Classification of Thai Rice Varieties Using Image Processing and Deep Learning Techniques

The methods for identifying Thai rice varieties are complex, time-consuming, and require high expertise to achieve accurate results. This research explores different deep learning techniques to efficiently classify the strains of Thai rice that optimize accuracy and speed. The focus rice varieties are Khao Hom-Mali Thai and Thai Hom Pathum Thani 1 fragrant rice; both have similar shapes and characteristics but differ in price, market value, and recognition. The proposed model is based on an instance segmentation model of YOLOv8, which is compared against popular instance segmentation models such as YOLACT, SOLOv2, and Mask R-CNN. Additionally, hyperparameter tuning is performed to ascertain the most optimal values. The evaluation of the model performance reports in the form of mean average precision (mAP), inference time, and model stability. Experimental results indicate that YOLOv8n-seg, with the fewest parameters, achieves the highest accuracy comparable to other YOLOv8-based models with more parameters. The proposed model demonstrates superior accuracy and processing speed performance compared to other state-of-the-art models.