Classification of Banana Ripeness Based on Color and Texture Characteristics


  • Ahmad Hafidzul Kahfi Universitas Nusa Mandiri
  • Muhamad Hasan Universitas Nusa Mandiri
  • Riyan Latifahul Hasanah Universitas Nusa Mandiri




Classification, Fruit Maturity, Gray Level Co-Occurrence Matrix, K-Means Clustering, K-Nearest Neighbor

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Banana is one of the most consumed fruits globally and is a rich source of vitamins, minerals and carbohydrates. With the many benefits that bananas have, many farmers cultivate this fruit. The problem that occurs when the harvest is produced on a large scale is the process of selecting bananas that are still unripe or ripe. Usually farmers carry out the selection process manually by visually identifying ripeness based on the color of the fruit skin. However, direct observation has several drawbacks such as subjectivity, takes a long time and is inaccurate. For this reason, we need a system that can help determine the maturity level of bananas automatically through a series of banana image processing processes. One way that can be used to determine the maturity level of bananas is by looking at the color and texture of the bananas. This study aims to classify the maturity level of bananas based on the color and texture characteristics of the banana image using the Gray Level Co-occurrence Matrix and K-Nearest Neighbor methods for the classification process. Based on the results of the research analysis that has been carried out, using the parameter k which has a value of 3 obtains very high accuracy.


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Andika, T. H., & Hafiz, A. (2018). Analisis Perbandingan Segmentasi Citra Menggunakan Metode K-Means dan Fuzzy C-Means. Seminar Nasional Teknologi Dan Bisnis 2018, 237–246.

Dittakan, K., Theera-Ampornpunt, N., Witthayarat, W., Hinnoy, S., Klaiwan, S., & Pratheep, T. (2017). Banana Cultivar Classification using Scale Invariant Shape Analysis. Proceeding of 2017 2nd International Conference on Information Technology, INCIT 2017, 1–6.

Febrinanto, F. G., Dewi, C., & Wiratno, A. T. (2018). Implementasi Algoritme K-Means Sebagai Metode Segmentasi Citra Dalam Identifikasi Penyakit Daun Jeruk. Jurnal Pengembangan Teknologi Informasi Dan Ilmu Komputer (J-PTIIK) Universitas Brawijaya, 2(11), 5375–5383.

Jaiswal, P., Jha, S. N., Kaur, P. P., Bhardwaj, R., Singh, A. K., & Wadhawan, V. (2014). Prediction of textural attributes using color values of banana (Musa sapientum) during ripening. Journal of Food Science and Technology, 51(6), 1179–1184.

Kaur, H., Sawhney, B. K., & Jawandha, S. K. (2018). Evaluation of plum fruit maturity by image processing techniques. Journal of Food Science and Technology, 55(8), 3008–3015.

Kipli, K., Zen, H., Sawawi, M., Noor, M. S. M., Julai, N., Junaidi, N., Razali, M. I. S. M., Chin, K. L., & Masra, S. M. W. (2018). Image Processing Mobile Application for Banana Ripeness Evaluation. 2018 International Conference on Computational Approach in Smart Systems Design and Applications, ICASSDA 2018, 1–5.

Marimuthu, S., & Mohamed Mansoor Roomi, S. (2017). Particle Swarm Optimized Fuzzy Model for the Classification of Banana Ripeness. IEEE Sensors Journal, 17(15), 4903–4915.

Mazen, F. M. A., & Nashat, A. A. (2019). Ripeness Classification of Bananas Using an Artificial Neural Network. Arabian Journal for Science and Engineering, 44(8), 6901–6910.

Mendoza, F., & Aguilera, J. M. (2006). Application of Image Analysis for Classification of Ripening Bananas. Journal of Food Science, 69(9), E471–E477.

Mustaffa, I. B., & Khairul, S. F. B. M. (2018). Identification of fruit size and maturity through fruit images using OpenCV-Python and Rasberry Pi. Proceeding of 2017 International Conference on Robotics, Automation and Sciences, ICORAS 2017, 2018-March, 1–3.

Pamungkas, D. P. (2019). Ekstraksi Citra menggunakan Metode GLCM dan KNN untuk Indentifikasi Jenis Anggrek (Orchidaceae). Innovation in Research of Informatics (INNOVATICS), 1(2), 51–56.

Ratnawati, L., & Sulistyaningrum, D. R. (2019). Penerapan Random Forest untuk Mengukur Tingkat Keparahan Penyakit pada Daun Apel. 8(2), A71–A77.

Sabilla, I. A., Wahyuni, C. S., Fatichah, C., & Herumurti, D. (2019). Determining banana types and ripeness from image using machine learning methods. Proceeding - 2019 International Conference of Artificial Intelligence and Information Technology, ICAIIT 2019, 407–412.

Saputro, A. H., Juansyah, S. D., & Handayani, W. (2018). Banana (Musa sp.) maturity prediction system based on chlorophyll content using visible-NIR imaging. 2018 International Conference on Signals and Systems, ICSigSys 2018 - Proceedings, 64–68.

Setiawan, K. N., & Putra, I. M. S. (2018). Klasifikasi Citra Mammogram Menggunakan Metode K-Means, GLCM, dan Support Vector Machine (SVM). Jurnal Ilmiah Merpati (Menara Penelitian Akademika Teknologi Informasi), 6(1), 13–24.

Surya Prabha, D., & Satheesh Kumar, J. (2015). Assessment of banana fruit maturity by image processing technique. Journal of Food Science and Technology, 52(3), 1316–1327.

Suta, I. B. L. M., Hartati, R. S., & Divayana, Y. (2019). Diagnosa Tumor Otak Berdasarkan Citra MRI (Magnetic Resonance Imaging). Majalah Ilmiah Teknologi Elektro, 18(2), 149–154.

Toma, F., Ahmmed, R., Hasan, M., Haque, M., Monju, M., & Surovi, M. (2018). Non-destructive maturity index of “Amritsagor” banana using RGB and HSV values. Journal of the Bangladesh Agricultural University, 16(2), 293–302.

Wan, P., Toudeshki, A., Tan, H., & Ehsani, R. (2018). A methodology for fresh tomato maturity detection using computer vision. Computers and Electronics in Agriculture, 146(February 2017), 43–50.

Wibisono, I. S., & Mujiyono, S. (2018). Segmentasi Fuzzy C-Means Untuk Membantu Identifikasi Kualitas Beras Berdasarkan Nilai Threshold , Warna Dan Ukuran. Multimatrix, I(1), 22–25.



Submitted Date: 2023-01-10
Accepted Date: 2023-01-10
Published Date: 2023-01-15

How to Cite

Ahmad Hafidzul Kahfi, Hasan, M., & Riyan Latifahul Hasanah. (2023). Classification of Banana Ripeness Based on Color and Texture Characteristics. Journal of Computer Networks, Architecture and High Performance Computing, 5(1), 10-17.