Data Analytics on Agrometeorological Parameters for Building a Utility System for Farmer Community
Sowmya BJ1, Gautam Mundada2, Pranav Hegde3, Seema S4, K G Srinivasa5 

1Sowmya B J, Assistant Professor, Department of Computer Science and Engineering, M S Ramaiah Institute of Technology, Bangalore, (Karnataka), India.
2Gautam Mundada, Student at Ramaiah Institute of Technology, Bengaluru, (Karnataka), India.
3Pranav Hegde, B.E Student, Ramaiah Institute of Technology, Bengaluru, (Karnataka), India.
4Dr. Seemas, Professor, Department of Computer Science and Engineering, M S Ramaiah Institute of Technology, Bangalore, (Karnataka), India.
5Dr. K G Srinivasa, Professor, Department of National Institute of Technical Teacher Training & Research, Chandigarh, Chandigarh, (Punjab), India. 

Manuscript received on 09 March 2019 | Revised Manuscript received on 17 March 2019 | Manuscript published on 30 July 2019 | PP: 3223-3230 | Volume-8 Issue-2, July 2019 | Retrieval Number: B2676078219/19©BEIESP | DOI: 10.35940/ijrte.B2676.078219
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Abstract: The day-to-day evolution of the world is throwing many challenges, thereby demanding the humans to be on par with the modern tools and technologies that are on offer. These technologies are contributing to the overall growth across all the domains. One such domain that is highly affected by the modern tools and techniques is Agriculture. Gone are the days, when only a handful methodologies and tools were utilized to understand about agriculture. Although the farmers are sceptical to the modernization, the end result has been encouraging at many a times. With the use of analytics, one can get to know more about the type of soil, crops and fertilizers, amount of water to be utilized depending on the climatic conditions and thereby have an effective yield. In this work, the elementary task of portraying the effect of climatic conditions on the production of different varieties of crops is carried out. Also, the application of Multivariate Linear Regression and Artificial Neural Network (ANN) techniques provides a significance outcome in the prediction of yield with the focus on Ragi and Rice. The work provides an accuracy of 65% with the 3-fold cross validation technique and 68% accuracy with ANN model.
Keywords: Agrometeorology, Utility System, ANN, Linear Regression, Humidity, Cross Validation.

Scope of the Article: Microwave Absorption