Rice Grain Size Measurement using Image Processing

TL;DR

The paper addresses the need for automatic, reliable rice grain size measurement to replace manual, error-prone inspection. It presents a three-step image-processing pipeline that segments a region of interest, filters grain contours, and estimates size using best-fit ellipses calibrated against a known canvas reference to handle images captured at different heights. The approach achieves 95% grain detection and 90% accuracy in length/width measurements across 30 images, demonstrating practical potential for rapid quality control. This method can be extended to other grains and conditions, enabling objective, scalable assessment in agricultural processing.

Abstract

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.

Figures (9)

• Figure 1: Flowchart of the proposed approach for rice grain size measurement
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