Advanced Medicinal Plant Recognition with Convolutional Neural Networks
Abstract
In the realm of herbal medicine, Precise recognition of medicinal plants is essential for unlocking their therapeutic benefits. To fully utilize the curative potential of medicinal plants, precise identification is essential in the domain of medicine. Traditional methods often rely on visual cues from leaves, but the deceptive similarities and variable characteristics pose challenges. To overcome these hurdles, this work introduces an innovative approach utilizing deep learning models like VGG16, RESNET50 and Inception. By focusing on the intricate features of plant leaves, our model aims to accurately distinguish between medicinal and others plants. Drawing upon the complex features of plant leaves, our model is able to identify medicinal plants with greater accuracy than existing approaches. Leveraging advanced neural network architectures, this work seeks to provide a reliable solution for medical practitioners, herbalists, and researchers, bridging traditional knowledge with modern technology to enhance the precision of medicinal plant identification.
Keywords:
CNN, Deep learning, Inception, Medical Plant classification, ResNet, Visual Geometry Group
Downloads
Download data is not yet available.
References
Abdollahi, J. (2022). Identification of medicinal plants in ardabil using deep learning: identification of medicinal plants using deep learning. In 2022 27th International computer conference, computer society of Iran (CSICC) (pp. 1-6). IEEE. https://doi.org/10.1109/CSICC55295.2022.9780493
Ang, R. M. L., & Linsangan, N. B. (2023). Herbal Medicinal Plant Identification Using Leaf Vein Through Image Processing and Convolutional Neural Network. In TENCON 2023-2023 IEEE Region 10 Conference (TENCON) (pp. 1046-1051). IEEE. https://doi.org/10.1 109/TENCON58879.2023.10322529.
Ayumi, V., Ermatita, E., Abdiansah, A., Noprisson, H., Purba, M., & Utami, M. (2021). A study on medicinal plant leaf recognition using artificial intelligence. In 2021 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS (pp. 40-45). IEEE. https://doi.org/10.1109/CIMCIS53775.2021.9699363.
Ding, R., Luo, J., Wang, C., Yu, L., Yang, J., Wang, M., ...&Gu, R. (2023). Identifying and mapping individual medicinal plant Lamiophlomisrotata at high elevations by using unmanned aerial vehicles and deep learning. Plant Methods, 19(1), 38. https://doi.org/10.1186/s13007-023-01015-z.
Gracelin, S., & Raimond, K. (2022). Deep Learning based Indigenous Herbal Medicinal Plants Recognition: A Comprehensive Review. In 2022 6th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1412-1418). IEEE. https://doi.org/10.1109/ICCMC534702022.9753825.
Kadiwal, S. M., Hegde, V., Shrivathsa, N. V., Gowrishankar, S., Srinivasa, A. H., & Veena, A. (2022). Deep Learning based Recognition of the Indian Medicinal Plant Species. In 2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA) (pp. 762-767). IEEE. https://doi.org/10.1 109/ACIRCA54612.2022.9985746.
Kumar, P. M., Surya, C. M., & Gopi, V. P. (2017). Identification of ayurvedic medicinal plants by image processing of leaf samples. In 2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) (pp. 231-238). IEEE. https://doi.org/10.1109/ICRCICN.2017.8234512
LV, N. P., Dokku, D. B., Talasila, S. L., & Tumuluru, P. (2023). Hyper Parameter Optimization for Transfer Learning of ShuffleNetV2 with Edge Computing for Casting Defect Detection. https://doi.org/10.56042/jsir.v82i2.70250.
Pacifico, L. D., Britto, L. F., Oliveira, E. G., & Ludermir, T. B. (2019, October). Automatic classification of medicinal plant species based on color and texture features. In 2019 8th Brazilian Conference on Intelligent Systems (BRACIS) (pp. 741-746). IEEE. https://doi.org/10.1109/BRACIS.2019.00133.
Paulson, A., & Ravishankar, S. (2020, July). AI based indigenous medicinal plant identification. In 2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA) (pp. 57-63). IEEE. https://doi.org/10.1109/ACCTHPA49271.2020.9213224.
