  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. We address these gaps
in this review. We discuss 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.
