International Journal of Image, Graphics and Signal Processing(IJIGSP)

ISSN: 2074-9074 (Print), ISSN: 2074-9082 (Online)

Published By: MECS Press

IJIGSP Vol.11, No.10, Oct. 2019

Fruit Recognition Using Color and Morphological Features Fusion

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Myint San, Mie Mie Aung, Phyu Phyu Khaing

Index Terms

Fruit recognition, feature fusion, color feature, morphological feature


It is still difficult to recognize the kind of fruit which are of different colors, shapes, and textures. This paper proposes a features fusion method to recognize five different classes of fruits that are the images from the fruit360 dataset. We are processed with four stages: pre-processing, boundary extraction, feature extractions, and classification. Pre-processing is performed to remove the noise by using the median filter, and boundary extraction are operated with the morphological operation. In feature extraction, we have extracted two types of features: color, and morphological features of the image. Color features are extracted from the RGB color channel, and morphological features are extracted from the image that detected the boundary of fruit by using morphological operations. These two types of features are combined in a single feature descriptor.  These features are passed to five different classifiers: Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN), and Random Forest (RF). In the study, the accuracy that classified with Random Forest (RF) classifier for the proposed feature fusion method is better than the other classifiers, such as Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbor (KNN).

Cite This Paper

Myint San, Mie Mie Aung, Phyu Phyu Khaing, "Fruit Recognition Using Color and Morphological Features Fusion", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.11, No.10, pp. 8-15, 2019.DOI: 10.5815/ijigsp.2019.10.02


[1]Li, D., Shen, M., Li, D. and Yu, X., 2017, “Green apple recognition method based on the combination of texture and shape features”, In 2017 IEEE International Conference on Mechatronics and Automation (ICMA) (pp. 264-269). IEEE.

[2]Shukla, D. and Desai, A., 2016, “Recognition of fruits using hybrid features and machine learning”, In 2016 International Conference on Computing, Analytics and Security Trends (CAST) (pp. 572-577). IEEE.

[3]Xu, H., Fan, K., Song, S. and Meng, M.Q.H., 2016, “A novel method for recognizing fruits with plastic packing”, In 2016 IEEE International Conference on Information and Automation (ICIA) (pp. 1797-1800). IEEE.

[4]Arivazhagan, S., Shebiah, R.N., Nidhyanandhan, S.S. and Ganesan, L., 2010, “Fruit recognition using color and texture features”, Journal of Emerging Trends in Computing and Information Sciences, 1(2), pp-90-94.

[5]Jana, S., Basak, S. and Parekh, R., 2017, March, “Automatic fruit recognition from natural images using color and texture features”, In 2017 Devices for Integrated Circuit (DevIC) (pp. 620-624), IEEE.

[6]Ilyas, M., Rahman, S.U., Waqas, M. and Alam, F., “A Robust Algorithm for Fruits Recognition System”, Transylvanian Review, 1(1), 2018.

[7]Dubey, S.R. and Jalal, A.S., 2015, “Fruit and vegetable recognition by fusing colour and texture features of the image using machine learning”, International Journal of Applied Pattern Recognition, 2(2), pp.160-181.

[8]Naskar, S. and Bhattacharya, T., 2015, “A fruit recognition technique using multiple features and artificial neural network”, International Journal of Computer Applications, 116(20).

[9]Jana, S. and Parekh, R., 2016, “Intra-class recognition of fruits using color and texture features with neural classifiers”, International Journal of Computer Applications, 148(11), pp.1-6.

[10]Sonka, Milan, Vaclav Hlavac, and Roger Boyle. Image processing, analysis, and machine vision. Cengage Learning, 2014.

[11]R.R Darlin Nisha, V.Gowri,  “A Survey on Image Classification Methods and Techniques for Improving Accuracy”, International Journal of Advanced Research in Computer Engineering & Technology (IJARCET) Volume 3, Issue 1, January 2014 

[12]Himani Sharma1, Sunil Kumar, “A Survey on Decision Tree Algorithms of Classification in Data Mining”, International Journal of Science and Research (IJSR).

[13]“Chapter 9: Decision Trees”, Lior Rokach,Oded Maimon,,, Department of Industrial Engineering, Tel-Aviv University

[14]“An Introduction to Logistic Regression: From Basic Concepts to Interpretation with Particular Attention to Nursing Domain”, Park, Hyeoun-Ae, J Korean Acad Nurs, Vol.43, No.2, April-2013.

[15]Leo Breiman, “Random Forest”, Statistic Department, University of California, Berkeley, CA 94720, January, 2001

[16]Chaitali Dhaware, Mrs. K. H. Wanjale, “Survey On Image Classification Methods In Image Processing”, International Journal of Computer Science Trends and Technology (IJCST) – Volume 4 Issue 3, May - Jun 2016.

[17];k-Nearest Neighbor Classifier.

[18] 62214cea29c7, k Nearest Neighbor algorithm.