An Automated Image Processing Module for Quality Evaluation of Milled Rice.

ABSTRACT: The paper demonstrates a low-cost rice quality assessment system based on image processing and machine learning (ML) algorithms. A Raspberry-Pi based image acquisition module was developed to extract the structural and geometric features from 3081 images of eight different varieties of rice grains. Based on features such as perimeter, area, solidity, roundness, compactness, and shape factor, an automatic identification system is developed to segment the grains based on their types and classify them by using seven machine learning algorithms. These ML models are trained using the images and are compared using different ML models. ROC curves are plotted for each model for quantitative analysis to assess the model's performance. It is concluded that the random forest classifier presents an ac

SUBMITTER: Kurade C 

PROVIDER: S-EPMC10048426 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature