Expert System for Crop Selection

Authors(4) :-Somnath Devadhe, Abhijit Kausadikar, Pratik Daphal, Akshay Joshi

Despite technological advancements, farming sector in India remains un-organized. Agriculture of India needs essential changes with respect to its current social, geographic and economic trends by the combination of practical knowledge gathered over generations and the scientific basis. There is a need to adapt artificial intelligence in agricultural sector due to its promising results in fields ranging from medical science to automated machines. With the help of artificial intelligence, computations on historical data can be performed to predict crop productions. To predict the productivity of crops, this paper demonstrates the use of various machine-learning techniques such as linear regression, decision tree and random forest. Data visualization techniques are used to present region wise patterns of crop productivity using HTML5, AngularJS etc.

Authors and Affiliations

Somnath Devadhe
Department of Computer Engineering, KKWIEER, Nashik, Maharashtra, India
Abhijit Kausadikar
Department of Computer Engineering, KKWIEER, Nashik, Maharashtra, India
Pratik Daphal
Department of Computer Engineering, KKWIEER, Nashik, Maharashtra, India
Akshay Joshi
Department of Computer Engineering, KKWIEER, Nashik, Maharashtra, India

Artificial Intelligence, Visualization, Machine Learning

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Publication Details

Published in : Volume 3 | Issue 3 | March-April 2017
Date of Publication : 2017-04-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 436-438
Manuscript Number : IJSRST1733155
Publisher : Technoscience Academy

Print ISSN : 2395-6011, Online ISSN : 2395-602X

Cite This Article :

Somnath Devadhe, Abhijit Kausadikar, Pratik Daphal, Akshay Joshi, " Expert System for Crop Selection", International Journal of Scientific Research in Science and Technology(IJSRST), Print ISSN : 2395-6011, Online ISSN : 2395-602X, Volume 3, Issue 3, pp.436-438, March-April-2017.
Journal URL : http://ijsrst.com/IJSRST1733155

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