Forecasting Crop Yield Using Discrete Wavelet Transform and Deep Neural Networks

Abstract

Agriculture is undergoing a metamorphosis due to several environmantal and scoal factors. Due to challenges such as global warming, intermittent rainfall patterns and eroding nutrient values of soil, crop yileds have become more upredictable in the last decade. This has resulted in famines, armer suicides and deaths due to hunger. Thus, one of the key objectives of the world health organization is to provide food security globally and also help the agriculture community as a whole whith special emhpasis on low income group countries. This has made crop yield forecasting extremely important. As the crop yield depends on several factors which are highly uncorrelated in nature, hence machine learing based appraoches have been employed for the purpose. In this paper a deep neural network approach has been proposed along with the discrete wavelet transfrom to forecast crop yields. The wavelet transform has been used as a filtering techniques to remove local disturbances from the data, and deep neural networks have been used for pattern recognition and forecasting. The evaluation of the proposed system has been evaluated in terms of the mean absolute percentage error, accuracy and regression. It has been found that the proposed work outperforms existing baseline techniques in terms of the accuracy of forecasting.