<![CDATA[Tomatoes, as a type of vegetable or fruit, are often susceptible to damage, making handling them a complex challenge. Distinguishing between fresh and damaged tomatoes is very important, considering the significant impact on nutritional value and economic aspects. Traditional approaches via visual inspection have proven to be less efficient and inconsistent in their detection accuracy. To overcome this challenge, the use of images is a vital solution for distinguishing ripe, half-ripe and unripe tomatoes. In this context, HSI (Hue, Saturation, Intensity) calculations are applied to measure RGB color and room transformations. Images are extracted in jpg format, saved as training data, and this method is implemented using the Python programming language and GUI interface design in MATLAB. The research results show the HSI value for each class, with the ripe tomato class having an average hue of 0.0051 – 0.026, saturation 0.1862 – 0.3291, and intensity 0.0975 – 0.7522. Half-ripe tomatoes have hue 0.0208 – 0.0848, saturation 0.1346 – 0.5746, and intensity 0.1056 – 0.4714, while immature tomatoes have hue 0.0174 – 0.0689, saturation 0.0474 – 0.2072, and intensity 0.0595 – 0.3203. The integration of the HSI algorithm steps with the RGB color space provides an additional dimension to color analysis, which has the potential to increase the accuracy of tomato ripeness detection.]]>
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