Application of Machine Learning on Remote Sensing Data for Sugarcane Crop Classification: A Review

Abstract

Sugarcane is a major contributing component in the economy of tropical and subtropical countries like India, Brazil and China. Sugarcane agriculture is empowered with the advancements in the remote sensing technology because of its timely, non invasive, and labor and cost effective capability. Remote sensing data with machine learning algorithms like Support Vector Machine, Artificial Neural Network and Random Forest are proven to be suitable in sugarcane agriculture. The aim of this paper is to present a review of studies that implemented various machine learning algorithms based on remote sensing data in sugarcane crop mapping and classification.