Image Extraction of Lettuce Leaves using Fast Fourier Transform Method and Color Moments

Danang Erwanto, Yudo Bismo Utomo

Abstract


Lettuce (Lactuca sativa L.) is a seasonal leaf vegetable with high nutritional content which is usually fresh for consumption. Typical harvest age for lettuce is 45 days. Lettuce which is harvested more than 45 days will affect the taste of the lettuce. In addition to the lettuce’s age, there are several things that can affect taste of the lettuce, including room temperature, harvest time, and thickness of the leaf color. In this study, Fast Fourier transform (FFT) was used as feature extraction by changing the spatial domain in frequency domain image of the lettuce leaves, while the color moment method was used as the extraction of lettuce leaves. With this digital image processing, it can automatically identify maturity level of lettuce leaves. The classification process uses the Naïve Bayes method with the Weka application. The obtained results of texture and color extraction using FFT method and the color moment using Naïve Bayes classification method in the Weka application work well. From the results of the age classification of lettuce based on its leaf color, the average percentage of total accuracy was 94.4%. The correlation of color and taste using the correlation test which performed by SPSS, and it was found that there are positive relationship between color and taste with sig. (1-tailed) < 0.05.

Keywords


Color Moment;Fast Fourier Transform;Lettuce Leaves;Naïve Bayes Method

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DOI: https://doi.org/10.26905/jeemecs.v5i2.5275

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