Abstract
Advancement in image processing techniques and automation in industrial sector urge its usage in almost all the fields. Fruit classification and grading with its image still remain a challenging task. Fruit classification can be used to perform the sorting and grading process automatically. A traditional method for fruits classification is manual sorting which is time consuming and involves human presence always. Automated sorting process can be used to implement Smart Fresh Park. In this paper, various methods used for fruit classification have experimented. Different fruits considered for classification are five categories of apple, banana, orange and pomegranate. Results were compared by applying the fruit-360 dataset between typical machine learning and deep learning algorithms. To apply machine learning algorithms, basic features of the fruit like the color (RGB Color space), size, height and width were extracted from its image. Traditional machine learning algorithms KNN and SVM were applied over the extracted features. The result shows that using Convolutional Neural Network (CNN) gives a promising result than traditional machine learning algorithms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Kamilaris, A., Prenafeta-boldú, F.X.: Deep learning in agriculture: a survey. Comput. Electron. Agric. 147, 70–90 (2018)
Dimatira, J.B.U., Dadios, E.P., Culibrina, F., et al.: Application of fuzzy logic in recognition of tomato fruit maturity in smart farming. In: IEEE Region 10 Annual International Conference, Proceedings/TENCON, pp. 2031–2035 (2017)
Zhang, Y., Wang, S., Ji, G., Phillips, P.: Fruit classification using computer vision and feedforward neural network. J. Food Eng. 143, 167–177 (2014)
Zhang, Y., Wu, L.: Classification of fruits using computer vision and a multiclass support vector machine. Sensors 12, 12489–12505 (2012)
Srinivasan, K., Porkumaran, K., Sainarayanan, G.: A new approach for human activity analysis through identification of body parts using skin colour segmentation. Int. J. Signal Imaging Syst. Eng. 3(2), 93–104 (2010)
Srinivasan, K., Porkumaran, K., Sainarayanan, G.: Background subtraction techniques for human body segmentation in indoor video surveillance. J. Sci. Ind. Res. 73, 342–345 (2014)
Srinivasan, K., Porkumaran, K., Sainarayanan, G.: Enhanced background subtraction techniques for monocular video applications. Int. J. Image Process. Appl. 1, 87–93 (2010)
Jana, S., Parekh, R.: Shape-based fruit recognition and classification, pp. 184–196. Springer (2017)
Karis, M.S., Hidayat, W., Saad, M., et al.: Fruit sorting based on machine vision technique. J. Telecommun. Electron. Comput. Eng. 8(4), 31–35 (2016)
Moallem, P., Serajoddin, A., Pourghassem, H.: Computer vision-based apple grading for golden delicious apples based on surface features. Inf. Process. Agric. 4(1), 33–40 (2017)
Mahendran, R., Gc, J., Alagusundaram, K.: Application of computer vision technique on sorting and grading of fruits and vegetables. J. Food Process. Technol. 10, 2157–7110 (2012)
Zeng, G.: Fruit and vegetables classification system using image saliency and convolutional neural network. In: IEEE Conference, pp. 613–617 (2017)
Hou, L., Wu, Q.: Fruit recognition based on convolution neural network. In: International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, pp. 18–22 (2016)
Khaing, Z.M., Naung, Y., Htut, P.H.: Development of control system for fruit classification based on convolutional neural network. In: IEEE Conference, pp. 1805–1807 (2018)
Ma, L.: Deep learning ımplementation using convolutional neural network in mangosteen surface defect detection. In: IEEE International Conference on Control System, Computing and Engineering (ICCSCE 2017), Penang, Malaysia, pp. 24–26 (2017)
Tahir, M.W., Zaidi, N.A., Rao, A.A., Blank, R., Vellekoop, M.J., Lang, W.: A fungus spores dataset and a convolutional neural networks based approach for fungus detection. IEEE Trans. Nanobiosci. 17, 281–290 (2018)
Acknowledgements
This research work was supported and carried out at the department of Information Technology, Sri Ramakrishna Engineering College, Coimbatore. We would like to thank our Management, Director (Academics), Principal and Head of the Department for supporting us with the infrastructure and learning resource to carry out the research work.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
✓ All authors declare that there is no conflict of interest
✓ No humans/animals involved in this research work.
✓ We have used our own data.
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Saranya, N., Srinivasan, K., Pravin Kumar, S.K., Rukkumani, V., Ramya, R. (2020). Fruit Classification Using Traditional Machine Learning and Deep Learning Approach. In: Smys, S., Tavares, J., Balas, V., Iliyasu, A. (eds) Computational Vision and Bio-Inspired Computing. ICCVBIC 2019. Advances in Intelligent Systems and Computing, vol 1108. Springer, Cham. https://doi.org/10.1007/978-3-030-37218-7_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-37218-7_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37217-0
Online ISBN: 978-3-030-37218-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)