Quoc, T. N., & Hoang, V. T. (2020, October). Medicinal Plant identification in the wild by using CNN. In 2020 International Conference on Information and Communication Technology Convergence (ICTC) (pp. 25-29). IEEE. https://doi.org/10.1 109/ICTC49870.2020.9289480.
Rao, M., Kumar, S., & Rao, K. (2023). Effective medical leaf identification using hybridization of GMM-CNN. International Journal of Experimental Research and Review, 32, 115-123. https://doi.org/10.52756/ijerr.2023.v32.009
Reddy, A. S., Praveen, S. P., Ramudu, G. B., Anish, A. B., Mahadev, A., & Swapna, D. (2023). A network monitoring model based on convolutional neural networks for unbalanced network activity. In 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT) (pp. 1267-1274). IEEE. https://doi.org/10.1109/ICSSIT55814.2023.10060879.
Sharrab, Y., Al-Fraihat, D., Tarawneh, M., & Sharieh, A. (2023). Medicinal plants recognition using deep learning. In 2023 International Conference on Multimedia Computing, Networking and Applications (MCNA) (pp. 116-122). IEEE. https://doi.org/10.1109/MCNA59361.2023.10185880.
Simion, I. M., Casoni, D., & Sârbu, C. (2019). Classification of Romanian medicinal plant extracts according to the therapeutic effects using thin layer chromatography and robust chemometrics. Journal of Pharmaceutical and Biomedical Analysis, 163, 137-143. https://doi.org/10.1016/j.jpba.2018.09.047.
Sirisha, U., & Bolem, S. C. (2023). Utilizing a hybrid model for human injury severity analysis in traffic accidents. Traitement du Signal, 40(5), 2233. https://doi.org/10.18280/ts.400540.
Swapna, D., Sri, U. K., Himaja, V. S. N., Varshita, T. N., Gayatri, V., & Praveen, S. P. (2023, December). Crypto Logistic Network: Food Supply Chain and Micro Investment using Blockchain. In 2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS) (pp. 908-915). IEEE. https://doi.org/10.1109/ICACRS58579.2023.10404449.
Talasila, S., & Vijaya Kumari, R. (2022). Cascade Network Model to Detect Cognitive Impairment using Clock Drawing Test. Journal of Scientific & Industrial Research, 81(12), 1276-1284. https://doi.org/10.56042/jsir.v81i12.69309.
Thanikkal, J. G., Dubey, A. K., & Thomas, M. T. (2020). Unique shape descriptor algorithm for medicinal plant identification (SDAMPI) with abridged image database. IEEE Sensors Journal, 20(21), 13103-13109. https://doi.org/10.1109/JSEN 2020.3002909.
Thatha, V. N., Kumari, P. M. K., Sirisha, U., Manoj, V. V. R., & Surapaneni, P. P. (2024). GLAD: Advanced Attention Mechanism-Based Model for Grape Leaf Disease Detection. Ingenierie des Systemes d'Information, 29(2), 687. https://doi.org/10.18280/isi.290230.
Tran, A. C., Thoa, P. K., Tran, N. C., & Duong-Trung, N. (2020). Real-time recognition of medicinal plant leaves using bounding-box based models. In 2020 International Conference on Advanced Computing and Applications (ACOMP) (pp. 34-41). IEEE. https://doi.org/10.1109/ACOMP50827.202000013.
