Waters, Ethan Kane, Chen, Carla Chia-Ming, and Rahimi Azghadi, Mostafa (2025) Machine learning for asymptomatic ratoon stunting disease detection with freely available satellite based multispectral imaging. Information Processing in Agriculture. (In Press)

Preview

PDF (Publisher Accepted Version) - Published Version Available under License Creative Commons Attribution. Download (3MB) | Preview

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.

Item ID:	90190
Item Type:	Article (Research - C1)
ISSN:	2214-3173
Keywords:	SugarcaneHealth monitoring systemRemote sensingSatellite-based spectroscopyMachine learningVegetation indicesDisease detection
Copyright Information:	© 2025 The Authors. Published by Elsevier B.V. on behalf of China Agricultural University. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Date Deposited:	20 May 2026 02:58
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460209 Planning and decision making @ 30% 46 INFORMATION AND COMPUTING SCIENCES > 4611 Machine learning > 461199 Machine learning not elsewhere classified @ 40% 30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3004 Crop and pasture production > 300409 Crop and pasture protection (incl. pests, diseases and weeds) @ 30%
SEO Codes:	28 EXPANDING KNOWLEDGE > 2801 Expanding knowledge > 280101 Expanding knowledge in the agricultural, food and veterinary sciences @ 50% 26 PLANT PRODUCTION AND PLANT PRIMARY PRODUCTS > 2606 Industrial crops > 260607 Sugar @ 50%
	More Statistics