This paper is published in Volume-4, Issue-2, 2018
Area
Data Mining, Agricultural Data
Author
Amiksha Patel
Co-authors
Dr. Dhaval R. Kathiriya
Org/Univ
Charotar University of Science and Technology, Changa, Gujarat., India
Pub. Date
05 April, 2018
Paper ID
V4I2-1593
Publisher
Keywords
Data mining, Cotton crop, Yield prediction, Temperature, Rainfall.

Citationsacebook

IEEE
Amiksha Patel, Dr. Dhaval R. Kathiriya. A data mining perspective of the dual effect of rainfall and temperature on cotton crop yield prediction, International Journal of Advance Research, Ideas and Innovations in Technology, www.IJARIIT.com.

APA
Amiksha Patel, Dr. Dhaval R. Kathiriya (2018). A data mining perspective of the dual effect of rainfall and temperature on cotton crop yield prediction. International Journal of Advance Research, Ideas and Innovations in Technology, 4(2) www.IJARIIT.com.

MLA
Amiksha Patel, Dr. Dhaval R. Kathiriya. "A data mining perspective of the dual effect of rainfall and temperature on cotton crop yield prediction." International Journal of Advance Research, Ideas and Innovations in Technology 4.2 (2018). www.IJARIIT.com.

Abstract

This paper presents the final investigation within the north Gujarat region of qualitative and quantitative investigations carried out for the processing and analysis of geographic land-usage data in an agricultural context. The geographic data was made up of crop and cotton cropland use profile. These were linked to previously recorded climatic data from fixed weather stations in north Gujarat. In this study, the profiles for the stochastic average monthly temperature and rainfall for north Gujarat selected area were used to determine their simultaneous effects on crop production. The temperature and rainfall were sampled for a selected decade of crop production for the years from 2006 to 2015. The evaluation was carried out using graphical, correlational and data-mining-regression techniques to detect the patterns of crop production in response to the climatic effect across the agricultural region. Data mining classification algorithms within the WEKA software package were used with the location as the classifier to make comparisons between predicted and actual cotton yields. The predicted patterns suggested that crop production is affected by the climate variability especially at certain stages of plant growth.  
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