Turkoglu, M., & Hanbay, D. (2019). Leaf-based plant species recognition based on improved local binary pattern and extreme learning machine. Physica A: Statistical Mechanics and its Applications, 527, 121297. https://doi.org/10.1016/j.physa.2019.121297
Yoo, S., Ha, S., Shin, M., Noh, K., Nam, H., & Lee, D. (2018). A data-driven approach for identifying medicinal combinations of natural products. IEEE Access, 6, 58106-58118. https://doi.org/10.1109/ACCESS.2018.2874089
Yuan, Z., Xie, Y., Wu, W., Guo, X., Wang, Y., Zhao, X., ... & Kumar, S. P. (2022). Image Recognition Study on Leaf Characteristics of Medicinal Plants Based on Otsu Dynamic Threshold Segmentation Algorithm. In 2022 IEEE 2nd International Conference on Mobile Networks and Wireless Communications (ICMNWC) (pp. 1-7). IEEE. https://doi.org/10.1109/ICMNWC56175.2022.10031938
Ang, R. M. L., & Linsangan, N. B. (2023). Herbal Medicinal Plant Identification Using Leaf Vein Through Image Processing and Convolutional Neural Network. In TENCON 2023-2023 IEEE Region 10 Conference (TENCON) (pp. 1046-1051). IEEE. https://doi.org/10.1 109/TENCON58879.2023.10322529.
Ayumi, V., Ermatita, E., Abdiansah, A., Noprisson, H., Purba, M., & Utami, M. (2021). A study on medicinal plant leaf recognition using artificial intelligence. In 2021 International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS (pp. 40-45). IEEE. https://doi.org/10.1109/CIMCIS53775.2021.9699363.
Ding, R., Luo, J., Wang, C., Yu, L., Yang, J., Wang, M., ...&Gu, R. (2023). Identifying and mapping individual medicinal plant Lamiophlomisrotata at high elevations by using unmanned aerial vehicles and deep learning. Plant Methods, 19(1), 38. https://doi.org/10.1186/s13007-023-01015-z.
Gracelin, S., & Raimond, K. (2022). Deep Learning based Indigenous Herbal Medicinal Plants Recognition: A Comprehensive Review. In 2022 6th International Conference on Computing Methodologies and Communication (ICCMC) (pp. 1412-1418). IEEE. https://doi.org/10.1109/ICCMC534702022.9753825.
Kadiwal, S. M., Hegde, V., Shrivathsa, N. V., Gowrishankar, S., Srinivasa, A. H., & Veena, A. (2022). Deep Learning based Recognition of the Indian Medicinal Plant Species. In 2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA) (pp. 762-767). IEEE. https://doi.org/10.1 109/ACIRCA54612.2022.9985746.
Kumar, P. M., Surya, C. M., & Gopi, V. P. (2017). Identification of ayurvedic medicinal plants by image processing of leaf samples. In 2017 Third International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN) (pp. 231-238). IEEE. https://doi.org/10.1109/ICRCICN.2017.8234512
LV, N. P., Dokku, D. B., Talasila, S. L., & Tumuluru, P. (2023). Hyper Parameter Optimization for Transfer Learning of ShuffleNetV2 with Edge Computing for Casting Defect Detection. https://doi.org/10.56042/jsir.v82i2.70250.
Pacifico, L. D., Britto, L. F., Oliveira, E. G., & Ludermir, T. B. (2019, October). Automatic classification of medicinal plant species based on color and texture features. In 2019 8th Brazilian Conference on Intelligent Systems (BRACIS) (pp. 741-746). IEEE. https://doi.org/10.1109/BRACIS.2019.00133.
Paulson, A., & Ravishankar, S. (2020, July). AI based indigenous medicinal plant identification. In 2020 Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA) (pp. 57-63). IEEE. https://doi.org/10.1109/ACCTHPA49271.2020.9213224.
Quoc, T. N., & Hoang, V. T. (2020, October). Medicinal Plant identification in the wild by using CNN. In 2020 International Conference on Information and Communication Technology Convergence (ICTC) (pp. 25-29). IEEE. https://doi.org/10.1 109/ICTC49870.2020.9289480.
Rao, M., Kumar, S., & Rao, K. (2023). Effective medical leaf identification using hybridization of GMM-CNN. International Journal of Experimental Research and Review, 32, 115-123. https://doi.org/10.52756/ijerr.2023.v32.009
Reddy, A. S., Praveen, S. P., Ramudu, G. B., Anish, A. B., Mahadev, A., & Swapna, D. (2023). A network monitoring model based on convolutional neural networks for unbalanced network activity. In 2023 5th International Conference on Smart Systems and Inventive Technology (ICSSIT) (pp. 1267-1274). IEEE. https://doi.org/10.1109/ICSSIT55814.2023.10060879.
