Currently, herbs recognition system has become a promising method to identify herbs species. Misuse of herbal medicine can cause serious health problems due to toxicological effects of phytochemical. As a result, a system that able to distinguish the types of herbs is needed. Most herbs recognition systems available in the market are dependent on experts. In this research, the concern is to identify the herbs compounds within the same group species where the physical appearance and aroma are similar. The work mainly focuses on herbs recognition system that intended for researchers and medical practitioners use without the need for experts. Electronic Nose (E-Nose) devices have been used extensively to differentiate and characterize the herb species based on their unique odour. Electrical signal generated from the gas sensor array is one of the physical properties studied. The robustness test of the proposed herbs recognition systems is performed via four classification models based on machine learning algorithm: Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Multinomial Logistic Regression (MLR), and Gaussian Radial Basis Function (RBF) Kernel. The performance of the classification accuracy using KNN shows a better result within the family group from 92.15% to 100% compared to the others method.

• Electronic Nose
• Gaussian Radial Basis Function Kernel,
• Herbs Recognition System,
• Herbs Physical Properties,
• K-Nearest Neighbours,
• Multinomial Logistic Regression,
• Odours Pattern Recognition,
• Support Vector Machine

[1] Bodhwani, V., Acharjya, D. P., & Bodhwani, U. 2019. Deep residual networks for plant identification. Procedia Computer Science, 152: 186–194.

[2] Naresh, Y. G., & Nagendraswamy, H. S. 2016. Classification of medicinal plants: An approach using modified LBP with symbolic representation. Neurocomputing, 173: 1789–1797.

[3] Mustafa, M. S., Husin, Z., Tan, W.K., Mavi, M. F., & Farook, R. S. M. 2020. Development of automated hybrid intelligent system for herbs plant classification and early herbs plant disease detection. Neural Computing and Applications, 32:11419-11441.

[4] Muneer, A., & Fati, S.M. 2020. Efficient and automated herbs classification approach based on shape and texture features using deep learning. IEEE Access, 8 :196747-196764.

[5] Shabanzade, M., Zahedi, M., & Aghami, S. A. 2011. Combination of local descriptors and global features for leaf recognition, signal and image processing. Signal & Image Processing: An International Journal (SIPIJ), 2(3): 23-31.

[6] Vo, A.H., Dang, H.T., Nguyen, B.T., & Pham, V.-H. 2019. Vietnamese herbal plant recognition using deep convolutional features. International Journal Machine Learning Computing, 9(3): 363–367.

[7] Zhang, W., & Wen, J. 2021. Research on leaf image identification based on improved AlexNet neural network. Journal of Physics, 2031:1-13.

[8] Mohamad Yusof, U. K. 2015. Development of electronic nose for herbs recognition based on artificial intelligent techniques. Unpublished Master Thesis, Faculty of Engineering, Universiti Putra Malaysia, Serdang, Selangor, Malaysia.

[9] Xu, M., Wang, J., & Zhu, L. 2021. Tea quality evaluation by applying E-nose combined with chemometrics methods. Journal of Food Science and Technology, 58(4): 1549–1561.

[10] Haryono, Anam, K., & Saleh, A. 2020. Autentikasi daun herbal menggunakan convolutional neural network dan raspberry pi. Jurnal Nasional Teknik Elektro dan Teknologi Informasi, 9(3): 278 – 286.

[11] Prasad, S., Kumar, P. S., & Ghosh, D. 2017. An efficient low vision plant leaf shape identification system for smart phones. Multimedia Tools & Applications, 76(5): 6915–6939.

[12] Chiu, S. W., & Tang, K. T. 2013. Towards a chemiresistive sensor-integrated electronic nose: a review. Sensors, 13(10): 14214-14247.

[13] Cui, S., Inocente, E. A. A., Acosta, N., Keener, H. M., Zhu, H., & Ling, P.P. 2019. Development of fast e-nose system for early-stage diagnosis of aphid-stressed tomato plants. Sensors, 19(3480): 1-14.

[14] Jia, W., Liang, G., Jiang, Z., & Jihua, W. 2019. Advances in electronic nose development for application to agricultural products. Food Analytical Methods, 12: 2226–2240.

[15] Tan, T., & Xu, J. 2020. Applications of electronic nose (e-nose) and electronic tongue (e-tongue) in food quality-related properties determination: A review. Artificial Intelligence in Agriculture, 4: 104-115.

[16] Harvey, B. S., & Flores-Sarnat, L. 2019. Development of the human olfactory system. Handbook of Clinical Neurology, 164: 29-45, 2019.

