Intelligent fruit quality classification system using transfer learning
Abstract
Amidst the burgeoning demands of fruit agriculturists and grading companies for enhanced fruit quality classification, this research presents a cutting-edge approach to binary fruit quality assessment. We built a portable device for exact fruit quality inspection using transfer learning, a deep learning approach, resulting in a decrease in both human and machine labor. The performance of the system is validated and evaluated under real-time situations, with an emphasis on end-user applicability. This paper rigorously validates and assesses the system’s performance in real-world scenarios, with a strong focus on its practicality for end-users. The model is trained on an online picture dataset that is divided into two categories: ‘good’ and ‘poor’ fruits. On dataset 1, our numerical findings show outstanding classification accuracies of 99.49% and 99.75% for the first and second models, respectively. Meanwhile, on dataset 2, the first and second models attain accuracies of 85.43% and 96.75%, respectively, highlighting the efficacy of our technique.
Keywords
Full Text:
PDFReferences
1. Bizvibe. Global Fruit Industry 2020: Fruit Production, Top Fruit Producers, Fruit Exports and Imports (NAICS 1113). Available online: https://www.bizvibe.com/blog/uncategorized/global-fruit-industry-factsheet/ (accessed on 10 August 2023).
2. Jasani D, Patel P, Patel S, et al. Review of Shape and Texture Feature Extraction Techniques for Fruits. International Journal of Computer Science and Information Technologies. 2015, 6(6): 4851–4854.
3. Stajnko D, Rakun J, Blanke M. Modelling apple fruit yield using image analysis for fruit colour, shape and texture. European Journal of Horticultural Science. 2009, 74(6): 260–267.
4. Meruliya T, Dhameliya P, Patel J, et al. Image Processing for Fruit Shape and Texture Feature Extraction - Review. International Journal of Computer Applications. 2015, 129(8): 30-33. doi: 10.5120/ijca2015907000
5. Dubey SR, Jalal AS. Apple disease classification using color, texture and shape features from images. Signal, Image and Video Processing. 2015, 10(5): 819-826. doi: 10.1007/s11760-015-0821-1
6. Tulli S, Yogesh. Application of Machine Learning for Analysis of Fruit Defect: A Review. Computational Intelligence. Published online 2023: 527-537. doi: 10.1007/978-981-19-7346-8_45
7. Gupta R, Kaur M, Garg N, et al. Lemon Diseases Detection and Classification using Hybrid CNN-SVM Model. 2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC). Published online May 26, 2023. doi: 10.1109/icsccc58608.2023.10176828
8. Garg N, Gupta R, Kaur M, et al. Efficient Detection and Classification of Orange Diseases using Hybrid CNN-SVM Model. 2023 International Conference on Disruptive Technologies (ICDT). Published online May 11, 2023. doi: 10.1109/icdt57929.2023.10150721
9. Punam, Goyal R. Analysis of Automatic Plant Disease Classification Using Image Processing Techniques. 2022 2nd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). Published online April 28, 2022. doi: 10.1109/icacite53722.2022.9823862
10. Aggarwal M, Khullar V, Goyal N. Exploring Classification of Rice Leaf Diseases using Machine Learning and Deep Learning. 2023 3rd International Conference on Innovative Practices in Technology and Management (ICIPTM). Published online February 22, 2023. doi: 10.1109/iciptm57143.2023.10117854
11. Aggarwal M, Khullar V, Goyal N, et al. Pre-Trained Deep Neural Network-Based Features Selection Supported Machine Learning for Rice Leaf Disease Classification. Agriculture. 2023, 13(5): 936. doi: 10.3390/agriculture13050936
12. Jadhav RS, Patil SS. A Fruit Quality Management System Based on Image Processing. IOSR Journal of Electronics and Communication Engineering. 2013, 8(6): 01-05. doi: 10.9790/2834-0860105
13. Sahu D, Dewangan C. Identification and Classification of Mango Fruits Using Image Processing. International Journal of Scientific Research in Computer Science, Engineering and Information Technology. 2017, 2(2): 203–210.
