Crafting a Specific Deep Network for Real-Time Identification of Ayurvedic Plants
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Abstract
Plants play vital role for existence of living being specially humans as they rely for food, medicine and for many other needs. Plant-based medicine is an age-old science practiced in many countries. Use of plant-based medicine is considered safer compared to chemical-based medicine for humans because it comprises of natural ingredients. Planet earth is blessed with plenty plant species having medicinal values. However, current generation have lack of knowledge of these medicinal plants. Hence there is a requirement for automated identification of medicinal plants to use them as medicine. In the present work, automated classifying system for identification of medicinal leaves is designed using deep learning approach. Further for real-time usage of the developed classifying system, Android based cell phone application is developed. Medicinal values of the identified leaves are also displayed on the cell phone screen. The dataset required for training the deep network is acquired in the Southern part of Karnataka, India. The system identifies eight types of medicinal leaves with an average accuracy of 99%. Such an automated system will help people associated with ayurvedic medicine, botanists and also common people for using herbs as medicine.
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References
Abdollahi, J. (2022). Identification of medicinal plants in Ardabil using deep learning: Identification of medicinal plants using deep learning. In 27th International Computer Conference (pp. 1–6). Computer Society of Iran. https://doi.org/10.1109/CSICC55295.2022.9780493
Ali, R., Hardie, R., & Essa, A. (2018). A leaf recognition approach to plant classification using machine learning. In NAECON 2018 – IEEE National Aerospace and Electronics Conference, Dayton, OH, USA (pp. 431–434). IEEE. doi: 10.1109/NAECON.2018.8556785
Ariful Hassan, M., Sydul Islam, M., Mehedi Hasan, M., Shorif, S. B., Tarek Habib, M., & Uddin, M. S. (2022). Medicinal plant recognition from leaf images using deep learning. In M. S. Uddin, & J. C. Bansal (Eds.), Computer vision and machine learning in agriculture 2. Algorithms for intelligent systems (pp. 137–154). Springer. https://doi.org/10.1007/978-981-16-9991-7_9
Azadnia, R., Al-Amidi, M. M., Mohammadi, H., Cifci, M. A., Daryab, A., & Cavallo, E. (2022). An AI based approach for medicinal plant identification using deep CNN based on global average pooling. Agronomy, 12(11), 2723. https://doi.org/10.3390/agronomy12112723
Begue, A., Kowlessur, V., Singh, U., Mahomoodally, F., & Pudaruth, S. (2017). Automatic recognition of medicinal plants using machine learning techniques. International Journal of Advanced Computer Science and Applications, 8(4), 166–175.
Geerthana, R., Nandhini, P., & Suriyakala, R. (2021). Medicinal plant identification using deep learning. International Research Journal on Advanced Science Hub, 3(05S), 48–53. https://doi.org/10.47392/irjash.2021.139
Haryono, K. A., & Saleh, A. (2020). A novel herbal leaf identification and authentication using deep learning neural network. In International Conference on Computer Engineering, Network, and Intelligent Multimedia (CENIM), Surabaya, Indonesia, (pp. 338–342). IEEE. https://doi.org/10.1109/CENIM51130.2020.9297952
Hedjazi, M. A., Kourbane, I., & Genc, Y. (2017). On identifying leaves: A comparison of CNN with classical ML methods. In 2017 25th Signal Processing and Communications Applications Conference (SIU), Antalya, Turkey, (pp. 1–4). IEEE. doi: 10.1109/SIU.2017.7960257
Kartikeyan, P., & Shrivastava, G. (2021). Review on emerging trends in detection of plant diseases using image processing with machine learning. International Journal of Computer Applications, 174, 39–48. doi: 10.5120/ijca2021920990
Kumar, S., & Pearline, A. (2023). Real-Time Plant Species Recognition Using Non-averaged DenseNet-169 Deep Learning Paradigm. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol. 1777 (pp. 58-72) Springer. https://doi.org/10.1007/978-3-031-31417-9_5
Lee, C. P., Lim, K. M., Song, Y. X., & Alqahtani, A. (2023). Plant-CNN-ViT: Plant classification with ensemble of convolutional neural networks and vision transformer. Plants, 12(14), 2642. https://doi.org/10.3390/plants12142642
Malik, O. A., Ismail, N., Hussein, B. R., & Yahya, U. (2022). Automated real-time identification of medicinal plants species in natural environment using deep learning models – A case study from Borneo region. Plants, 11(15), 1952. https://doi.org/10.3390/plants11151952
Manoharan, J. S. (2021). Flawless detection of herbal plant leaf by machine learning classifier through two stage authentication procedure. Journal of Artificial Intelligence and Capsule Networks, 3(2), 125–139. doi: 10.36548/jaicn.2021.2.005
Naeem, S., Ali, A., Chesneau, C., Tahir, M. H., Jamal, F., Sherwani, R. A. K., & Ul Hassan, M. (2021). The classification of medicinal plant leaves based on multispectral and texture feature using machine learning approach. Agronomy, 11(2), 263. https://doi.org/10.3390/agronomy11020263
Oppong, S. O., Twum, F., Hayfron-Acquah, J. B., & Missah, Y. M. (2021). Medicinal plant identification using Gabor filters and deep learning techniques: A paper review. Journal of Computer Science, 17(12), 1210–1221. https://doi.org/10.3844/jcssp.2021.1210.1221
Oppong, S. O., Twum, F., Hayfron-Acquah, J. B., & Missah, Y. M. (2022). A novel computer vision model for medicinal plant identification using Log-Gabor filters and deep learning algorithms. Computational Intelligence and Neuroscience, article ID 1189509. https://doi.org/10.1155/2022/1189509
Pearline, S. A, Vajravelu, S. K., & Harini, S. (2019). A study on plant recognition using con-ventional image processing and deep learning approaches. Journal of Intelligent & Fuzzy Systems, 36(3), 1997–2004. doi: 10.3233/JIFS-169911
Picek, L., Šulc, M., Patel, Y., & Matas, J. (2022). Plant recognition by AI: Deep neural nets, trans-formers, and kNN in deep embeddings. Frontiers of Plant Science, 13, 787527. https://doi.org/10.3389/fpls.2022.787527
Pudaruth, S., Mahomoodally, M. F., Kissoon, N., & Chady, F. (2021). MedicPlant: A mobile application for the recognition of medicinal plants from the Republic of Mauritius using deep learning in real-time. International Journal of Artificial Intelligence, 10(4), 938–947. doi:10.11591/ijai.v10.i4.pp938-947
SkandaH, N., Karanth, S. S., Suvijith, S., & SwathiK., S. (2019). Plant identification meth-odologies usingmachine learning algorithms. International Journal of Engineering Research and Technology, 8, 187–190. https://api.semanticscholar.org/CorpusID:150158706
Tan, J. w., Chang, S.-W., Abdul-Kareem, S., Yap, H. J., & Yong, K.-T. (2020). Deep learning for plant species classification using leaf vein morphometric. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(1), 82–90. doi: 10.1109/TCBB.2018.2848653
Wagle, S. A., Harikrishnan, R., Ali, S. H. M., & Faseehuddin, M. (2022). Classification of plant leaves using new compact convolutional neural network models. Plants, 11(1), 24. https://doi.org/10.3390/plants11010024
Yue, J., Li, W., & Wang, Y. (2021). Superiority verification of deep learning in the identification of medicinal plants: Taking Paris polyphylla var. yunnanensis as an example. Frontiers in Plant Science, 12. https://doi.org/10.3389/fpls.2021.752863