| dc.description.abstract | Decision Support Systems (DSS) are wildly used in agriculture, industry, and
healthcare, due to their efficient information management and decision-making
activities. Melon has a considerably large genetic variability and is widely used in
scientific research such as biology and genetics. In this study, we aimed to develop
a system to detect the right harvesting time for melon fruits because the quality
cannot be maintained after harvesting. Hence, it would be best if the harvesting is
carried out at the right time, which will not affect the fruit harvested earlier.
Secondly, melon fruit harvesting is done every day and not on the same day for all
fruits due to environmental factors. The harvest DSS will classify melon’s
categories on the plant regarding melon’s skin color by the guidance of the expert.
The essential sensory factor for freshness and maturity is color, as mentioned in
many studies. We use the skin color to classify melon fruit while it is on the plant
into three categories: Ripe, About to Ripe, and Under Ripe, using a melon image.
Firstly, localizing the melon on the plant using SSD and extracting its skin color.
The input of fuzzy logic is the extracted color channels means values. The
extraction of color is a challenging job because some fruits blocked with plant’s
stem, root, or leaves, so, we divided the process of reaching the purest melon skin
into several steps: detecting the melon fruit on the plant, segmenting the detected
melon and removing the unwanted part from the skin, and finally, applying image
normalization and calculate mean values for melon’s skin color. In this study, we
achieved 91-100% accuracy in classifying melon ripeness levels. All images source
were captured using iPhone rear camera. | id |
