Waters, Ethan Kane, Chen, Carla Chia Ming, and Rahimi Azghadi, Mostafa (2025) Sugarcane health monitoring with satellite spectroscopy and machine learning: A review. Computers and Electronics in Agriculture, 229. 109686.

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Abstract

Research into large-scale crop monitoring has flourished due to increased accessibility to satellite imagery. This review delves into previously unexplored and under-explored areas in sugarcane health monitoring and disease/pest detection using satellite-based spectroscopy and Machine Learning (ML). It discusses key considerations in system development, including relevant satellites, vegetation indices, ML methods, factors influencing sugarcane reflectance, optimal growth conditions, common diseases, and traditional detection methods. Many studies highlight how factors like crop age, soil type, viewing angle, water content, recent weather patterns, and sugarcane variety can impact spectral reflectance, affecting the accuracy of health assessments via spectroscopy. However, these variables have not been fully considered in the literature. In addition, the current literature lacks comprehensive comparisons between ML techniques and vegetation indices. This review addresses these gaps and discusses that, while current findings suggest the potential for an ML-driven satellite spectroscopy system for monitoring sugarcane health, further research is essential. This paper offers a comprehensive analysis of previous research to aid in unlocking this potential and advancing the development of an effective sugarcane health monitoring system using satellite technology.

Item ID:	84449
Item Type:	Article (Research - C1)
ISSN:	1872-7107
Keywords:	Sugarcane, Health monitoring system, Remote sensing, Satellites, Spectroscopy, Machine learning, Vegetation indices, Disease, Pests
Copyright Information:	© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Date Deposited:	20 Jan 2025 05:03
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