ICTACT Journal on Soft Computing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Tracing and Recognition of Medicinal Herbs in Marunthuvazh Malai at the Western Ghats Through Feature Extraction


Affiliations
1 Department of Information Technology, University College of Engineering, Nagercoil, India., India
2 Department of Mechanical Engineering University College of Engineering, Nagercoil, India., India
     

   Subscribe/Renew Journal


The identification and classification of the herbs using the naked eye is difficult in forest or mountain areas like Marunthuvazh Malai of Kanyakumari district. The difficulties arise because of the variations in the crops identified are inaccurate. Mostly the manual prediction is taken place in those areas which require high expertise and more human resources. In this work both plant identification and tracking system based on fuzzy empowered Hybrid artificial neural networks (FHANN) are proposed. Here the input is taken from the video signals taken by the drone camera. The input video signals are converted into images. The fuzzy logic along with the HANN is used for the classification of the specific herbs from the set of plants. Some of the herbs included in the analysis are Parsley, Dill, Oregano, Chervil, Stevia, Basil, Catnip, Fennel and Lemon Grass. This approach used artificial neural networks (ANN) in combination with the K-Nearest neighbor (KNN) as the hybrid model for the herb prediction and classification in association with the fuzzy logic. The Linear Discriminant Analysis (LDA) and Convolutional Autoencoder are used as a hybrid model for the extraction of the feature from the obtained images. This approach considers various shapes, color features, and textures specifically representing the specific herbs. The experimental results show that the proposed model provides better results in the identification and classification of the various medicinal herbs.

Keywords

Artificial Neural Networks, K-Nearest Neighbour, Linear Discriminant Analysis, Convolutional Auto Encoder.
Subscription Login to verify subscription
User
Notifications
Font Size

  • M. Zhang, Chunhong Zhang, Heuberger Heidi, and Minhui Li, “Comparison of the Guidelines on Good Agricultural and Collection Practices in Herbal Medicine of the European Union, China, the WHO, and the United States of America”, Pharmacological Research, Vol. 167, pp. 105533-10545, 2021.

  • Danning Ma, Shanshan Wang, Yu Shi, Shenglou Ni, Minke Tang and Anlong Xu, “The Development of Traditional Chinese Medicine”, Journal of Traditional Chinese Medical Sciences, Vol. 8, pp. 1-9, 2021.

  • Hubert Cheung, Hunter Doughty, Amy Hinsley, Elisabeth Hsu, Tien Ming Lee, E.J. Milner‐Gulland, Hugh P. Possingham and Duan Biggs, “Understanding Traditional Chinese Medicine to Strengthen Conservation Outcomes”, People and Nature, Vol. 3, No. 1, pp. 1151-128, 2021.

  • Luis Carlos, Jorge Pereira Machado, Fernando Jorge Monteiro and Henry Johannes Greten, “Understanding Traditional Chinese Medicine Therapeutics: An Overview of the Basics and Clinical Applications”, Healthcare, Vol. 9, No. 3, pp. 257-265, 2021.

  • Kumar K. Suresh, A.S. Radhamani, S. Sundaresan and T. Ananth Kumar, “Medical Image Classification and Manifold Disease Identification through Convolutional Neural Networks: A Research Perspective”, Proceedings of International Conference on Deep Learning in Biomedical Engineering and Health Informatics, pp. 203-225, 2021.

  • Wajid Zaman, Jianfei Ye, Saddam Saqib, Yun Liu, Zhangjian Shan, Dacheng Hao, Zhiduan Chen, and Peigen Xiao, “Predicting Potential Medicinal Plants with Phylogenetic Topology: Inspiration from the Research of Traditional Chinese Medicine”, Journal of Ethnopharmacology, Vol. 281, pp. 1-14, 2021.

  • Jia-Chen, Shuang-Jing Li, Jian Yong Guo, Guo Yan Zhang, Hui Kang, Xiu-Jing Shi, Han Zhou, Yu-Fen Liang, WeiTong Shen and Li Jian Lei, “Urinary Cadmium and Peripheral Blood Telomere Length Predict the Risk of Renal Function Impairment: A Study of 547 Community Residents of Shanxi, China”, Environmental Science and Pollution Research, Vol. 23, pp. 1-12, 2022.

