Application of K-NN algorithm using gray level co-occurrence matrix for mango fruit classification cased on leaf image
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Published:
Jun 30, 2024
Keywords:
Circularity, Compactness, Image Processing, K-Nearest Neighbor, Mango
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Abstract
Mango is a fruit crop favored by the community, especially the people of Probolinggo. The most widely planted types of mangoes in the Probolinggo area are Saruman is, golek, and manalagi mangoes because they taste good. This study uses mango leaves as a dataset of three types of mangoes: arumanis, golek, and manalagi. Various ways can be done to distinguish mango types, one of which is by looking at the shape and texture of the mango tree leaves. Suppose you look at the data in the field. In that case, the shape and texture of the leaves of Saruman, golek, and manalagi mangoes have many similarities, making it difficult to distinguish with the naked eye. This research aims to classify mango types based on leaf shape and texture using the K-Nearest Neighbor method. The shape feature extraction process uses compactness and circularity methods, while the texture feature extraction process uses energy and contrast from the co-occurrence matrix approach. The classification method used is K-Nearest Neighbor. The test results of shape feature extraction took 0.043 seconds and texture 0.053 seconds
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Nugroho, B. I. ., Aziz, T., Santoso, N. A. ., & Gunawan, G. . (2024). Application of K-NN algorithm using gray level co-occurrence matrix for mango fruit classification cased on leaf image. Journal of Intelligent Decision Support System (IDSS), 7(2), 93-101. https://doi.org/10.35335/idss.v7i2.233
References
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Tatrin Kurniati, F., Sembiring, I., Setiawan, A., Setyawan, I., Rudolf Huizen, R., Raya Puputan No, J., & Bali, D. (2023). GLCM-Based Feature Combination for Extraction Model Optimization in Object Detection Using Machine Learning. Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika (JITEKI), 9(4), 1196–1205. https://doi.org/10.26555/jiteki.v9i4.27842
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Thaseentaj, S., & Sudhakar Ilango, S. (2023). Deep Convolutional Neural Networks for South Indian Mango Leaf Disease Detection and Classification. Computers, Materials and Continua, 77(3), 3593–3618. https://doi.org/10.32604/cmc.2023.042496
Ullah, M. A., Kiloes, A. M., Aziz, A. A., & Joyce, D. C. (2024). Impact of factors contributing to internal disorders of mango (Mangifera indica L.) fruit—A systematic literature review. In Scientia Horticulturae (Vol. 331). Elsevier B.V. https://doi.org/10.1016/j.scienta.2024.113150
Zhang, W., Chen, X., Liu, Y., & Xi, Q. (2020). A Distributed Storage and Computation k-Nearest Neighbor Algorithm Based Cloud-Edge Computing for Cyber-Physical-Social Systems. IEEE Access, 8, 50118–50130. https://doi.org/10.1109/ACCESS.2020.2974764
Aslam, T., Qadri, S., Qadri, S. F., Nawaz, S. A., Razzaq, A., Zarren, S. S., Ahmad, M., Ur Rehman, M., Hussain, A., Hussain, I., Jabeen, J., & Altaf, A. (2022). Machine learning approach for classification of mangifera indica leaves using digital image analysis. International Journal of Food Properties, 25(1), 1987–1999. https://doi.org/10.1080/10942912.2022.2117822
Bhatt, D., Patel, C., Talsania, H., Patel, J., Vaghela, R., Pandya, S., Modi, K., & Ghayvat, H. (2021). Cnn variants for computer vision: History, architecture, application, challenges and future scope. In Electronics (Switzerland) (Vol. 10, Issue 20). MDPI. https://doi.org/10.3390/electronics10202470
Bohnsack, K., Engelsberger, A., Kaden, M., & Villmann, T. (2023). Efficient Representation of Biochemical Structures for Supervised and Unsupervised Machine Learning Models Using Multi-Sensoric Embeddings. 59–69. https://doi.org/10.5220/0011644000003414
Cruciani, F., Vafeiadis, A., Nugent, C., Cleland, I., McCullagh, P., Votis, K., Giakoumis, D., Tzovaras, D., Chen, L., & Hamzaoui, R. (2020). Feature learning for Human Activity Recognition using Convolutional Neural Networks: A case study for Inertial Measurement Unit and audio data. CCF Transactions on Pervasive Computing and Interaction, 2(1), 18–32. https://doi.org/10.1007/s42486-020-00026-2
Dheepak, G., J, A. C., & Vaishali, D. (2023). Brain tumor classification: a novel approach integrating GLCM, LBP and composite features. Frontiers in Oncology, 13. https://doi.org/10.3389/fonc.2023.1248452
Kaden, M., Bohnsack, K. S., Weber, M., Kudła, M., Gutowska, K., Blazewicz, J., & Villmann, T. (2022). Learning vector quantization as an interpretable classifier for the detection of SARS-CoV-2 types based on their RNA sequences. Neural Computing and Applications, 34(1), 67–78. https://doi.org/10.1007/s00521-021-06018-2
