Machine Learning for Asymptomatic Ratoon Stunting Disease Detection With Freely Available Satellite Based Multispectral Imaging

TL;DR

This study demonstrates that machine learning models, especially SVM-RBF, can effectively detect asymptomatic Ratoon Stunting Disease in sugarcane using freely available satellite multispectral data, offering a scalable alternative to manual testing.

Contribution

It introduces a satellite-based remote sensing approach combined with machine learning for large-scale, cost-effective detection of RSD in sugarcane varieties, advancing current disease monitoring methods.

Findings

SVM-RBF achieved up to 96.55% accuracy.

Vegetation indices and sugarcane variety are key factors.

Remote sensing can replace manual testing for disease detection.

Abstract

Disease detection in sugarcane, particularly the identification of asymptomatic infectious diseases such as Ratoon Stunting Disease (RSD), is critical for effective crop management. This study employed various machine learning techniques to detect the presence of RSD in different sugarcane varieties, using vegetation indices derived from freely available satellite-based spectral data. Our results show that the Support Vector Machine with a Radial Basis Function Kernel (SVM-RBF) was the most effective algorithm, achieving classification accuracy between 85.64% and 96.55%, depending on the variety. Gradient Boosting and Random Forest also demonstrated high performance achieving accuracy between 83.33% to 96.55%, while Logistic Regression and Quadratic Discriminant Analysis showed variable results across different varieties. The inclusion of sugarcane variety and vegetation indices was important in the detection of RSD. This agreed with what was identified in the current literature. Our study highlights the potential of satellite-based remote sensing as a cost-effective and efficient method for large-scale sugarcane disease detection alternative to traditional manual laboratory testing methods.