DOI : https://doi.org/10.18517/ijaseit.10.3.11135

Mobile Application for Identification of Coffee Fruit Maturity using Digital Image Processing

Oka Sudana (1), Deden Witarsyah (2), Adhitya Putra (3), Sunia Raharja (4)
(1) Department of Information Technology, Udayana University, Badung, 80361, Indonesia
(2) Telkom University
(3) Department of Information Technology, Udayana University, Badung, 80361, Indonesia
(4) Department of Information Technology, Udayana University, Badung, 80361, Indonesia
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How to cite (IJASEIT) :
Sudana, Oka, et al. “Mobile Application for Identification of Coffee Fruit Maturity Using Digital Image Processing”. International Journal on Advanced Science, Engineering and Information Technology, vol. 10, no. 3, June 2020, pp. 980-6, doi:10.18517/ijaseit.10.3.11135.
Citation Format :
Indonesia is an agricultural country that relies on the agricultural sector and is well known in producing various plantation commodities, one of which is coffee. Coffee is a leading export commodity developed in Indonesia. Community coffee plantations play an important role because most of the coffee production comes from community plantations. However, the condition of community coffee plantations can be said to be still hampered, due to the quality of coffee is still relatively low. It is caused by coffee fruit sorting, which is still done conventionally. The conventional sorting process of coffee fruits is still carried out with the help of operator knowledge, so the level of operator knowledge dramatically influences the results of sorting. The ease of sorting coffee ripeness can be done by implementing a mobile application using digital image processing. Techniques used in digital image processing are the HSV color space to get color features of coffee fruit and the K-Nearest Neighbor (KNN) classification method to classify coffee fruit ripeness. The results of the identification are in the form of ripe, half-ripe, or unripe fruits. The mobile application of this research has two main features, namely training data feature and non real-time identification feature. The results of the testing conducted resulted in an accuracy rate of 95.56% with the best membership value (k) of 3.

N. L. C. P. S. Suyasa, I. M. R. T. Setiawan, and M. Regina, “Kopi Arabika Jempolan Catur Village, Kintamani: Product and Promotional Mix Analysis,” SINTESA, 2018.

I. P. A. Heryana, I. M. Sudarma, and I. G. S. A. D. I. Putra, “Perbandingan Pendapatan antara Usahatani Kopi dan Usahatani Jeruk di Desa Serai Kecamatan Kintamani Kabupaten Bangli,” E-Journal Agribisnis dan Agrowisata (Journal Agribus. Agritourism), 2016.

T. Kustiasari, U. Setyoko, D. Arieni, and P. Prayitno, “Arabica Coffee Bean Quality Test With Wet Processing (Full Wash Processing) System At" Sejahtera Bersama" Farmers Group, Panti Sub-District, Jember Regency, East Java,” in Proceeding of the 1st International Conference on Food and Agriculture, 2018.

O. R. Indriani, E. J. Kusuma, C. A. Sari, and E. H. Rachmawanto, “Tomatoes classification using KNN based on GLCM and HSV color space,” in 2017 International Conference on Innovative and Creative Information Technology (ICITech), 2017, pp. 1-6.

E. V. A. Purnamaningtyas and E. M. A. Utami, “Implementation of K-Nearest Neighbor Algorithm Analysis in Predicting Regular Hajj Applicant Failure,” J. Theor. Appl. Inf. Technol., vol. 95, no. 20, 2017.

V. P. Maridelana, Y. Hariyati, and E. B. Kuntadi, “Fungsi Keuntungan Usahatani Kopi Rakyat di Desa Belantih Kecamatan Kintamani Kabupaten Bangli,” Berk. Ilm. Pertan., vol. 1, no. 3, pp. 47-52, 2014.

G. Schroth, P. Lí¤derach, D. S. B. Cuero, J. Neilson, and C. Bunn, “Winner or loser of climate change? A modeling study of current and future climatic suitability of Arabica coffee in Indonesia,” Reg. Environ. Chang., vol. 15, no. 7, pp. 1473-1482, 2015.

