Visual identification of material attributes in wakame: exploring thickness, strength, and chlorophyll content

This study investigates the potential of using visual features to predict key material attributes in wakame, focusing on thickness, strength, and chlorophyll content (SPAD values). We compared frozen and salted wakame samples to understand how different processing methods affect these predictions. Using a combination of RGB, L*a*b*, and HSV color features, we developed and evaluated various regression models, including simple linear regression, quadratic regression, and random forests. Our results indicate that color features can effectively predict SPAD values, particularly in frozen samples, with the best models achieving an R 2 of 0.900. However, predicting thickness and strength proved more challenging, with models showing limited predictive power. Interestingly, strength predictions were more accurate for salted samples, suggesting that salt curing may enhance the relationship between visual features and physical strength. We found that processing methods significantly impact the effectiveness of prediction models. Freezing appears to better preserve the original optical properties of wakame, while salt curing introduces greater complexity, necessitating more sophisticated modeling approaches. This study contributes to the development of rapid, non-destructive methods for assessing wakame quality, which is crucial for the growing wakame industry. Our findings highlight the potential of visual analysis in wakame quality assessment while also emphasizing the need for tailored approaches based on processing methods. Future work should focus on refining these models and exploring additional factors that influence wakame properties.