A new method for classifying fruits using image processing technique is proposed in this paper. The data set used had 70 apple images and 70 banana images for training and 25 images of apple and 25 images of bananas for testing. RGB image was first converted to HSI image. Then by using Otsu’s thresholding method region of interest was segmented by taking into account only the HUE component image of the HSI image. Later, after background subtraction, a total of 36 statistical and texture features were extracted with the help of the coefficients obtained by applying wavelet transformation on the segmented image using Haar filter. Extracted features were given as inputs to a SVM classifier to classify the test images as apples and bananas. As KNN classification method did not give 100% accuracy while classification SVM classification method was used. 140 sample images of apples and bananas were used for training and 25 images of banana and 25 images of apples were used for testing the proposed algorithm. The proposed algorithm gave 100% accuracy rate.

RGB, HSI, Region of interest, Wavelet domain, Haar filter, SVM classification

[1] Sahu, Dameshwari, and Chitesh Dewangan. "Identification and Classification of Mango Fruits Using Image Processing." Int. J. Sci. Res. Comput. Sci. Eng. Inf. Technol 2.2 (2017): 203-210.
[2] Tarale, Ketki, and Anil Bavaskar. "Fruit Detection Using Image Processing Technique." National Conference on Advances in Engineering and Applied Science (NCAEAS). 2017,(3)2: 178-183
[3] Seng, Woo Chaw, and Seyed Hadi Mirisaee. "A new method for fruits recognition system." Electrical Engineering and Informatics 2009. ICEEI`09. International Conference on. Vol. 1. IEEE, 2009.
[4] Prabha, D. Surya, and J. Satheesh Kumar. "A study on image processing methods for fruit classification." Proc. Int. Conf. on Computational Intelligence and Information Technology, CIIT 2012.
[5] Zawbaa, Hossam M., Maryam Hazman, Mona Abbass, and Aboul Ella Hassanien. "Automatic fruit classification using random forest algorithm." In Hybrid Intelligent Systems (HIS), 2014 14th International Conference on, pp. 164-168. IEEE, 2014.
[6] Kumar, C., Chauhan, S., & Alla, R. N. (2015, April). “Classifications of citrus fruit using image processing-GLCM parameters”. In Communications and Signal Processing (ICCSP), 2015 International Conference on (pp. 1743-1747). IEEE.
[7] Zawbaa, Hossam M., Mona Abbass, Maryam Hazman, and Aboul Ella Hassenian. "Automatic fruit image recognition system based on shape and color features." In International Conference on Advanced Machine Learning Technologies and Applications, pp. 278-290 Springer, Cham, 2014.
[8] Dubey, Shiv Ram, and A. S. Jalal. "Robust approach for fruit and vegetable classification." Procedia Engineering 38 (2012): 3449-3453.
[9] Gongal, A., M. Karkee, and S. Amatya. "Apple fruit size estimation using a 3D machine vision system." Information Processing in Agriculture 5, no. 4 (2018): 498-503.
[10] Wang, Weizu, Zhou Yang, Huazhong Lu, and Han Fu. "Mechanical damage caused by fruit-to-fruit impact of litchis." IFAC- PapersOnLine 51, no. 17 (2018): 532-535.
[11] Momin, M. A., et al. "Geometry-based mass grading of mango fruits using image processing." Information Processing in Agriculture, 4.2 (2017): 150-160.