A novel method for identifying rice seed purity based on hybrid machine learning algorithms

TL;DR

This paper introduces a new hybrid machine learning approach combining deep learning feature extraction with classification algorithms to automatically determine rice seed purity, enhancing accuracy over existing methods.

Contribution

It presents a novel hybrid machine learning method that integrates deep learning and traditional algorithms for rice seed purity identification, demonstrating improved performance.

Findings

Significant performance improvement over existing methods

Effective automatic identification of rice seed purity

Potential for practical application in seed quality control

Abstract

In the grain industry, the identification of seed purity is a crucial task as it is an important factor in evaluating the quality of seeds. For rice seeds, this property allows for the reduction of unexpected influences of other varieties on rice yield, nutrient composition, and price. However, in practice, they are often mixed with seeds from others. This study proposes a novel method for automatically identifying the rice seed purity of a certain rice variety based on hybrid machine learning algorithms. The main idea is to use deep learning architectures for extracting important features from the raw data and then use machine learning algorithms for classification. Several experiments are conducted following a practical implementation to evaluate the performance of the proposed model. The obtained results show that the novel method improves significantly the performance of existing methods. Thus, it can be applied to design effective identification systems for rice seed purity.