05/06/2026
Enhancing rice quality assessment through machine learning.
Discover the full study here: dx.doi.org/10.12944/CARJ.12.2.21
This comparative study evaluates CNNs and traditional machine learning techniques for rice grading, highlighting the superior performance of texture-feature-based models with accuracy reaching 99.2%, paving the way for reliable and automated grain quality assessment.