Open Access
Subscription Access
Open Access
Subscription Access
Classification of Spices using Machine Learning Techniques
Subscribe/Renew Journal
Machine learning (ML) has played a significant role in pattern recognition including fruits and vegetables classification. In this paper, comparative analysis of various ML techniques have been carried out for the identification of Spices. For the current work, ML techniques namely Naïve Bayes (NB), Decision Tree (DT), K-Nearest Neighbor (KNN), Random Forest (RF) and Support Vector Machine (SVM) have been undertaken. The main aim of the current study is to find out the most appropriate ML approach for Spices recognition. The experimental study has been performed on primary dataset of Spices. This dataset consists of 1000 images of five different Spices including clove, green cardamom, cinnamon, black pepper and curry leaf. The performance of the ML techniques have been analyzed on the basis of four parameters i.e. accuracy, precision, recall and f1-score. Out of five implemented ML models, best performance has been predicted by SVM approach with accuracy of 94.5%, precision of 95%, and recall of 94% with f1-score of 0.95..
Keywords
Decision Tree, K-Nearest Neighbor, Machine Learning, Spices Recognition, Support Vector Machine
Subscription
Login to verify subscription
User
Font Size
Information
- K. Ponmozhi, and R. Jose, “A review on spices classification and recognition,” International Journal of Mathematics and Computer, vol. 12, no. 1, pp. 25-32, 2022.
- A. Jiang, “Health benefits of culinary herbs and spices,” Journal of AOAC International, vol. 102, no. 2, pp. 395-411, 2019, doi: 10.5740/jaoacint.18-0418.
- Machine Learning. [Online]. Available: https://en.m.wikipedia.org/wiki/Machine_learning#:~:text =Machine%20learning%20(ML)%20is%20the,a%20part%20of%20artificial%20intelligence
- Y.-D. Zhang, and L. Wu, “Classification of fruits using computer vision and a multiclass support vector machine,” Sensors, vol. 12, no. 9, pp. 12489-12505, 2012, doi: 10.3390/s120912489.
- V. Anuradha, A. Praveena, and K. Sanjayan, “Nutritive analysis of fresh and dry fruits of Morinda tinctoria,” International Journal of Current Microbiology and Applied Sciences, vol. 2, no. 3, pp. 65-74, 2013.
- R. Surya, and S. S. Priya, “Food image recognition using SVM classifier for measuring calorie and nutrition values,” International Journal of Scientific & Engineering Research, vol. 6, no. 4, pp. 324-328, 2015.
- F. Femling, A. Olsson, and F. Fernandez, “Fruit and vegetable identification using machine learning for retail applications,” International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 2018, doi: 10.1109/SITIS.2018.00013.
- I. M. Dheir, A. S. A. Mettleq, and A. A. Elsharif, “Nuts types classification using deep learning,” International Journal of Academic Information Systems Research (IJAISR), vol. 3, no. 12, pp. 12-17, 2019.
- S. Bhattacharya, and J. Mukherjee, “Recognisation of Indian spices based on the combination of features and comparison using neural network,” International Research Journal of Engineering and Technology (IRJET), vol. 7, no. 6, pp. 2418-2423, 2020.
- J. Aranda, J. Varela, J. Tello, and G. Ramirez, “Fruit classification for retail stores using deep learning,” in K. F. Mora, J. A. Marín, J. Cerda, J. Carrasco-Ochoa, J. Martínez-Trinidad, and J. Olvera-López (Eds.) Pattern Recognition, MCPR 2020, Lecture Notes in Computer Science, vol. 12088, Springer Nature Switzerland AG, , 2020, doi: 10.1007/978-3-030-49076-8_1.
- H. Shaikh, Y. Wagh, S. Shinde, and S. Patil, “Classification of affected fruits using machine learning,” International Journal of Engineering Research & Technology (IJERT), vol. 9, no. 3, 2021.
- P. Patil, S. Lande, V. Nagalkar, S. Nikam, and G. Wakchaure, “Grading and sorting technique of dragon fruits using machine learning algorithms,” Journal of Agriculture and Food Research, vol. 4, no. 3, 2021. [Online]. Available: https://doi.org/10.1016/j.jafr.2021.100118
- A. Zakeri, R. Hedayati, M. Khedmati, and M. Taghipour-Gorjikolaie, “Classification of jujube fruit based on several pricing factors using machine learning methods,” 2021. [Online]. Available: https://doi.org/10.48550/arXiv.2111.00112
- Spyder Interface. [Online]. Available: https://anaconda.org/anaconda/spyder
- C. Prasad, “An efficient heart disease detection system utilizing Naïve Bayes classification,” Journal of Applied Information Science, vol. 9, no. 1, pp. 22-25, 2021.
- B. Poornima, “An improved decision tree classification for breast cancer detection with optimal parameters,” Journal of Applied Information Science, vol. 9, no. 1, pp. 19-21, 2021.
- G. Guo, H. Wang, D. Bell, Y. Bi, and K. Greer, “KNN model-based approach in classification,” OTM Confederated International Conferences, 2003, doi: 10.1007/978-3-540-39964-3_62.
- Gokul S., and N. Banu P. K., “Analysis of phishing detection using logistic regression and random forest,” Journal of Applied Information Science, vol. 8, no. 1&2, pp. 7-13, 2020.
- A. MS, and Manjesh R., “Fruit recognition using SVM technique,” International Journal of Science and Research (IJSR), vol. 9, no. 2, pp. 1210-1214, 2020, doi: 10.21275/SR20212155833.
- S. Thakral, and P. Manhas, “Image processing by using different types of discrete wavelet transform,” Advanced Informatics for Computing Research, 2018.
Abstract Views: 325
PDF Views: 0