| Abstract |
Rice is a globally important agricultural crop, with extensive cultivation and
consumption in Asia. In Thailand, it is a primary food crop and a crucial
export commodity. However, ensuring the quality standards of Thai rice is
challenging due to variations in grain mixtures, making conventional inspection methods laborious and time-consuming. Human judgment in visual
inspection introduces the risk of discrepancies. To address this, a swift and
accurate solution is needed for quality analysis and differentiation of rice
grain categories. Image processing techniques and machine learning offer
a promising approach for accurate rice grain classification and reducing
human grading errors. In a recent study focused on jasmine rice (KDML
105) samples, images of rice grains were captured using a developed device.
Morphological features related to shape and size were extracted through
image processing. The Boruta algorithm was employed to select significant
features, which were then used to train various machine learning classifiers.
After training and validation, the random forest classifier demonstrated the
highest performance and was chosen as the main classification model. It was
then tested with a new dataset to evaluate its identification accuracy. The
selected model successfully classified four categories of rice grains with an
accuracy exceeding 99.00%. While research efforts have primarily focused on
classifying rice varieties and detecting grain abnormalities, incorporating
a combination of morphology, color, and texture features is essential for
highly accurate predictions. However, when it comes to predicting rice grain
types with distinct shapes and sizes, considering relevant morphological
characteristics during the model development process is sufficient to achieve
highly precise and accurate results. |
Publication
Journal Publication
ไทย