Rice Grain Quality Analysis Using Image Processing

Abstract

Background of study: Rice quality is crucial for global food security and market value, but traditional assessment relies on labor-intensive, inconsistent, and error-prone manual inspection.Aims and scope of paper: This research proposes an automated system using image processing and AI for comprehensive rice grain quality analysis. The goal is to develop a robust, objective, and precise system to classify rice varieties and evaluate quality with minimal human intervention, reducing the effort, cost, and time of traditional methods.Methods: The core contribution is a computerized model that uses digital image processing to automatically segment, identify, and extract key quality parameters like length, width, area, perimeter, and shape descriptors. The methodology includes image acquisition, preprocessing (binary conversion, thresholding, noise reduction, morphological operations), edge detection, feature extraction (especially aspect ratio), classification, and visualization. The system was trained on a self-curated dataset of various rice varieties.Result: The system successfully analyzed Sona Masuri, Basmati, and Jasmine rice varieties based on grain count and average aspect ratio. Sona Masuri (211 grains, 1.57 aspect ratio) and Basmati (261 grains, 1.8 aspect ratio) were classified as 'Bold'. Jasmine (30 grains, 2.1 aspect ratio) was classified as 'Medium' , consistent with defined criteria.Conclusion: The project successfully analyzed and processed rice grain images to determine size, shape, and quality, accurately measuring length, width, and aspect ratio for classification. Image processing techniques improved image quality and defect detection. The system objectively applied classification logic, demonstrating high precision in rice grain quality determination, which is crucial for market value and consumer satisfaction.

Abstract from the paper’s OpenAlex record, shown for indexing and research. Copyright remains with the authors or the publisher. Read at the source ↗

The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.