. Therefore, this study aimed to investigate the GS sensor's ability to assess the nutritional status and surface area of the canopy and predict the performance of the garlic crop. In this research, five N-fertilizer treatments of N0, N50, N100, N150, and N200 were applied to garlic in a randomized complete block design with three replications. The image processing results in Labview software showed that the best separation performance for the elimination of background from the canopy could be achieved with the function of G-R. The results revealed that the best correlation coefficient between NDVI and LAI was 0.69, but no significant relationship was found between N and NDVI. Finally, it can be concluded that the GS could not estimate the N level of garlic. However, it could perform well in estimating tuber yield and canopy performance  satisfactorily.