[17] Huang, S., Cai, N., Pedro, P.P, Narrandes, S., Wang, Y., & Xu, W. 2018. Applications of support vector machine (svm) learning in cancer genomics. Cancer Genomics & Proteomics, 15(1): 41-51.

[18] Yan, X., & Jia, M. 2018. A novel optimized SVM classification algorithm with multi-domain feature and its application to fault diagnosis of rolling bearing. Neurocomputing, 313: 47-64.

[19] Manojkumar, P., Surya, C. M., & Varun, P. G. 2017, Identification of ayurvedic medicinal plant by image processing of leaf samples, Proceeding of International Conference on research in computational intelligence and communication network, 3-5 November 2017, Kolkata, India: 351-355. USA: IEEE.

[20] Kan, H. X., Jin, L., & Zhou, F. L. 2017. Classification of medical plant leaf image based on multi-feature extraction. Pattern recognition and analysis, 27(3): 581-587.

[21] Prabhakar, P., Shyamdew, K., Philip, V. S., Kishore, P., & Roopashree, S. 2016. Robust recognition and classification of herbal leaves. International Journal of Research in Engineering and Technology, 6(4):146-149.

[22] Basavaraj, S. A., Suvarna, S. N., & Govardhan, A. 2010. A combined color, texture and edge features-based approach for identification and classification of Indian medical plants. International Journal of Computer Applications, 6(12): 45-51.

[23] Deng, Z., Zhu, X., Cheng, D., Zong, M., & Zhang, S. 2016. Efficient kNN classification algorithm for big data. Neurocomputing, 195(C):143-148.

[24] Ghosh, S., Singh, A., K., Jhanjhi, N. Z., Masud, M., & Aljahdali,S. 2022. SVM and KNN based CNN architectures for plant classification. Computers, Materials & Continua, 71(3): 4257–4274.

[25] Bhardwaj, A., Kaur, M., & Kumar, A. 2013. Recognition of plants by leaf image using moment invariant and texture analysis. International Journal of Innovation and Application Studies, 3(1): 237-248.

[26] Satti, V., Satya, A., & Sharma, S. 2013. An automatic leaf recognition system for plant identification using mechine vision technology. International Journal of Engineering, Science and Technology, 5(4): 874-879.

[27] Connelly, L. 2020. Logistic regression. Medsurg Nursing; Pitman, 29(5): 353-354.

[28] Borah, J. W. G. S., Hines, E. L., Leeson, M. S., Iliescu, D. D., & Bhuyan, M. 2008. Neural network based electronic nose for classification of tea aroma. Univ. Warwick Institutional Repos, 2(1): 7-14.

[29] Abdolvahab, E.R., & Kumar, Y.H.S. 2010. Leaf recognition for plant classification using GLCM and PCA methods. International Journal of Computer Science & Technology, 3(1): 31-36.

[30] Kaur, P., Robin, Mehta, R.G., Balbir, S., & Arora, S. 2019. Development of aqueous-based multi-herbal combination using principal component analysis and its functional significance in HepG2 cells. BMC Complement Alternative Medicine, 19(18):1-17.

[31] Rana, P., Liaw, S. Y., Lee, M. S., & Sheu, S. C. 2021. Discrimination of four Cinnamomum species with physico-functional properties and chemometric techniques: application of PCA and MDA models. Foods, 10(11): 2871, 2021.

[32] Srivastava, D.K., & L. Bhambhu, L. 2010. Data classification using support vector machine. Journal of Theoretical and Applied Information Technology, 12(1): 1-7.

[33] Ben, J. M. Jason, M. D., Naomi, S. B., Mitzi,L. D., & Dudley, R. A. 2014. N-gram support vector machines for scalable procedure and diagnosis classification, with applications to clinical free text data from the intensive care unit. Journal of the American Medical Informatics Association, 21(5): 871-875.

[34] Zhang, Z. 2016. Introduction to machine learning: K-nearest neighbors. Annals of Translational Medicine, 4(11):1-7.

[35] Zhang, S., Li, X., Zong, M., Zhu, X., & Cheng, D. 2017. Learning k for kNN classification. ACM Transactions on Intelligent Systems and Technology, 8: 1-19.

[36] Sontakke, S., Lohokare, J., Dani, R., & Shivagaje, P. 2018. Classification of cardiotocography signals using machine learning, Proceedings of the 2018 Intelligent Systems Conference, 6-7 September 2018, London, UK: 1-6. USA: IEEE.

[37] Daniel, S. P., Ferri, C., & Ramirez, M. J. 2017. Improving performance of multiclass classification by inducing class hierarchies. Procedia Computer Science, 108: 1692-1701.