Background: The advances in precision agriculture have improved the monitoring system of agricultural crops growth and yield estimation. Yield is the primary key for precision crop management. Estimation of rice yield within the field, could be an option to find out higher or lower yield zone. Problem identification would be easier and suitable practices can be applied to improve yield. So, the main objective of this paper is to propose an image processing technique to detect the rice grains using low altitude unmanned aerial vehicle (UAV) images. Methods: We propose an image processing algorithm. Firstly, the algorithm read initial RGB image and a noise filter was used to remove the noise. Secondly, it converted RGB to L*a*b* color space and thirdly, the k-means clustering was used to classify colors in 'a*b*' space. Finally, reshape by cluster indexing and labelling of the pixels in the image was attained using k-means and then image segmentation by color was completed. On segmented image, by using k-mean clustering, blob of the rice grain panicles were gathered as one. The estimation of area of segmented grain panicles and leaves were done. Results: Proposed method have shown that, the area of segmented rice grain panicles and leaves were accurately estimated. The segmentation method is rapid and easy to apply but in some cases, it is needed to adapt the light of images. Discussions: Our method was efficient for area estimation of rice grain panicles using UAV images. In some cases we get less significant results due to the noise caused by reflectance of bared soil. Conclusion: The proposed method is able to estimate rice grain panicles and using this method we expect to estimate the volume of rice grain to predict the grain yield and the harvesting time by observing the grain and leaves color and size. Acknowledgement: This Research was supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries (IPET) through Agriculture, Food and Rural Affairs Research Center Support Program (Project No: 714002–07) funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA).