Abstract
Plants can be classified based on various classification methods such as cell, genetic and serum etc. It's difficult for an individual to explore the various classification methods and it's practically not feasible as it demands good knowledge in plant taxonomy and long-term time investment. Due to the shortage of experienced and qualified taxonomists in identification and classification of medicinal plants, with the help of different image processing algorithms and computer vision, the above difference can be bridged. The main objective is to develop a Deep Learning and Machine Learning based model to identify and classify plants based on various features, which is done with the help of Gabor filter and Gray Level Co-occurrence Matrices (GLCM) and using classifiers such as Random Forest (RF), and Light Gradient Boosting Machine (LGBM), and made a comparative analysis which resulted an accuracy of 95.5% with LGBM and GLCM filter and used to develop a standalone device that clicks a picture and identifies the medicinal plant.
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
Similar content being viewed by others
References
Dileep MR, Pournami PN (2019) AyurLeaf: a deep learning approach for classification of medicinal plants. In: TENCON 2019—2019 IEEE Region 10, conference (TENCON), Kochi, India.https://doi.org/10.1109/TENCON.2019.8929394
Paulson A, Ravishankar S (2020) AI based indigenous medicinal plant identification, In: 2020 advanced computing and communication technologies for high performance applications (ACCTHPA), Cochin, India. https://doi.org/10.1109/ACCTHPA49271.2020.9213224
Gao L, Lin X (2019) Fully automatic segmentation method for medicinal plant leaf images in complex background. Comput Electron Agric 164:104924
Dyrmann M, Karstoft H, Midtiby HS (2016) Plant species classification using deep convolutional neural network. Biosyst Eng 151:72–80
Amuthalingeswaran C, Sivakumar M, Renuga P, Alexpandi S, Elamathi J, Santhana Hari S (2019) Identification of medicinal plant's and their usage by using deep learning. In: 2019 3rd international conference on trends in electronics and informatics (ICOEI), Tirunelveli, India, 23–25 April 2019. https://doi.org/10.1109/ICOEI.2019.8862765
Kumar M, Gupta S, Gao X-Z, Singh A (2019) Plant species recognition using morphological features and adaptive boosting methodology. IEEE Access 7. https://doi.org/10.1109/ACCESS.2019.2952176
Khan R, Debnath R (2019) Segmentation of single and overlapping leaves by extracting appropriate contours. https://doi.org/10.5121/csit.2019.91323
Pushpa BR, Megha N, Amaljith KB (2020) Comparison and classification of medicinal plant leaf based on texture feature. In: 2020 international conference for emerging technology (INCET), 2020, pp 1–5. https://doi.org/10.1109/INCET49848.2020.9154155
Huixian J (2020) The analysis of plants image recognition based on deep learning and artificial neural network. IEEE (Data Min Internet Things). https://doi.org/10.1109/ACCESS.2020.2986946
Kesava PSA, Peeyush KP (2019) Autonomous robot to detect diseased leaves in plants using convolutional neural networks. In: 2019 3rd international conference on trends in electronics and informatics (ICOEI), 2019, pp 806–809. https://doi.org/10.1109/ICOEI.2019.8862737
Venkataraman D, Mangayarkarasi N (2017) Support vector machine based classification of medicinal plants using leaf features. In: 2017 international conference on advances in computing, communications and informatics (ICACCI), 2017, pp 793–798. https://doi.org/10.1109/ICACCI.2017.8125939
Sivaganesan D (2021) Performance estimation of sustainable smart farming with blockchain technology. IRO J Sustain Wirel Syst 3(2):97–106
Palani U, Vasanthi D, Rabiya Begam S (2020) Enhancement of medical image fusion using image processing. J Innov Image Process (JIIP) 2(04):165–174
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Das, A., Siva Sai Kumar, B., Shiva Shankar Reddy, S., Naveen Reddy, S., Peeyush, K.P. (2023). Ayurvedic Medicinal Plant Identification System Using Embedded Image Processing Techniques. In: Bindhu, V., Tavares, J.M.R.S., Vuppalapati, C. (eds) Proceedings of Fourth International Conference on Communication, Computing and Electronics Systems . Lecture Notes in Electrical Engineering, vol 977. Springer, Singapore. https://doi.org/10.1007/978-981-19-7753-4_14
Download citation
DOI: https://doi.org/10.1007/978-981-19-7753-4_14
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-7752-7
Online ISBN: 978-981-19-7753-4
eBook Packages: EngineeringEngineering (R0)