Classification of tomato (Lycoersicon Esculentum Miil) ripeness levels based on HSV color using digital image processing

Abstract

Tomato is a fruit that undergoes a rapid ripening process. Classification of tomatoes based on the level of ripeness is very important during the distribution process of tomatoes in various regions. In general, farmers classify the ripeness level of tomatoes based on fruit color, because this is easy to do. Generally, the classification of the ripeness level of tomatoes is done manually by looking directly with the eyes and based on the color of the fruit. The problem is, manually identifying the level of ripeness of tomatoes based on color still has many shortcomings and the results are less than optimal. This is because humans have many limitations such as differences in perceptions by farmers, fatigue, lack of focus, relatively long time required and produce a variety of products due to human visual limitations and differences in perceptions about the quality of tomatoes. One alternative technology that can be used to reduce this problem is to use digital image processing with color spaces such as hue, saturation, value (HSV). HSV color has a tendency to detect color with a degree of dominance to get accurate object recognition results. Using digital image processing techniques, information is obtained that can be processed by a computer and the results are more precise, fast and automatic. Based on the results of research using image processing with HSV color space can identify the maturity level of tomatoes at three levels of ripeness, unripe, half-ripe and ripe.