Classification of Lycopersicon Esculentum Fruit Based on Color Features with Linear Discriminant Analysis (LDA) Method

Classification of Lycopersicon Esculentum Fruit Based on Color Features with Linear Discriminant Analysis (LDA) Method

• By Budi, Arif, Setya; Hasan, Moh, arie; Hadianti, Sri; Merlina, Nita; Riana, Dwiza;
• Seminar Internasional Bereputasi
• 253

Tomatoes (Lycopersicon Esculentum) is a fruit that
has many types. One way to distinguish the types of tomatoes
can be seen from differences in color, size, and shape. To assist
in the classification of tomatoes, a study was conducted which
aims to classify two types of tomatoes namely plum tomatoes
and beef tomatoes. Therefore, the processing of tomato images
is done to facilitate the layman in classifying the two types of
tomatoes. The research method used consisted of preprocessing
in the form of color conversion l*a*b and HSV for feature
extraction, and Linear Discriminant Analysis (LDA) method
used to determine data distribution. This method can separate
and classify the two types of tomatoes well. The use of adequate
training data will further improve classification accuracy. The
final results of this study indicate that the level of accuracy in
the classification of tomatoes for both types is 90%.  

• Katakunci Lycopersicon esculentum, Linear Discriminant Analysis (LDA), Classification of Tomatoes, l*a*b, HSV
• URL https://ieeexplore.ieee.org/abstract/document/8985787
• Penerbit ,

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REFERENCES
[1] P. Isola, J. Zhu, … T. Z.-P. of the I., and undefined 2017, “Image-to

image translation with conditional openaccess.thecvf.com.

adversarial networks,”

[2] P. Sehgal and N. Goel, “Auto-annotation of tomato images based on
ripeness and firmness classification for multimodal retrieval,” in 2016
International Conference on Advances in Computing,
Communications and Informatics (ICACCI), pp. 1084–1091, 2016.
[3] L. Zhang, J. Jia, G. Gui, X. Hao, W. Gao, and M. Wang, “Deep
Learning Based Improved Classification System for Designing
Tomato Harvesting Robot,” IEEE Access, vol. 6, pp. 67940–67950,
2018.
[4] R. G. de Luna, E. P. Dadios, and A. A. Bandala, “Automated Image
Capturing System for Deep Learning-based Tomato Plant Leaf
Disease Detection and Recognition,” in TENCON 2018 - 2018 IEEE
Region 10 Conference, pp. 1414–1419, 2018.