Crop Yield Prediction Using Improved Extreme Learning Machine

Abstract

ABSTRACT Crop yield prediction (CYP) is a critical challenge for decision-makers of any kind of levels, including regional and national decision-making. Farmers might use effective CYP model to help them determine what to cultivate or when to grow it. The primary aim of precision agriculture (PA) is to increase crop growth and yield thereby decreasing production costs and emissions. Various developmental factors influence potential yield, including soil properties, irrigation, weather, fertilizer maintenance, and topography. CYP is critical to worldwide agricultural production. To improve the global food security, policymakers depend on reliable forecasts to make timely exports and imports decision. The main aim of this paper is to predict the crop yield using machine-learning technique. The collected data are pre-processed using Kalman filter algorithm and then certain features are extracted using the Linear Discriminant Analysis (LDA). The CYP is done using the improved extreme learning machine (IELM). The output weight of the extreme learning machine is improved using the chimp optimization algorithm (COA). The implementation tool used in this method is PYTHON and the dataset used for the CYP is farm yield prediction. The experimental results showed an accuracy of 99.99% which is better when compared to existing methods.