Abstract
During transportation, many defects appear on the surface of fruits due to multiple reasons such as negligence in packing methods, not maintaining the required temperature, and mixing rotten fruits with fresh fruits. This results in the quality of fruits getting degraded and the suppliers facing losses. In this research, a model is being created to detect defects in fruits. Convolutional Neural Network (CNN) is used because of its ability to learn from images, create patterns, then use it to train itself and predict results for fruit from its image. Our database consisted of 8 fruit images which are self-collated from google images and kaggle. Accuracy for each of the fruit classifier is more than 95%.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ministry of Statistics and Programme Implementation, “Sector-wise GDP of India,” Statistics Times (2021). https://statisticstimes.com/economy/country/india-gdp-sectorwise.php
Altisent, M.R.: Damage mechanisms in the handling of fruits. Handl. Fruits 1, 231–257 (1991)
“Fresh Fruits & Vegetables,” Agricultural & Processed Food Products Export Development Authority. http://apeda.gov.in/apedawebsite/six_head_product/FFV.htm. The area under cultivation of, %2C brinjal%2C Cabbages%2C etc.
Sharma, S.: India wastes up to 16% of its agricultural produce; fruits, vegetables squandered the most. Finan. Express-Read to Lead (2019). https://www.financialexpress.com/economy/india-wastes-up-to-16-of-its-agricultural-produce-fruits-vegetables-squandered-the-most/1661671/
Nakano, K.: Application of neural networks to the color grading of apples. Comput. Electron. Agric. 18(2–3), 105–116 (1997). https://doi.org/10.1016/s0168-1699(97)00023-9
Lu, S., Lu, Z., Aok, S., Graham, L.: Fruit classification based on six layer convolutional neural network. In: International Conference Digital Signal Processing (2018). https://doi.org/10.1049/joe.2019.0422
Kausar, A., Sharif, M., Park, J., Shin, D.R.: Pure-CNN: a framework for fruit images classification. In: Proceedings of the 2018 International Conference on Computational Science and Computational Intelligence, CSCI 2018, pp. 404–408 (2018). https://doi.org/10.1109/CSCI46756.2018.00082
Dandavate, R., Patodkar, V.: CNN and data augmentation based fruit classification model. In: Proceedings of the 4th International Conference on IoT Social, Mobile, Analytics and Cloud, ISMAC 2020, pp. 784–787 (2020). https://doi.org/10.1109/I-SMAC49090.2020.9243440
Chakraborty, S., Shamrat, F.M.J.M., Billah, M.M., Al Jubair, M., Alauddin, M., Ranjan, R.: Implementation of deep learning methods to identify rotten fruits, pp. 1207–1212 (2021). https://doi.org/10.1109/icoei51242.2021.9453004
Leemans, V., Destain, M.F.: A real-time grading method of apples based on features extracted from defects. J. Food Eng. 61(1), 83–89 (2004). https://doi.org/10.1016/S0260-8774(03)00189-4
Potdar, R.R., Shrivastava, A., Sohandani, R., Khatwani, N.: Fresh and Stale Images of Fruits and Vegetables. Kaggle. https://www.kaggle.com/raghavrpotdar/fresh-and-stale-images-of-fruits-and-vegetables
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Bhardwaj, A., Hasteer, N., Kumar, Y., Yogesh (2022). Deep Learning Based Fruit Defect Detection System. In: Mekhilef, S., Shaw, R.N., Siano, P. (eds) Innovations in Electrical and Electronic Engineering. ICEEE 2022. Lecture Notes in Electrical Engineering, vol 894. Springer, Singapore. https://doi.org/10.1007/978-981-19-1677-9_28
Download citation
DOI: https://doi.org/10.1007/978-981-19-1677-9_28
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-1676-2
Online ISBN: 978-981-19-1677-9
eBook Packages: EnergyEnergy (R0)