Sistem Pendeteksi Kematangan Tomat Menggunakan Metode Color Histogram Dan Nearest Neighbor
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Institusion
Institut Teknologi Telkom Purwokerto
Author
Miftakhul, Jannah
Subject
T Technology (General)
Datestamp
2022-04-14 07:42:55
Abstract :
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. Image processing has been develop in many sectore such as healthcare, factory, and working process. This study aims to utilize image processing technology in agricultural comodities. Tomato (Lycopersicum esculentum Mill) is a type of horticultural plant that has a relatively fast maturity time compared to other fruits. Production of Tomato comodities need to maintain especially in harvesting process. Image processing can be applied to 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 95% while the precision value obtained is 96% and the recall value obtained is 94,11%. The results of this study are expected to support the implementation of smart farming 4.0 in Indonesia. Keywords:color hidtogram, image processing, smart farming
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. Image processing has been develop in many sectore such as healthcare, factory, and working process. This study aims to utilize image processing technology in agricultural comodities. Tomato (Lycopersicum esculentum Mill) is a type of horticultural plant that has a relatively fast maturity time compared to other fruits. Production of Tomato comodities need to maintain especially in harvesting process. Image processing can be applied to 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 95% while the precision value obtained is 96% and the recall value obtained is 94,11%. The results of this study are expected to support the implementation of smart farming 4.0 in Indonesia. Keywords:color hidtogram, image processing, smart farming
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