ICT in agriculture forecasting and decision support system

Abstract

India is among the top countries as far as agricultural output is concerned and agriculture contributes majorly to the GDP (Gross Domestic Product). Over the years, it has been observed that, despite the availability of best varieties and associated inputs, the attainable yield of many crops is below the expected levels. The combination of mechanical technology and traditional agriculture is taking Indian Agriculture to a new height. However, due to lack of timely and effective information to the farmers, it is becoming difficult to make agriculture an economically viable business. The information about using the resources to the best of their potential at right time is a crucial factor. The resources if not used judiciously leads to the cost making the end user spending more on basic resources. Weather being the uncontrolled variable also plays a major role for crop yield as well as pest infestation in the crop. One of the main and crucial resource in agriculture is irrigation water. With water resources becoming costlier every day, there is a need to use this resource optimally. In this study, Artificial Neural Network based model integrated with other ICT techniques is developed using weather variables or suitable indices for evaporation prediction for irrigation scheduling. The weather variables like temperature, wind speed, relative humidity and sunshine hours were taken as independent variables to predict the dependent variable i.e., evaporation. In the study, an integrated/hybrid two phase algorithm is designed for minimizing the error and increase the accuracy in forecast. The developed model is a logical layer in the development of Decision Support System. Decision Support System provides timely information to the farmers to design crop-wise resource management. The system will provide a dynamic forecast on evaporation based on which irrigation scheduling can be done well in advance which will reduce the cost of cultivation to the farmers. Keywords : Decision Support System, Agriculture, Forewarning Syst