IEEE Style Citation: Rupesh Bante, Dipak Nakhate, Praful Gade, Saurabh Tagde, Swati Dixit, Gopal Sakarkar, Sofia Pillai, “Image Analysis and Classification of Flower Using Machine Learning Algorithm for Creating Organic Color,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1053-1058, 2019.
MLA Style Citation: Rupesh Bante, Dipak Nakhate, Praful Gade, Saurabh Tagde, Swati Dixit, Gopal Sakarkar, Sofia Pillai "Image Analysis and Classification of Flower Using Machine Learning Algorithm for Creating Organic Color." International Journal of Computer Sciences and Engineering 7.3 (2019): 1053-1058.
APA Style Citation: Rupesh Bante, Dipak Nakhate, Praful Gade, Saurabh Tagde, Swati Dixit, Gopal Sakarkar, Sofia Pillai, (2019). Image Analysis and Classification of Flower Using Machine Learning Algorithm for Creating Organic Color. International Journal of Computer Sciences and Engineering, 7(3), 1053-1058.
BibTex Style Citation:
@article{Bante_2019,
author = {Rupesh Bante, Dipak Nakhate, Praful Gade, Saurabh Tagde, Swati Dixit, Gopal Sakarkar, Sofia Pillai},
title = {Image Analysis and Classification of Flower Using Machine Learning Algorithm for Creating Organic Color},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {3 2019},
volume = {7},
Issue = {3},
month = {3},
year = {2019},
issn = {2347-2693},
pages = {1053-1058},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=3964},
doi = {https://doi.org/10.26438/ijcse/v7i3.10531058}
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i3.10531058}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=3964
TI - Image Analysis and Classification of Flower Using Machine Learning Algorithm for Creating Organic Color
T2 - International Journal of Computer Sciences and Engineering
AU - Rupesh Bante, Dipak Nakhate, Praful Gade, Saurabh Tagde, Swati Dixit, Gopal Sakarkar, Sofia Pillai
PY - 2019
DA - 2019/03/31
PB - IJCSE, Indore, INDIA
SP - 1053-1058
IS - 3
VL - 7
SN - 2347-2693
ER -
VIEWS
PDF
XML
1343
253 downloads
187 downloads
Abstract
This paper presents a mechanism for flower species identification using machine learning’s regression algorithm. The main objective behind this proposed approach is to utilize flowers and garlands contributing towards the waste management of temples in rural as well as urban areas. The proposed approach provides both social and environmental aspect as the waste flowers can be used for vermicomposting to improve the quality of soil as well as the other types of flowers and can be used for creating organic colour and dyes.
Key-Words / Index Term
Flower Species Identification, Machine Learning Algorithm, Regression Algorithm.
References
[1] Rattan S.S. “Thoery of Machines”, 3rd Edition, Mc Graw Hill pp.234-238, 2009.
[2] Goldy Katal, Saahil Gupta, Shitij Kakkar, “Design And Operation Of Synchronized Robotic Arm”, International Journal of Research in Engineering and Technology, Volume: 02 Issue: 08, Aug-2013.
[3] Szabo R.; Lie I. “Automated colored object sorting application for robotic arms” Electronics and Telecommunications (ISETC) 2012 10th International Symposium on vol. no. pp.95 98 15-16 Nov. 2012.
[4] T. P. Tho, N. T. Thinh and N. H. Bich, “Design and Development of the Vision Sorting System”, International Conference on Green Technology and Sustainable Development (GTSD), Kaohsiung. 2016.
[5] Yeow Khang Yung, ‟Color Sorting System with Robot Arm’’ Faculty of Electronic and Computer Engineering University Technical Malaysia Melaka, (2011).
6] P. P. Chavan, S. M. Kale, P. P. Gaikwad and S. M. Ghadge, “Intelligent Object Sorting Insolent System”. International Conference on Pervasive Computing (ICPC), Pune. 2015.
[7] V. Pereira, V. A. Fernandes and J. Sequeira, “Low cost object sorting robotic arm using Raspberry Pi”. IEEE Global Humanitarian Technology Conference - South Asia Satellite (GHTC-SAS), Trivandrum. 2014.
[8] Rafael C. Gonzalez and Richard E. Woos, “Digital Image Processing”, third edition vol.3. 2003
VIEWS
PDF 
XML 
