Sugarcane Health Monitoring

Sugarcane health monitoring aims to improve yield and quality by enabling early and accurate detection of diseases and pests. Current research focuses on developing robust machine learning models, including ensemble methods leveraging pre-trained convolutional neural networks and optimized weighted averaging techniques, often applied to leaf imagery for disease classification. Additionally, research explores the use of satellite spectroscopy combined with machine learning algorithms to monitor sugarcane health at a larger scale, though challenges remain in accounting for environmental factors influencing spectral reflectance. These advancements hold significant potential for optimizing sugarcane cultivation practices and improving global sugar production.