An Exploratory Study of Applications of Machine Learning in Crop Yield Prediction: A Review

Abstract

Agricultural yield prediction automation is an important and emerging subject for all over the world. Population is increasing at very fast rate and with increase in inhabitants the requirement of agricultural products increases significantly high. In earlier days, conventional methods used by farmers to predict the crops and those methods aren’t sufficient to serve the increasing need of industrial planning. The development in Remote Sensing (RS) and Machine Learning (ML) techniques saved the efforts in gathering ground data with improvement in accuracy in prediction of yield. The properties of ML techniques work on non-linear data which is available freely with remote sensing platform. The remote sensing data showed superior performance in many agricultural applications. In this paper, Crop yield predicting through machine learning using different data such as using ground data, using Remote sensing and Using UAV are compared to determine the best method. It has found that later two are more efficient and time saving as compared to the traditional method of using ground data.

Keywords: Crop yield, Machine learning, Remote sensing, UAV