CSgI: A Deep Learning based approach for Marijuana Leaves Strain Classification

Rajora, S; Kumar Vishwakarma, D; Singh, K; Prasad, M

CSgI: A Deep Learning based approach for Marijuana Leaves Strain Classification

Rajora, S Kumar Vishwakarma, D Singh, K Prasad, M

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Publication Type:: Conference Proceeding
Citation:: 2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2018, 2019, pp. 209 - 214
Issue Date:: 2019-01-16

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Field	Value	Language
dc.contributor.author	Rajora, S	en_US
dc.contributor.author	Kumar Vishwakarma, D	en_US
dc.contributor.author	Singh, K	en_US
dc.contributor.author	Prasad, M https://orcid.org/0000-0001-9719-2253	en_US
dc.date.issued	2019-01-16	en_US
dc.identifier.citation	2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2018, 2019, pp. 209 - 214	en_US
dc.identifier.isbn	9781538672662	en_US
dc.identifier.uri	http://hdl.handle.net/10453/132454
dc.description.abstract	© 2018 IEEE. This paper proposes a novel approach that classifies the images of various marijuana/cannabis leaves into their respective classes of strains and types. The proposed architecture works on a two-fold technique which when implemented in the requisite sequence delivers phenomenal results to the classification problem statement. The first fold, being the segmentation or foreground extraction in the images, focuses on extracting the RDI (Region of Interest) using a robust segmentation algorithm which can suitable separate the foreground from the image; and the second fold, being the Deep Learning aspect focuses on the result classification task. This literature gives a quantitative analysis of implementing this classification problem vide a transfer learning paradigm (for application instances with less training data in hand) training the entire CNN archetype from scratch (for application instances with sufficient training data in hand). Thus, altogether the proposed methodology distinctively deploys ConvNets for the posed classification problem having dual aspects of approaches implementation wiz: a) Transfer Learning b) Training the entire CNN from scratch. The novelty of the proposed work can be counted upon as the construction of a robust algorithm very first of its kind in this respective application domain which is potent enough to render the correct class label of the strain/type of marijuana or cannabis leaf image when fed to the system for classification.	en_US
dc.relation.ispartof	2018 IEEE 9th Annual Information Technology, Electronics and Mobile Communication Conference, IEMCON 2018	en_US
dc.relation.isbasedon	10.1109/IEMCON.2018.8615011	en_US
dc.title	CSgI: A Deep Learning based approach for Marijuana Leaves Strain Classification	en_US
dc.type	Conference Proceeding
utslib.for	080108 Neural, Evolutionary and Fuzzy Computation	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

© 2018 IEEE. This paper proposes a novel approach that classifies the images of various marijuana/cannabis leaves into their respective classes of strains and types. The proposed architecture works on a two-fold technique which when implemented in the requisite sequence delivers phenomenal results to the classification problem statement. The first fold, being the segmentation or foreground extraction in the images, focuses on extracting the RDI (Region of Interest) using a robust segmentation algorithm which can suitable separate the foreground from the image; and the second fold, being the Deep Learning aspect focuses on the result classification task. This literature gives a quantitative analysis of implementing this classification problem vide a transfer learning paradigm (for application instances with less training data in hand) training the entire CNN archetype from scratch (for application instances with sufficient training data in hand). Thus, altogether the proposed methodology distinctively deploys ConvNets for the posed classification problem having dual aspects of approaches implementation wiz: a) Transfer Learning b) Training the entire CNN from scratch. The novelty of the proposed work can be counted upon as the construction of a robust algorithm very first of its kind in this respective application domain which is potent enough to render the correct class label of the strain/type of marijuana or cannabis leaf image when fed to the system for classification.

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http://hdl.handle.net/10453/132454