Kim, J. H., Choi, J. H., Park, Y. H., Leung, C. K. S., & Nasridinov, A. (2021). Knn-sc: Novel spectral clustering algorithm using k-nearest neighbors. IEEE Access, 9, 152616–152627. https://doi.org/10.1109/ACCESS.2021.3126854
Le, T. D., Viet Nguyen, T., Muoi, N. Van, Toan, H. T., Lan, N. M., & Pham, T. N. (2022). Supply Chain Management of Mango (Mangifera indica L.) Fruit: A Review With a Focus on Product Quality During Postharvest. Frontiers in Sustainable Food Systems, 5. https://doi.org/10.3389/fsufs.2021.799431
Lebaka, V. R., Wee, Y. J., Ye, W., & Korivi, M. (2021). Nutritional composition and bioactive compounds in three different parts of mango fruit. In International Journal of Environmental Research and Public Health (Vol. 18, Issue 2, pp. 1–20). MDPI AG. https://doi.org/10.3390/ijerph18020741
Mandha, J., Shumoy, H., Devaere, J., Schouteten, J. J., Gellynck, X., De Winne, A., Matemu, A. O., & Raes, K. (2022). Effect of Lactic Acid Fermentation on Volatile Compounds and Sensory Characteristics of Mango (Mangifera indica) Juices. Foods, 11(3). https://doi.org/10.3390/foods11030383
Manome, N., Shinohara, S., Takahashi, T., Chen, Y., & Chung, U. il. (2021). Self-incremental learning vector quantization with human cognitive biases. Scientific Reports, 11(1). https://doi.org/10.1038/s41598-021-83182-4
Mo, H., & Zhao, G. (2024). RIC-CNN: Rotation-Invariant Coordinate Convolutional Neural Network. Pattern Recognition, 146. https://doi.org/10.1016/j.patcog.2023.109994
Mohapatra, M., Parida, A. K., Mallick, P. K., Zymbler, M., & Kumar, S. (2022). Botanical Leaf Disease Detection and Classification Using Convolutional Neural Network: A Hybrid Metaheuristic Enabled Approach. Computers, 11(5). https://doi.org/10.3390/computers11050082
Nihar, F., Khanom, N. N., Hassan, S. S., & Das, A. K. (2021). Plant Disease Detection through the Implementation of Diversified and Modified Neural Network Algorithms. Journal of Engineering Advancements, 2(01), 48–57. https://doi.org/10.38032/jea.2021.01.007
Pradipkumar Vaghela, H., Alagumalai, R., & Raja, A. (2021). Comparison of Support Vector Machine and k-Nearest Neighbor Classifiers for Tree Species Identification. In Indian Journal of Radio & Space Physics (Vol. 50).
Saleem, R., Shah, J. H., Sharif, M., & Ansari, G. J. (2021). Mango Leaf Disease Identification Using Fully Resolution Convolutional Network. Computers, Materials and Continua, 69(3), 3581–3601. https://doi.org/10.32604/cmc.2021.017700
Shaban, W. M., Rabie, A. H., Saleh, A. I., & Abo-Elsoud, M. A. (2020). A new COVID-19 Patients Detection Strategy (CPDS) based on hybrid feature selection and enhanced KNN classifier. Knowledge-Based Systems, 205. https://doi.org/10.1016/j.knosys.2020.106270
Singh, H., Sharma, V., & Singh, D. (2022). Comparative analysis of proficiencies of various textures and geometric features in breast mass classification using k-nearest neighbor. Visual Computing for Industry, Biomedicine, and Art, 5(1). https://doi.org/10.1186/s42492-021-00100-1
Sun, B., & Chen, H. (2021). A Survey of k Nearest Neighbor Algorithms for Solving the Class Imbalanced Problem. In Wireless Communications and Mobile Computing (Vol. 2021). Hindawi Limited. https://doi.org/10.1155/2021/5520990
Tatrin Kurniati, F., Sembiring, I., Setiawan, A., Setyawan, I., Rudolf Huizen, R., Raya Puputan No, J., & Bali, D. (2023). GLCM-Based Feature Combination for Extraction Model Optimization in Object Detection Using Machine Learning. Jurnal Ilmiah Teknik Elektro Komputer Dan Informatika (JITEKI), 9(4), 1196–1205. https://doi.org/10.26555/jiteki.v9i4.27842
Taye, M. M. (2023). Theoretical Understanding of Convolutional Neural Network: Concepts, Architectures, Applications, Future Directions. In Computation (Vol. 11, Issue 3). MDPI. https://doi.org/10.3390/computation11030052
Thaseentaj, S., & Sudhakar Ilango, S. (2023). Deep Convolutional Neural Networks for South Indian Mango Leaf Disease Detection and Classification. Computers, Materials and Continua, 77(3), 3593–3618. https://doi.org/10.32604/cmc.2023.042496
Ullah, M. A., Kiloes, A. M., Aziz, A. A., & Joyce, D. C. (2024). Impact of factors contributing to internal disorders of mango (Mangifera indica L.) fruit—A systematic literature review. In Scientia Horticulturae (Vol. 331). Elsevier B.V. https://doi.org/10.1016/j.scienta.2024.113150
Zhang, W., Chen, X., Liu, Y., & Xi, Q. (2020). A Distributed Storage and Computation k-Nearest Neighbor Algorithm Based Cloud-Edge Computing for Cyber-Physical-Social Systems. IEEE Access, 8, 50118–50130. https://doi.org/10.1109/ACCESS.2020.2974764
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