TOMATO MATURITY DETECTION SYSTEM USING COLOR HISTOGRAM METHOD AND NEAREST NEIGHBOR

Abstract

Tomato (Lycopersicum esculentum Mill) is a type of horticultural plant that has a relatively fast maturity time compared to other fruits. In order to accelerate the implementation of smart farming in Indonesia, various methods that support agriculture have been developed. One of the technologies that can be applied to develop agriculture in Indonesia is image processing. This study aims to utilize image processing technology to detect and classify ripe and unripe tomatoes. The system is made using static images taken using a digital camera. The marker used in making the system to detect tomato ripeness is the color histogram. While the method for grouping tomatoes uses the nearest neighbor method. This study proves that the performance of the color histogram and nearest negihbor can be used to detect and classify tomatoes that are not ripe or ripe. The accuracy value obtained using this method is 80% while the precision value obtained is 80.53% and the recall value is 81.5%. The results of this study are expected to support the implementation of smart farming 4.0 in Indonesia.

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