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

arXiv:2404.16844v1 Announce Type: cross
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 …

abstract accessibility arxiv cs.cv cs.lg detection development disease eess.sp health key machine machine learning monitoring research review satellite scale spectroscopy type