Tomato Maturity Classification using Naive Bayes Algorithm and Histogram Feature Extraction
DOI:
https://doi.org/10.33633/jais.v3i1.1988Abstract
Tomatoes have nutritional content that is very beneficial for human health and is one source of vitamins and minerals. Tomato classification plays an important role in many ways related to the distribution and sales of tomatoes. Classification can be done on images by extracting features and then classifying them with certain methods. This research proposes a classification technique using feature histogram extraction and Naïve Bayes Classifier. Histogram feature extractions are widely used and play a role in the classification results. Naïve Bayes is proposed because it has high accuracy and high computational speed when applied to a large number of databases, is robust to isolated noise points, and only requires small training data to estimate the parameters needed for classification. The proposed classification is divided into three classes, namely raw, mature and rotten. Based on the results of the experiment using 75 training data and 25 testing data obtained 76% accuracyReferences
A. F. Smith, The Tomato in America, University of Illinois Press, 1994.
S. R. Rupanagudi, B. Ranjani, P. Nagaraj and V. G. Bhat, "A cost effective tomato maturity grading system using image processing for farmers," in International Conference on Contemporary Computing and Informatics (IC3I), Mysore, 2014.
V. Pavithra, R. Pounroja and B. S. Bama, "Machine vision based automatic sorting of cherry tomatoes," in International Conference on Electronics and Communication Systems (ICECS), Coimbatore, 2015.
O. R. Indriani, E. J. Kusuma, C. A. Sari, E. H. Rachmawanto and D. R. I. M. Setiadi, "Tomatoes classification using K-NN based on GLCM and HSV color space," in International Conference on Innovative and Creative Information Technology (ICITech), Salatiga, 2017.
T. Sutojo, P. S. Tirajani, D. R. I. M. Setiadi, C. A. Sari and E. H. Rachmawanto, "CBIR for classification of cow types using GLCM and color features extraction," in International conferences on Information Technology, Information Systems and Electrical Engineering (ICITISEE), Yogyakarta, 2017.
W. Man, Y. Ji and Z. Zhang, "Image classification based on improved random forest algorithm," in International Conference on Cloud Computing and Big Data Analysis (ICCCBDA), Chengdu, 2018.
A. Krishna, D. Edwin and S. Hariharan, "Classification of liver tumor using SFTA based Naïve Bayes classifier and support vector machine," in International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT), Kannur, 2017.
G. Karthick and R. Harikumar, "Comparative performance analysis of Naive Bayes and SVM classifier for oral X-ray images," in International Conference on Electronics and Communication Systems (ICECS), Coimbatore, 2017.
X. Deng, J. Guo, Y. Chen and X. Liu, "A Method for Detecting Document Orientation by Using NaÃve Bayes Classifier," in International Conference on Industrial Control and Electronics Engineering, Xi'an, 2012.
M. A. Jabbar and S. Samreen, "Heart disease prediction system based on hidden naïve bayes classifier," in International Conference on Circuits, Controls, Communications and Computing (I4C), Bangalore, 2016.
K. Baati, T. M. Hamdani and A. M. Alimi, "Diagnosis of Lymphatic Diseases Using a Naive Bayes Style Possibilistic Classifier," in IEEE International Conference on Systems, Man, and Cybernetics, Manchester, 2013.
J. Polpinij and C. Sibunruang, "Thai heritage images classification by Naïve Bayes image classifier," in International Conference on Digital Content, Multimedia Technology and its Applications, Seoul, 2010.
S.-C. Hsu, I.-C. Chen and C.-L. Huang, "Image classification using pairwise local observations based Naive Bayes classifier," in Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), Hong Kong, 2015.
A. Z. Ichsan, A. and D. Yendri, "Perancangan dan Pembuatan Sistem Visual Inspection Sebagai Seleksi Buah Tomat Berdasarkan Kematangan Berbasis Web Camera," p. 9, 2013.
F. Yan, M. Hamit, A. Kutluk, C. Yan, L. Li, W. Yuan and D. Kong, "Feature extraction and analysis on X-ray image of Xinjiang Kazak Esophageal cancer by using gray-level histograms," in IEEE International Conference on Medical Imaging Physics and Engineering, Shenyang, 2013.
A. Ciputra, D. R. I. M. Setiadi, E. H. Rachmawanto and A. Susanto, "Klasifikasi Tingkat Kematangan Buah Apel Manalagi dengan Algoritma Naive Bayes dan Ekstraksi Fitur Citra Digital," Simetris, vol. 9, no. 1, pp. 465-471, 2018.
F. Y. Manik and K. S. Saragih, "Klasifikasi Belimbing Menggunakan Naive Bayes Berdasarkan Fitur Warna RGB," Indonesian Journal of Computing and Cybernetics Systems, vol. 11, no. 1, p. 10, 2017.
A. Ghofur, "Implementasi Metode Klasifikasi Naive Bayes Untuk Memprediksi Kualitas Cabai," Jurnal Ilmiah Informatika, vol. 1, no. 1, p. 7, 2016.
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