  • P. Thiruvikraman and M. Pavithra, “A Survey on Haze Removal Techniques in Satellite Images”, Irish Interdisciplinary Journal of Science and Research, Vol. 5, No. 2, pp. 1-6, 2021.

  • Jameel R. Al-Obaidi and E.I. Ahmad Kamil, “Integration of Medicinal Plants into Comprehensive Supply Chains: The Threats and Opportunities of Environmental Devastation”, Proceedings of International Conference on Environmental Challenges and Medicinal Plants, pp. 487-512, 2022.

  • Gil Nelson and Shari Ellis, “The History and Impact of Digitization and Digital Data Mobilization on Biodiversity Research”, Philosophical Transactions of the Royal Society, Vol. 17, pp. 1-14, 2019.

  • Ashkan Nabavi-Pelesaraei, Naghmeh Mohammadkashi, Leila Naderloo, Mahsa Abbasi and Kwok-Wing Chau, “Principal of Environmental Life Cycle Assessment for Medical Waste during COVID-19 Outbreak to Support Sustainable Development Goals”, Science of the Total Environment, Vol. 827, pp. 1-12, 2022.

  • Muhammed Ashiq, Mantu Kumar Mahalik and K. Mohamed Ismail Yasar Arafath, “The Role of Tourism Development in India's Environmental Degradation: Evidence from ARDL and Wavelet Coherence Approaches”, Management of Environmental Quality: An International Journal, Vol. 23, pp. 1-13, 2022.

  • Sunday Adeola, Joseph Bamidele Awotunde, Ademola Olusola Adesina, Philip Achimugu and T. Ananth Kumar, “Internet of Medical Things (IoMT): Applications, Challenges, and Prospects in a Data-Driven Technology”, Intelligent Healthcare, Vol. 12, pp. 299-319, 2022.

  • P. Pugazhendiran, T. Ananth Kumar and S. Sundaresan, “An Advanced Revealing and Classification System for Plant Illnesses using Unsupervised Bayesian-based SVM Classifier and Modified HOG-ROI Algorithm”, Proceedings of Contemporary Issues in Communication, Cloud and Big Data Analytics, pp. 259-269, 2022.

  • Jameel R. Al-Obaidi and E.I. Ahmad Kamil, “Integration of Medicinal Plants into Comprehensive Supply Chains: The Threats and Opportunities of Environmental Devastation”, Proceedings of International Conference on Environmental Challenges and Medicinal Plants, pp. 487-512, 2022.

  • H.X. Kan and F.L. Zhou, “Classification of Medicinal Plant Leaf Image based on Multi-Feature Extraction”, Pattern Recognition and Image Analysis, Vol. 27, No. 3, pp. 581- 587, 2017.

  • Cem Kalyoncu and Onsen Toygar, “Geometric Leaf Classification”, Computer Vision and Image Understanding, Vol. 133, pp. 102-109, 2015.

  • Silky Sachar and Anuj Kumar, “Survey of Feature Extraction and Classification Techniques to Identify Plant Through Leaves”, Expert Systems with Applications, Vol. 167, pp. 1-13, 2021.

  • Priyanka Bijalwan, Shreya Bajaj and Nitin Khanna, “Stability Analysis of Classifiers for Leaf Recognition using Shape Features”, Proceedings of International Conference on Communication and Signal Processing, pp. 657-661, 2014.

  • P.S. Soltis and Shelley A. James Gil Nelson, “Green Digitization: Online Botanical Collections Data Answering Real‐World Questions”, Applications in Plant Sciences, Vol. 6, No. 2, pp. 1-13, 2018.

  • Halil Durak, “Pyrolysis of Xanthium Strumarium in a Fixed Bed Reactor: Effects of Boron Catalysts and Pyrolysis Parameters on Product Yields and Character”, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, Vol. 38, No. 10, pp. 1400-1409, 2016.

  • Jose Carranza-Rojas and Erick Mata-Montero, “Combining Leaf Shape and Texture for Costa Rican Plant Species Identification”, CLEI Electronic Journal, Vol. 19, No. 1, pp.1-7, 2016.

