Rice Grain Size Measurement using Image Processing
This work addresses grain quality assessment for agriculture, but it is incremental as it applies existing image processing techniques to a specific domain.
The researchers tackled the problem of inefficient and inconsistent manual rice grain size measurement by developing an image processing approach that detects 95% of grains and achieves 90% accuracy in length and width measurements.
The rice grain quality can be determined from its size and chalkiness. The traditional approach to measure the rice grain size involves manual inspection, which is inefficient and leads to inconsistent results. To address this issue, an image processing based approach is proposed and developed in this research. The approach takes image of rice grains as input and outputs the number of rice grains and size of each rice grain. The different steps, such as extraction of region of interest, segmentation of rice grains, and sub-contours removal, involved in the proposed approach are discussed. The approach was tested on rice grain images captured from different height using mobile phone camera. The obtained results show that the proposed approach successfully detected 95\% of the rice grains and achieved 90\% accuracy for length and width measurement.