A novel computer vision-based approach to automatic detection and severity assessment of crop diseases

Accurate detection and identification of crop diseases plays an important role in effectively controlling and preventing diseases for sustainable agriculture and food security. In this work, we have developed a novel computer vision-based approach for automatically identifying crop diseases based on marker-controlled watershed segmentation, superpixel based feature analysis and classification. The experimental result demonstrates that the proposed approach can accurately detect crop diseases (i.e. Septoria and Yellow rust. Two types of most important and major wheat diseases in UK and across the world) and assess the disease severity with efficient processing speed.