Detection System of Strawberry Ripeness Using K-Means

Dolly Indra(1*); Ramdan Satra(2); Huzain Azis(3); Abdul Rachman Manga(4); Harlinda L(5);

(1) SCOPUS ID: 57196343003 - Computer Science - Universitas Muslim Indonesia
(2) Universitas Muslim Indonesia
(3) Universitas Muslim Indonesia
(4) Universitas Muslim Indonesia
(5) Universitas Muslim Indonesia
(*) Corresponding Author



Strawberry is one type of fruit that is favored by the people of Indonesia. The detection process to identify strawberries can be done by utilizing advances in computer technology, One of them is in the field of digital image processing. In this study, we made a strawberry ripeness detection system using the values of Red, Green and Blue as the reference values, while for identification in determining the type of classification using the K-Means algorithm that uses the Euclidean distance difference as the reference. Based on the results of testing using the K-Means algorithm on 51 strawberry images consisting of ripe, semi ripe and raw fruit yielding an accuracy rate of 82.14%, we also conducted tests other than strawberry images as many as 8 images yielded an accuracy rate of 100%.


Strawberry; detection system; RGB; K-Means; Euclidean Distance.


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L. Ma et al., Algorithm of Strawberry Disease Recognition Based on Deep Convolutional Neural Network, Complexity, vol. 2021, 2021, doi: 10.1155/2021/6683255.

H. Zhang, P. Lin, J. He, and Y. Chen, Accurate Strawberry Plant Detection System Based on Low-altitude Remote Sensing and Deep Learning Technologies, 2020 3rd Int. Conf. Artif. Intell. Big Data, ICAIBD 2020, pp. 15, 2020, doi: 10.1109/ICAIBD49809.2020.9137479.

Y. Yu, K. Zhang, H. Liu, L. Yang, and D. Zhang, Real-Time Visual Localization of the Picking Points for a Ridge-Planting Strawberry Harvesting Robot, IEEE Access, vol. 8, pp. 116556116568, 2020, doi: 10.1109/ACCESS.2020.3003034.

N. Lamb and M. C. Chuah, A Strawberry Detection System Using Convolutional Neural Networks, Proc. - 2018 IEEE Int. Conf. Big Data, Big Data 2018, pp. 25152520, 2019, doi: 10.1109/BigData.2018.8622466.

H. Y. Riskiawan, T. Rizaldi, and D. P. S. Setyohadi, Identification Strawberry Maturity using Nave-Bayes and Image Processing, pp. 394400.

P. Lin and Y. Chen, Detection of Strawberry Flowers in Outdoor Field by Deep Neural Network, 2018 3rd IEEE Int. Conf. Image, Vis. Comput. ICIVC 2018, pp. 482486, 2018, doi: 10.1109/ICIVC.2018.8492793.

S. Anraeni, D. Indra, D. Adirahmadi, S. Pomalingo, Sugiarti, and S. H. Mansyur, Strawberry Ripeness Identification Using Feature Extraction of RGB and K-Nearest Neighbor, 3rd 2021 East Indones. Conf. Comput. Inf. Technol. EIConCIT 2021, pp. 395398, 2021, doi: 10.1109/EIConCIT50028.2021.9431854.

D. Indra, Pendeteksian Tepi Objek Menggunakan Metode Gradien, Ilk. J. Ilm., vol. 8, no. 2, pp. 6975, 2016, doi: 10.33096/ilkom.v8i2.48.69-75.

D. Indra and L. N. Hayati, Skin Detection Using Color Distance Measurement And Thresholding, no. 5, pp. 14411443, 2019, doi: 10.35940/ijeat.E1209.0585C19.

D. Indra, Purnawansyah, S. Madenda, and E. P. Wibowo, Indonesian sign language recognition based on shape of hand gesture, Procedia Comput. Sci., vol. 161, pp. 7481, 2019, doi: 10.1016/j.procs.2019.11.101.

D. Indra, S. Madenda, and E. P. Wibowo, Feature Extraction of Bisindo Alphabets Using Chain Code Contour, Int. J. Eng. Technol., vol. 9, no. 4, pp. 32333241, 2017, doi: 10.21817/ijet/2017/v9i4/170904142.

D. Indra, T. Hasanuddin, R. Satra, and N. R. Wibowo, Eggs Detection Using Otsu Thresholding Method, Proc. - 2nd East Indones. Conf. Comput. Inf. Technol. Internet Things Ind. EIConCIT 2018, no. 2, pp. 1013, 2018, doi: 10.1109/EIConCIT.2018.8878517.

A. Pisal, R. Sor, and K. S. Kinage, Facial Feature Extraction Using Hierarchical MAX(HMAX) Method, 2017 Int. Conf. Comput. Commun. Control Autom. ICCUBEA 2017, no. figure 2, pp. 15, 2018, doi: 10.1109/ICCUBEA.2017.8463755.

G. Kumar and P. K. Bhatia, A detailed review of feature extraction in image processing systems, Int. Conf. Adv. Comput. Commun. Technol. ACCT, pp. 512, 2014, doi: 10.1109/ACCT.2014.74.

S. Yuqing, Q. Junfei, and H. Honggui, Structure design for RBF neural network based on improved K-means algorithm, Proc. 28th Chinese Control Decis. Conf. CCDC 2016, pp. 70357040, 2016, doi: 10.1109/CCDC.2016.7532265.

S. E. Basri, D. Indra, H. Darwis, A. W. Mufila, L. B. Ilmawan, and B. Purwanto, Recognition of Indonesian Sign Language Alphabets Using Fourier Descriptor Method, 3rd 2021 East Indones. Conf. Comput. Inf. Technol. EIConCIT 2021, pp. 405409, 2021, doi: 10.1109/EIConCIT50028.2021.9431883.

R. Huang, C. Cui, W. Sun, and D. Towey, Poster : Is Euclidean Distance the best Distance Measurement for Adaptive Random Testing pp. 406409, 2020, doi: 10.1109/ICST46399.2020.00049.


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