Data Analytic Techniques for Developing Decision Support System on Agrometeorological Parameters for Farmers

Data Analytic Techniques for Developing Decision Support System on Agrometeorological Parameters for Farmers

Sowmya B.J. (M. S. Ramaiah Institute of Technology, Bengaluru, India), Krishna Chaitanya S. (M.S. Ramaiah Institute of Technology, Bengaluru, India), Seema S. (M.S. Ramaiah Institute of Technology, Bengaluru, India) and K.G. Srinivasa (National Institute of Technical Teachers Training and Research, Chandigarh, India)
DOI: 10.4018/IJCINI.2020040106


The day-to-day lives of humans are changing remarkably due to the evolution in tools, techniques and technology across the planet. This evolution is not only impacting the growth of humans but also contributing to the growth and well-being of society and country. The domain of data analytics (DA) and internet of things (IoT) is very much facilitating this growth. But there have been only a handful of innovations and explorations in the field of agriculture, although it being the backbone and largely contributing to the gross domestic product (GDP) of a country like India. The reason for it may be profuse, such as the erratic weather conditions, improper irrigation, farmers being skeptical using modern tools and many more. But being in a developing country that has its primary focus on invention and innovation, a consensus has to be reached so that the modern tools and technologies, abet agriculture throughout the country. In our work, an attempt is made to analyze the different aspects that influences the variable outcomes in agriculture with the aid of various data analytic algorithms. Rainfall, humidity and temperature are some of the variables that determine the type of crop. Therefore, the task of prediction of crop type given these factors using decision trees and support vector machines (SVM) is implemented, and the accuracy of the models are computed. Here, more focus is given to the state of Karnataka and to its major crops. With rice, ragi and maize being some of the predominant crops, an analysis is portrayed considering the yield across the state.
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Before diving into the actual implementation of the Agricultural decision support system, there are some noteworthy related works that have already made an impact around the globe in the field of agriculture. Some of them are pictured below that uses different data analytic techniques to solve the existing problems.

The rudimentary task of an agriculture decision support system would be the crop type and yield prediction. Sujatha and Isakki in their work make use of different classification algorithms such as Naïve Bayes and Decision tree for forecasting the crop yield (Sujatha and Isakki, 2016). The system proposed by them contains an input module (comprising of crop name, land area, soil type, soil pH, pest details, weather, water level, seed type) and feature selection unit (minimizes the selection set of an attribute based on crop details). Eventually, after feature selection the data is grouped with similar contents on the basis of defined classification rules, which are then implemented to categorize the crop depending on the name, yield and pesticide.

Similarly, Vijayabaskara et al. (2017) in their work use predictive analytics (numerous statistical techniques to interpret historic and present data and forecast based on it) to predict the crop. An application to test the soil fertility, suggest or predict the crop and its yields, and recommendations related to fertilizers is provided and as the prediction depends on the atmosphere which is not constant, the results may vary accordingly.

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