Floriculture classification using simple neural network and deep learning

Shrikant Dharwadkar; Ganesh Bhat; N. V. Subba Reddy; Prakash Kalingrao Aithal

doi:10.1109/RTEICT.2017.8256671

Floriculture classification using simple neural network and deep learning

Shrikant Dharwadkar, Ganesh Bhat, N. V. Subba Reddy, Prakash Kalingrao Aithal

Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

10 Citations (Scopus)

Abstract

This paper presents an approach based on deep learning for identification and classification of flowers to aid in domains such as patent analysis of flowers and floriculture industry. The Deep convolutional network using its pre-Trained knowledge shows the potential for accurate identification of flowers than the present existing approaches for image classification. The study reveals that the use of deep learning neural network comparatively increases the accuracy to identify the flowers consisting of high semantic features over the simple neural network built from scratch.

Original language	English
Title of host publication	RTEICT 2017 - 2nd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	619-622
Number of pages	4
Volume	2018-January
ISBN (Electronic)	9781509037049
DOIs	https://doi.org/10.1109/RTEICT.2017.8256671
Publication status	Published - 12-01-2017
Event	2nd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2017 - Bangalore, India Duration: 19-05-2017 → 20-05-2017

Conference

Conference	2nd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2017
Country/Territory	India
City	Bangalore
Period	19-05-17 → 20-05-17

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Computer Science Applications
Media Technology
Control and Optimization
Instrumentation
Transportation
Communication

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10.1109/RTEICT.2017.8256671

Cite this

Dharwadkar, S., Bhat, G., Subba Reddy, N. V., & Aithal, P. K. (2017). Floriculture classification using simple neural network and deep learning. In RTEICT 2017 - 2nd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, Proceedings (Vol. 2018-January, pp. 619-622). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/RTEICT.2017.8256671

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abstract = "This paper presents an approach based on deep learning for identification and classification of flowers to aid in domains such as patent analysis of flowers and floriculture industry. The Deep convolutional network using its pre-Trained knowledge shows the potential for accurate identification of flowers than the present existing approaches for image classification. The study reveals that the use of deep learning neural network comparatively increases the accuracy to identify the flowers consisting of high semantic features over the simple neural network built from scratch.",

author = "Shrikant Dharwadkar and Ganesh Bhat and {Subba Reddy}, {N. V.} and Aithal, {Prakash Kalingrao}",

year = "2017",

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Dharwadkar, S, Bhat, G, Subba Reddy, NV & Aithal, PK 2017, Floriculture classification using simple neural network and deep learning. in RTEICT 2017 - 2nd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, Proceedings. vol. 2018-January, Institute of Electrical and Electronics Engineers Inc., pp. 619-622, 2nd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2017, Bangalore, India, 19-05-17. https://doi.org/10.1109/RTEICT.2017.8256671

Floriculture classification using simple neural network and deep learning. / Dharwadkar, Shrikant; Bhat, Ganesh; Subba Reddy, N. V. et al.

RTEICT 2017 - 2nd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, Proceedings. Vol. 2018-January Institute of Electrical and Electronics Engineers Inc., 2017. p. 619-622.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - This paper presents an approach based on deep learning for identification and classification of flowers to aid in domains such as patent analysis of flowers and floriculture industry. The Deep convolutional network using its pre-Trained knowledge shows the potential for accurate identification of flowers than the present existing approaches for image classification. The study reveals that the use of deep learning neural network comparatively increases the accuracy to identify the flowers consisting of high semantic features over the simple neural network built from scratch.

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PB - Institute of Electrical and Electronics Engineers Inc.

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Dharwadkar S, Bhat G, Subba Reddy NV , Aithal PK. Floriculture classification using simple neural network and deep learning. In RTEICT 2017 - 2nd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, Proceedings. Vol. 2018-January. Institute of Electrical and Electronics Engineers Inc. 2017. p. 619-622 doi: 10.1109/RTEICT.2017.8256671

Floriculture classification using simple neural network and deep learning

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