Sharrab, Y., Al-Fraihat, D., Tarawneh, M., & Sharieh, A. (2023). Medicinal plants recognition using deep learning. In 2023 International Conference on Multimedia Computing, Networking and Applications (MCNA) (pp. 116-122). IEEE. https://doi.org/10.1109/MCNA59361.2023.10185880.
Simion, I. M., Casoni, D., & Sârbu, C. (2019). Classification of Romanian medicinal plant extracts according to the therapeutic effects using thin layer chromatography and robust chemometrics. Journal of Pharmaceutical and Biomedical Analysis, 163, 137-143. https://doi.org/10.1016/j.jpba.2018.09.047.
Sirisha, U., & Bolem, S. C. (2023). Utilizing a hybrid model for human injury severity analysis in traffic accidents. Traitement du Signal, 40(5), 2233. https://doi.org/10.18280/ts.400540.
Swapna, D., Sri, U. K., Himaja, V. S. N., Varshita, T. N., Gayatri, V., & Praveen, S. P. (2023, December). Crypto Logistic Network: Food Supply Chain and Micro Investment using Blockchain. In 2023 2nd International Conference on Automation, Computing and Renewable Systems (ICACRS) (pp. 908-915). IEEE. https://doi.org/10.1109/ICACRS58579.2023.10404449.
Talasila, S., & Vijaya Kumari, R. (2022). Cascade Network Model to Detect Cognitive Impairment using Clock Drawing Test. Journal of Scientific & Industrial Research, 81(12), 1276-1284. https://doi.org/10.56042/jsir.v81i12.69309.
Thanikkal, J. G., Dubey, A. K., & Thomas, M. T. (2020). Unique shape descriptor algorithm for medicinal plant identification (SDAMPI) with abridged image database. IEEE Sensors Journal, 20(21), 13103-13109. https://doi.org/10.1109/JSEN 2020.3002909.
Thatha, V. N., Kumari, P. M. K., Sirisha, U., Manoj, V. V. R., & Surapaneni, P. P. (2024). GLAD: Advanced Attention Mechanism-Based Model for Grape Leaf Disease Detection. Ingenierie des Systemes d'Information, 29(2), 687. https://doi.org/10.18280/isi.290230.
Tran, A. C., Thoa, P. K., Tran, N. C., & Duong-Trung, N. (2020). Real-time recognition of medicinal plant leaves using bounding-box based models. In 2020 International Conference on Advanced Computing and Applications (ACOMP) (pp. 34-41). IEEE. https://doi.org/10.1109/ACOMP50827.202000013.
Turkoglu, M., & Hanbay, D. (2019). Leaf-based plant species recognition based on improved local binary pattern and extreme learning machine. Physica A: Statistical Mechanics and its Applications, 527, 121297. https://doi.org/10.1016/j.physa.2019.121297
Yoo, S., Ha, S., Shin, M., Noh, K., Nam, H., & Lee, D. (2018). A data-driven approach for identifying medicinal combinations of natural products. IEEE Access, 6, 58106-58118. https://doi.org/10.1109/ACCESS.2018.2874089
Yuan, Z., Xie, Y., Wu, W., Guo, X., Wang, Y., Zhao, X., ... & Kumar, S. P. (2022). Image Recognition Study on Leaf Characteristics of Medicinal Plants Based on Otsu Dynamic Threshold Segmentation Algorithm. In 2022 IEEE 2nd International Conference on Mobile Networks and Wireless Communications (ICMNWC) (pp. 1-7). IEEE. https://doi.org/10.1109/ICMNWC56175.2022.10031938
Statistics
90 Views | 74 Downloads
How to Cite
Lakshmi, T., Ayyappa, Y., Aruna, V., Rani, D., Kumari, P., & Praveen, S. (2024). Advanced Medicinal Plant Recognition with Convolutional Neural Networks. International Journal of Advancement in Life Sciences Research, 7(4), 87-97. https://doi.org/https://doi.org/10.31632/ijalsr.2024.v07i04.008
Section
Research Articles
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
.