Sugarcane Health Monitoring With Satellite Spectroscopy and Machine Learning: A Review

TL;DR

This review explores the use of satellite spectroscopy combined with machine learning for large-scale sugarcane health monitoring, highlighting current gaps and proposing directions for future research to improve accuracy and system development.

Contribution

It identifies key variables affecting spectral reflectance and compares ML techniques and vegetation indices, addressing gaps in existing literature for better sugarcane health assessment.

Findings

Satellite spectroscopy shows potential for sugarcane health monitoring.

Factors like crop age and soil type influence spectral reflectance.

Further research needed to optimize ML methods and indices.

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. 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.