Title

Rice crop yield forecasting of tropical wet and dry climatic zone of India using data mining techniques

Document Type

Conference Proceeding

Publisher

IEEE

School

SChool of Science

Comments

Originally published as:

Gandhi, N., & Armstrong, L. J. (2016, October). Rice crop yield forecasting of tropical wet and dry climatic zone of India using data mining techniques. In Advances in Computer Applications (ICACA), IEEE International Conference on (pp. 357-363). IEEE.

Original paper available here

Abstract

Data mining is the process of identifying the hidden patterns from large and complex data. It may provide crucial role in decision making for complex agricultural problems. Data visualisation is also equally important to understand the general trends of the effect of various factors influencing the crop yield. The present study examines the application of data visualisation techniques to find correlations between the climatic factors and rice crop yield. The study also applies data mining techniques to extract the knowledge from the historical agriculture data set to predict rice crop yield for Kharif season of Tropical Wet and Dry climatic zone of India. The data set has been visualised in Microsoft Office Excel using scatter plots. The classification algorithms have been executed in the free and open source data mining tool WEKA. The experimental results provided include sensitivity, specificity, accuracy, F1 score, Mathews correlation coefficient, mean absolute error, root mean squared error, relative absolute error and root relative squared error. General trends in the data visualisation show that decrease in precipitation in the selected climatic zone increases the rice crop yield and increase in minimum, average or maximum temperature for the season increases the rice crop yield. For the current data set experimental results show that J48 and LADTree achieved the highest accuracy, sensitivity and specificity. Classification performed by LWL classifier displayed the lowest accuracy, sensitivity and specificity results.

DOI

10.1109/ICACA.2016.7887981

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