D. J. Perkebunan, Pedoman teknis budidaya kopi yang baik (Good Agriculture Practices /GAP on Coffee). 2014.

S. Febrianto Kurniawan, I. K. G. Darma Putra, and A. A. Kompiang Sudana, “Bone Fracture Detection Using Opencv,” J. Theor. Appl. Inf. Technol., vol. 64, no. 1, pp. 249-254, 2014.

W. Burger and M. J. Burge, Digital image processing: an algorithmic introduction using Java. Springer, 2016.

V. Chernov, J. Alander, and V. Bochko, “Integer-based accurate conversion between RGB and HSV color spaces,” Comput. Electr. Eng., vol. 46, pp. 328-337, 2015.

I. P. Pratama Andika, I. P. Agung Bayupati, and N. K. Ayu Wirdiani, “Rancang Bangun Aplikasi Pendeteksi Tipe Dan Nilai Resistor Berbasis Android,” Lontar Komput. J. Ilm. Teknol. Inf., pp. 61-5832, 2015.

G. Saravanan, G. Yamuna, and S. Nandhini, “Real time implementation of RGB to HSV/HSI/HSL and its reverse color space models,” in 2016 International Conference on Communication and Signal Processing (ICCSP), 2016, pp. 462-466.

M. P. A. Deole and R. Longadge, “Content Based Image Retrieval using Color Feature Extraction with KNN Classification,” IJCSMC, vol. 3, no. 5, pp. 1274-1280, 2014.

J. Maillo, S. Ramí­rez, I. Triguero, and F. Herrera, “kNN-IS: An Iterative Spark-based design of the k-Nearest Neighbors classifier for big data,” Knowledge-Based Syst., vol. 117, pp. 3-15, 2017.

D. A. Adeniyi, Z. Wei, and Y. Yongquan, “Automated web usage data mining and recommendation system using K-Nearest Neighbor (KNN) classification method,” Appl. Comput. Informatics, vol. 12, no. 1, pp. 90-108, 2016.

F. Astutik, “Sistem Pengenalan Kualitas Ikan Gurame dengan Wavelet, PCA, Histogram HSV dan KNN,” Lontar Komput. J. Ilm. Teknol. Inf., 2015.

R. M. Gurav and P. K. Kadbe, “Real time finger tracking and contour detection for gesture recognition using OpenCV,” in 2015 International Conference on Industrial Instrumentation and Control (ICIC), 2015, pp. 974-977.

H. Lahiani, M. Elleuch, and M. Kherallah, “Real time hand gesture recognition system for android devices,” in 2015 15th International Conference on Intelligent Systems Design and Applications (ISDA), 2015, pp. 591-596.

Z.-Q. Zhao, L.-H. Ma, Y. Cheung, X. Wu, Y. Tang, and C. L. P. Chen, “ApLeaf: An efficient android-based plant leaf identification system,” Neurocomputing, vol. 151, pp. 1112-1119, 2015.

A. F. Waruwu, I. P. A. Bayupati, and I. K. G. D. Putra, “Augmented Reality Mobile Application of Balinese Hindu Temples: DewataAR,” Int. J. Comput. Netw. Inf. Secur., vol. 7, no. 2, p. 59, 2015.

A. Tedeschi and F. Benedetto, “A real-time automatic pavement crack and pothole recognition system for mobile Android-based devices,” Adv. Eng. Informatics, vol. 32, pp. 11-25, 2017.

A. Tedeschi, A. Liguori, and F. Benedetto, “Information Security and Threats in Mobile Appliances,” Recent Patents Comput. Sci., vol. 7, no. 1, pp. 3-11, 2014.

H. Zhang, H. Wu, and A. Rountev, “Automated test generation for detection of leaks in Android applications,” in Proceedings of the 11th International Workshop on Automation of Software Test, 2016, pp. 64-70.

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