Detection of Net Blotch Disease of Barley Using UAV-Based RGB and Multispectral Imagery at Plot Scale

Net blotch, caused by Pyrenophora teres, is a major barley disease that occurs in two forms, spot form net blotch (SFNB) and net form net blotch (NFNB), reducing grain yield and quality worldwide. Accurate detection is critical for disease management and breeding resistant cultivars; however, traditional disease scoring is labour-intensive and error-prone. This study evaluates the use of UAV-based red–green–blue (RGB) and multispectral imagery, combined with machine learning, for determining net blotch infection levels at the plot scale across multiple sites and seasons in Australia. Various colour features, vegetation indices, and algorithms were tested, including a cross-domain testing for model generalisation. We propose a robust UAV-driven pipeline enabling precise disease monitoring and phenotyping in barley breeding programs.