14. Jayanthi AN, Nareshkumar C, Rajesh S, et al. Fruit Quality Inspection using Image Processing. Iconic Research and Engineering Journals. 2019, 2(10): 260–263.
15. Chandini AA, Maheswari B. U. Improved Quality Detection Technique for Fruits Using GLCM and MultiClass SVM. 2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI). Published online September 2018. doi: 10.1109/icacci.2018.8554876
16. Sidehabi SW, Suyuti A, Areni IS, et al. Classification on passion fruit’s ripeness using K-means clustering and artificial neural network. 2018 International Conference on Information and Communications Technology (ICOIACT). Published online March 2018. doi: 10.1109/icoiact.2018.8350728
17. Sa I, Ge Z, Dayoub F, et al. DeepFruits: A Fruit Detection System Using Deep Neural Networks. Sensors. 2016, 16(8): 1222. doi: 10.3390/s16081222
18. Hossain MS, Al-Hammadi M, Muhammad G. Automatic Fruit Classification Using Deep Learning for Industrial Applications. IEEE Transactions on Industrial Informatics. 2019, 15(2): 1027-1034. doi: 10.1109/tii.2018.2875149
19. Vasumathi MT, Kamarasan M. Fruit disease prediction using machine learning over big data. International Journal of Recent Technology and Engineering. 2019, 7(6): 556-559.
20. Zhang YD, Dong Z, Chen X, et al. Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation. Multimedia Tools and Applications. 2017, 78(3): 3613-3632. doi: 10.1007/s11042-017-5243-3
21. Xue G, Liu S, Ma Y. A hybrid deep learning-based fruit classification using attention model and convolution autoencoder. Complex & Intelligent Systems. 2020, 9(3): 2209-2219. doi: 10.1007/s40747-020-00192-x
22. Shaikh H, Wagh Y, Shinde S, Patil SM. Classification of Affected Fruits using Machine Learning. International Journal of Engineering Research & Technology (IJERT). 2021, 9(3): 519-524.
23. Xiang Q, Wang X, Li R, et al. Fruit Image Classification Based on MobileNetV2 with Transfer Learning Technique. Proceedings of the 3rd International Conference on Computer Science and Application Engineering. Published online October 22, 2019. doi: 10.1145/3331453.3361658
24. Siddiqi R. Effectiveness of Transfer Learning and Fine Tuning in Automated Fruit Image Classification. Proceedings of the 2019 3rd International Conference on Deep Learning Technologies. Published online July 5, 2019. doi: 10.1145/3342999.3343002
25. Dhiman P. Contemporary Study on Citrus Disease Classification System. ECS Transactions. 2022, 107(1): 10035-10043. doi: 10.1149/10701.10035ecst
26. Dhiman P, Kukreja V, Manoharan P, Kaur A, Kamruzzaman MM, Dhaou IB, Iwendi C. A Novel Deep Learning Model for Detection of Severity Level of the Disease in Citrus Fruits. Electronics. 2022; 11(3):495. doi: 10.3390/electronics11030495.
27. Gautam V, Tiwari RG, Misra A, et al. Dry Fruit Classification Using Deep Convolutional Neural Network Trained with Transfer Learning. 2023 International Conference on Advancement in Data Science, E-learning and Information System (ICADEIS). Published online August 2, 2023. doi: 10.1109/icadeis58666.2023.10270982
28. Dhiman P, Kaur A, Balasaraswathi VR, et al. Image Acquisition, Preprocessing and Classification of Citrus Fruit Diseases: A Systematic Literature Review. Sustainability. 2023, 15(12): 9643. doi: 10.3390/su15129643
29. Dhiman P, Manoharan P, Lilhore UK, et al. PFDI: a precise fruit disease identification model based on context data fusion with faster-CNN in edge computing environment. EURASIP Journal on Advances in Signal Processing. 2023, 72. doi: 10.1186/s13634-023-01025-y.
DOI: https://doi.org/10.32629/jai.v7i4.1424
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Vikas Khullar, Mohit Angurala, Abhineet Anand, Jagdish Chandra Patni, Harjit Pal Singh
License URL: https://creativecommons.org/licenses/by-nc/4.0/