Krishimitr: A Cloud Computing and Platform for Disease Detection in Agriculture
Parul Sharma1, YashPaul Singh Berwal2, Wiqas Ghai3

1Parul Sharma*, PhD Research Scholar, RIMT University, Mandi Gobindgarh, Country.
2Dr Y P S berwal Additional Director, Haryana Technical Education, Gobindgarh.
3Dr. Wiqas Ghai , RIMT University, Mandi Gobindgarh.

Manuscript received on September 14, 2019. | Revised Manuscript received on 22 September, 2019. | Manuscript published on October 10, 2019. | PP: 2967-2970 | Volume-8 Issue-12, October 2019. | Retrieval Number: K19550981119/2019©BEIESP | DOI: 10.35940/ijitee.K1955.1081219
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Abstract: Automating disease detection is a cornerstone in the journey to achieving sustainable agriculture. We describe a framework utilizing Machine Learning, Cloud Computing and Internet-of-Things which brings experts to farmers, allowing for timely detection of diseases. This innovative and comprehensive framework provides agronomists and farmers with a solution for diagnosing plant diseases. By leveraging modern ICT capabilities, this extensible framework is currently trained for over 15 plant types and more than 51 disease types. Our framework employs a hybrid model combining use of both online and offline resources to provide up-to-date information to farmers even in case of patchy connectivity.
Keywords: Machine Learning, Cloud Computing, Internet-of-Things, Plant Disease Detection.
Scope of the Article: Machine Learning