  • Jana Kholova, Elizabeth Arnaud, Destan Aytekin and Vania Azevedo, “In Pursuit of a Better World: Crop Improvement and the CGIAR”, Journal of Experimental Botany, Vol. 72, No. 14, pp. 5158-5179, 2021.

  • Laurence Packer, Cory Sheffield and Robert Hanner, “DNA Barcoding and the Mediocrity of Morphology”, Molecular Ecology Resources, Vol. 9, pp. 42-50, 2009.

  • Brian A. Keating, Mervyn E. Probert, Michael J. Robertson, D. Holzworth and Neil I. Huth, “An Overview of APSIM, A Model Designed for Farming Systems Simulation”, European Journal of Agronomy, Vol. 18, No. 3-4, pp. 267- 288, 2003.

  • Kaijun Zhou and Lizhi Shen, “Rotation and Translation Invariant Palmprint Recognition with Biologically Inspired Transform”, IEEE Access, Vol. 8, pp. 80097-80119, 2020.

  • Ahmed El Banhawy, Ahmed ElKordy, Reham Farag, Ola Abd Elbar, Ahmed Faried and Faten Ellamouni, “Taxonomic Significance of the Leaf Geometric and Micrometric Attributes in the Discrimination of Some Cultivars of Mangifera Indica L (Anacardiaceae)”, Egyptian Journal of Botany, Vol. 61, No. 1, pp. 255-269, 2021.

  • Krishna Singh and Sangeeta Gupta, “SVM-BDT PNN and Fourier Moment Technique for Classification of Leaf Shape”, International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol. 3, No. 4, pp. 67- 78, 2010.

  • Ahmet Arslan and Mehmet Kaya, “Determination of Fuzzy Logic Membership Functions using Genetic Algorithms”, Fuzzy Sets and Systems, Vol. 118, No. 2, pp. 297-306, 2001.

  • David Adedayo, Zhaoqiang Wei and Yang Yongquan, “Automated Web Usage Data Mining and Recommendation System using K-Nearest Neighbor (KNN) Classification Method”, Applied Computing and Informatics, Vol. 12, No. 1, pp. 90-108, 2016.


Abstract Views: 215

PDF Views: 0




  • Tracing and Recognition of Medicinal Herbs in Marunthuvazh Malai at the Western Ghats Through Feature Extraction

Abstract Views: 215  |  PDF Views: 0

Authors

T. Sahila
Department of Information Technology, University College of Engineering, Nagercoil, India., India
A. Radhakrishnan
Department of Information Technology, University College of Engineering, Nagercoil, India., India
V.A. Nagarajan
Department of Mechanical Engineering University College of Engineering, Nagercoil, India., India

Abstract


The identification and classification of the herbs using the naked eye is difficult in forest or mountain areas like Marunthuvazh Malai of Kanyakumari district. The difficulties arise because of the variations in the crops identified are inaccurate. Mostly the manual prediction is taken place in those areas which require high expertise and more human resources. In this work both plant identification and tracking system based on fuzzy empowered Hybrid artificial neural networks (FHANN) are proposed. Here the input is taken from the video signals taken by the drone camera. The input video signals are converted into images. The fuzzy logic along with the HANN is used for the classification of the specific herbs from the set of plants. Some of the herbs included in the analysis are Parsley, Dill, Oregano, Chervil, Stevia, Basil, Catnip, Fennel and Lemon Grass. This approach used artificial neural networks (ANN) in combination with the K-Nearest neighbor (KNN) as the hybrid model for the herb prediction and classification in association with the fuzzy logic. The Linear Discriminant Analysis (LDA) and Convolutional Autoencoder are used as a hybrid model for the extraction of the feature from the obtained images. This approach considers various shapes, color features, and textures specifically representing the specific herbs. The experimental results show that the proposed model provides better results in the identification and classification of the various medicinal herbs.

Keywords


Artificial Neural Networks, K-Nearest Neighbour, Linear Discriminant Analysis, Convolutional Auto Encoder.

References