Email this article 
Detection of Mango Tree Varieties Based on Image Processing
Abstract
Mango is one of the most favourite fruits in the world. Therefore, this type of fruit has been researched deeply to enrich the variety. Here, the purpose of this study was to find the easiest method to determine the type and the variety of mango. In short of the experimental method, we analyzed the leaf using an image processing method. To confirm our result, several analyses were also conducted: leaves process digital image acquisition, and preprocessing, as well as feature extraction and classification. The result showed that our image processing method was effective to detect the variation of up to 78%. We believe that further study using this method will be effective for other types of fruits.
Keywords
Full Text:
PDFReferences
Ahmad, U. (2010). Aplikasi Teknik Pengolahan Citra dalam Analisis Non-Destruktif Produk Pangan, Jurnal Pangan, 19 (1), 71-80
Agustin, S., and E. Prasetyo. (2011). Klasifikasi Jenis Pohon Mangga Gadung Dan Curut Berdasarkan Tesktur Daun, Proseding SESINDO, Institut Teknologi Sepuluh Nopember, Surabaya
Dhaygude, S.B., and N.P. Kumbhar. (2013). Agricultural plant Leaf Disease Detection Using Image Processing, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, 2 (1), 599-602.
Ganiron, T.U. (2014). Size Properties of Mangoes using Image Analysis, International Journal of Bio-Science and Bio-Technology, 6 (2), 31-42.
Imaduddin, Z., H.A. Tawakal. (2015). Aplikasi Mobile untuk Deteksi dan Klasifikasi Daun secara Real Time, Jurnal Teknologi Terapan, 1 (1), 27-30.
Kadir, M.F.A, N.A.N. Yusri, M. Rizon, A.R. Mamat, M. Makhtar, A.A Jamal. (2015). Automatic Mango Detection Using Texture Analysis and Randomised Hough Transform, Applied Mathematical Sciences, 9 (129), 6427 - 6436
Kadu, R.N, S. Kangan, S. Vikhe, R. Pandita, V. Inamke. (2015). Leaf Disease Detection Using Arm7 and Image Processing, Int. Journal of Engineering Research and Applications, 5 (2), 68-71.
Maqbool, I., S. Qadri, D.M. Khan, M. Fahad. (2015). Identification of Mango Leaves by Using Artificial Intelligence, International Journal of Natural and Engineering Sciences, 9 (3), 45-53
Permatasari, N., Sucahya, T. N., & Nandiyanto, A. B. D. (2016). Review: Agricultural Wastes as a Source of Silica Material. Indonesian journal of science and technology, 1(1), 82-106.
Prasetyo, E. (2011). Pengolahan Citra Digital dan Aplikasinya Menggunakan Matlab, 1st Edition, Andi Offset, Yogyakarta.
Prasetyo, E. (2012). Perbaikan Sistem Pengenal Jenis Pohon Mangga Menggunakan SVM dan FK-NNC, SCAN, 7 (3), 9-14
Prasetyo, E. (2014). Data Mining – Mengolah Data menjadi Informasi, Andi Offset: Yogyakarta, Indonesia.
Prasetyo, E. (2015). Analisis Fitur Tekstur Daun Mangga Dengan Fisher’s Discriminant Ratio Untuk Pencapaian Fitur Yang Informatif, Jurnal Teknologi Informasi dan Terapan, 02 (01), 197-204
Theodoridis, S., K. Koutroumbas. (2009). Pattern Recognition – 4th edition, Academic Press: Burlington,MA, USA.
Sanjana, Y., A. Sivasamy, S. Jayanth. (2015). Plant Disease Detection Using Image Processing Techniques, International Journal of Innovative Research in Science, Engineering and Technology, 4 (6), 295-301.
Yamamoto, K., W. Guo, Y. Yoshioka, and S. Ninomiya S. (2014). On Plant Detection of Intact Tomato Fruits Using Image Analysis and Machine Learning Methods, Sensors, 14, 12191-12206.
DOI: https://doi.org/10.17509/ijost.v1i2.3800
Refbacks
- There are currently no refbacks.
Copyright (c) 2016 Indonesian Journal of Science and Technology
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Indonesian Journal of Science and Technology is published by UPI.
View My Stats