Improvised Extreme Learning Machine for Crop Yield Prediction
Swati Vashisht, Praveen Kumar, Munesh Chandra Trivedi
Abstract
Machine learning is effectively involved in crop yield prediction as a decision support tool including which crops to grow in a specific region and how to increase their yield in the reaping and growing season of the crops. A number of deep learning techniques, neural network architectures and prediction models have been employed to assist crop yield prediction research. This study proposes extreme learning machine to predict crop yield, we performed data pre-processing using Kalman Filter Algorithm, certain features have been extracted using Linear Discriminant Analysis and crop prediction is done by using an improved version of Extreme Linear Machine. Yield of rice crop has been predicted based upon geography, season and area of cultivation.