Refine your search
Collections
Co-Authors
Year
A B C D E F G H I J K L M N O P Q R S T U V W X Y Z All
Parikh, Ajay
- Indian Ayurvedic Plant Identification Using Multi Organ Image Analytics
Abstract Views :139 |
PDF Views:1
Authors
Meera Kansara
1,
Ajay Parikh
1
Affiliations
1 Department of Computer Science, Gujarat Vidyapith, IN
1 Department of Computer Science, Gujarat Vidyapith, IN
Source
ICTACT Journal on Image and Video Processing, Vol 13, No 1 (2022), Pagination: 2806-2810Abstract
Medicinal Plants are the main resource base of Indian autochthonic health care traditions called Ayurveda. The general use of ayurvedic preparation is also common without ill effects. As advancement in Image Processing, Image Analytics techniques transpiring, researchers in the machine learning and computer vision fields are striving to achieve accurate automatic plant identification and classification. This paper focuses on the Automatic Indian Ayurvedic plant identification based on muti organ images analytics. A lot of research work has been carried out for the identification of plants by their leaves, this research carries out multi organ-based identification of Indian Medicinal plants. This paper proposes IMPINet which is a network developed for Indian Medicinal Plant Identification. IMPINet is a non-sequential deep network having multiple convolutions at the same level. A novel approach for multiorgan based plant identification is also proposed where final accuracy is calculated by score-based fusion. Comparison of IMPINet has been carried out with the state of art networks and performance of IMPINet is evaluated on benchmark dataset Flavia.Keywords
Indian Medicinal Plant Identification, Image Analytics, Multi-Organ based Plant Identification, Deep Learning, Image Dataset.References
- D. Venkataraman and N. Mangayarkarasi, “Support Vector Machine based Classification of Medicinal Plants using Leaf Features”, Proceedings of International Conference on Advances in Computing, Communications and Informatics, pp. 793-798, 2017.
- A. Gokhale, S. Babar, S. Gawade and S. Jadhav, “Identification of Medicinal Plant using Image Processing and Machine Learning”, Proceedings of International Conference on Applied Computer Vision and Image Processing, pp. 272-282, 2020.
- M.R. Dileep and P.N. Pournami, “Ayurleaf: A Deep Learning Approach for Classification of Medicinal Plants”, Proceedings of International Conference on Advances in Computing and Communications, pp. 321-325, 2019.
- P. Kumar, C.M. Surya and V. P. Gopi, “Identification of Ayurvedic Medicinal Plants by Image Processing of Leaf Samples”, Proceedings of International Conference on Research in Computational Intelligence and Communication Networks, pp. 231-238, 2017.
- S. Aggarwal, R. Madaan and M. Bhatia, “Morphological based Optimized Random Forest classification for Indian Oxygen Plants”, International Journal on Emerging Technologies, Vol. 11, No. 3, pp. 707-714, 2020.
- T. Gaber, A.Tharwat, V. Snasel and A.E. Hassanien, “Plant Identification: Two Dimensional-Based vs. One Dimensional-based Feature Extraction Methods”, Proceedings of International Conference on Soft Computing Models in Industrial and Environmental Applications, pp. 375-385, 2015.
- A. Rao and S. Kulkarni, “An Improved Technique of Plant Leaf Classification using Hybrid Feature Modeling”, Proceedings of International Conference on Innovative Mechanisms for Industry Applications, pp. 5-9, 2017.
- V.M. Araujo, A.S. Britto, A.L. Brun, A.L. Koerich and L.E. Oliveira, “Fine-Grained Hierarchical Classification of Plant Leaf Images using Fusion of Deep Models”, Proceedings of International Conference on Tools with Artificial Intelligence, pp. 1-5, 2018.
- V.M. Araujo, A.S. Britto, L.S. Oliveira and A.L. Koerich, “Two-View Fine-Grained Classification of Plant Species”, Neurocomputing, Vol. 467, pp. 427-441, 2020.
- M. Kansara and A. Parikh, “Indian Ayurvedic Plant Identification using Multi-Organ Image Analytics: Creation of Image Dataset of Indian Medicinal Plant Organs”, Proceedings of the International Conference on Innovative Computing and Communications, pp. -1-6, 2020.
- P. Pawara, E. Okafor, L. Schomaker and M. Wiering, “Data Augmentation for Plant Classification”, Proceedings of International Conference on Advanced Concepts for Intelligent Vision Systems, pp. 615-626, 2017.
- X. Zhang and Y. LeCun,. “Universum Prescription: Regularization using Unlabeled Data”, Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 31, No. 1, pp. 1-14, 2017.
- K. Kim, E.H. Kennedy and A.I. Naimi, “Incremental Intervention Effects in Studies with Dropout and Many Timepoints”, Journal of Causal Inference, Vol. 9 No. 1, pp. 302-344, 2021.
- K. Simonyan and A. Zisserman, “Very Deep Convolutional Networks for Large-Scale Image Recognition”, Proceedings of International Conference on Advances in Computing, Communications and Informatics, pp. 1-12, 2014.
- A.G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto and H. Adam, “Mobilenets: Efficient Convolutional Neural Networks for Mobile Vision Applications”, Proceedings of International Conference on Advanced Concepts for Intelligent Vision Systems, pp. 523-529, 2017.
- S.G. Wu, F.S. Bao, E. Y Xu, Y.X. Wang, Y.F. Chang and L. Xiang, “A Leaf Recognition Algorithm for Plant Classification using Probabilistic Neural Network”, Proceedings of International Conference on Signal Processing and Information Technology, pp. -1-6, 2007.