Abstract
In recent days, deep learning has been considered as the state-of-the-art computer vision technique for image classification task. The introduction of Convolutional Neural Network (CNN) made the feature engineering task simple. The classification of various stages of maturity of a fruit is a challenging task using machine learning techniques as it is hard to differentiate the visual feature of the fruits at different maturity stages. In this proposed work, four different ripeness stage of banana were classified using proposed CNN model and compared with the state-of-the-art CNN model using transfer learning. Classification using CNN model requires a huge number of training images to achieve better classification result. The proposed CNN model was trained and tested with both original and augmented images. The CNN model was trained with overall validation accuracy of 96.14%.
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Dataset available at https://drive.google.com/drive/folders/1nRWBYAHNRqmL4R0SLrs6dbGQFSWGVY8V.
References
Adebayo SE, Hashim N, Abdan K, Hanafi M, Zude-Sasse M (2017a) Prediction of banana quality attributes and ripeness classification using artificial neural network. Acta Hort 1152:335–343
Adebayo SE, Hashim N, Abdan K, Hanafi M, Zude-Sasse M (2017b) Banana quality attribute prediction and ripeness classification using support vector machine. ETP Int J Food Eng 3(1):42–47
Athiraja A, Vijayakumar P (2020) Banana disease diagnosis using computer vision and machine learning methods. J Ambient Intell Hum Comput 2016
Behera SK, Rath AK, Mahapatra A, Sethy PK (2020) Identification, classification and grading of fruits using machine learning and computer intelligence: a review. J Ambient Intell Hum Comput Kondo 2010
Bindu H, Bhuvaneshwari G, Jagadeesh SL, Ganiger VM (2019) Evaluation of physical and functional properties of weaning food blended with banana, sweet potato and drumstick leaves powder 8(2): 1568–1573
Cubero S, Lee WS, Aleixos N, Albert F, Blasco J (2016) Automated systems based on machine vision for inspecting citrus fruits from the field to postharvest—a review. Food Bioprocess Technol 9(10):1623–1639
El-Bendary N, El Hariri E, Hassanien AE, Badr A (2015) Using machine learning techniques for evaluating tomato ripeness. Expert Syst Appl 42(4):1892–1905
Kamilaris A, Prenafeta-boldú FX (2018) Deep learning in agriculture: a survey. Elsevier Comput Electron Agric vol 147(February): 70–90
Li H, Lee WS, Wang K (2016) Immature green citrus fruit detection and counting based on fast normalized cross correlation (FNCC) using natural outdoor colour images. Precis Agric 17(6):678–697
Mazen FMA, Nashat AA (2019) Ripeness Classification of Bananas Using an Artificial Neural Network. Arab J Sci Eng 44(8):6901–6910
Mureşan H, Oltean M (2018) Fruit recognition from images using deep learning. Acta Univ Sapient Inform 10(1):26–42
Nasiri A, Taheri-Garavand A, Zhang YD (2019) Image-based deep learning automated sorting of date fruit. Postharvest Biol Technol 153(January):133–141
Perez L, Wang J.(2017) The effectiveness of data augmentation in image classification using deep learning. http://arxiv.org/abs/1712.04621
Piedad E, Larada JI, Pojas GJ, Ferrer LVV (2018) Postharvest classification of banana (Musa acuminata) using tier-based machine learning. Postharvest Biol Technol 145(June):93–100
Sa I, Ge Z, Dayoub F, Upcroft B, Perez T, McCool C (2016) Deepfruits: a fruit detection system using deep neural networks. Sens (Switzerl) 16(8)
Shamim HM, Al-Hammadi M, Muhammad G (2019) Automatic fruit classification using deep learning for industrial applications. IEEE Trans Ind Inf 15(2):1027–1034
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition: 1–14. http://arxiv.org/abs/1409.1556
Singh R, Gosewade S, Ravinder Singh C, Kaushik R (2018) Bananas as underutilized fruit having huge potential as raw materials for food and non-food processing industries: a brief review. Pharma Innov J 7(6): 574–580 www.thepharmajournal.com
Thor N (2017) Applying machine learning clustering and classification to predict banana ripeness states and shelf life. Cloud Publi Int J Adv Food Sci Technol 2(1):20–25
Zhang L, Jia J, Gui G, Hao X, Gao W, Wang M (2018a) Deep learning based improved classification system for designing tomato harvesting robot. IEEE Access 6:67940–67950
Zhang Y, Lian J, Fan M, Zheng Y (2018) Deep indicator for fine-grained classification of banana’s ripening stages. Eur J Image Video Process 2018(1)
Acknowledgement
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, Principal and Head of the Department for supporting us with the infrastructure and learning resource to carry out the research work.
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Saranya, N., Srinivasan, K. & Kumar, S.K.P. Banana ripeness stage identification: a deep learning approach. J Ambient Intell Human Comput 13, 4033–4039 (2022). https://doi.org/10.1007/s12652-021-03267-w
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DOI: https://doi.org/10.1007/s12652-021-03267-w