Deep Learning-Based Rice Quality Evaluation using Image Processing for Physical Attribute Analysis

Abstract

The quality check of rice grain was done manually by experienced inspectors, but their analysis was incorrect.
This paper proposes an automated strategy for collecting data on various rice types and analysing them based on
their physical properties. We used methods such as computer vision and digital image processing, which included
pre-processing, morphological analysis, edge and object detection, and object measurement. The system is trained
using both manual and machine learning techniques. The findings of image processing are saved to a file, and
hypotheses for manual and machine learning training are generated using SVM and manual approaches. The
quality is then examined to establish whether the two ways result in higher or lower marks, and the best
methodology is chosen through observation.