Automated Detection and Severity Prediction of Wheat Rust Using Cost-Effective Xception Architecture

Abstract

Wheat crop production is under constant threat from leaf and stripe rust, an airborne fungal disease caused by the pathogen Puccinia triticina. Early detection and efficient crop phenotyping are crucial for managing and controlling the spread of this disease in susceptible wheat varieties. Current detection methods are predominantly manual and labour-intensive. Traditional strategies such as cultivating resistant varieties, applying fungicides and practicing good agricultural techniques often fall short in effectively identifying and responding to wheat rust outbreaks. To address these challenges, we propose an innovative computer vision-based disease severity prediction pipeline. Our approach utilizes a deep learning-based classifier to differentiate between healthy and rust-infected wheat leaves. Upon identifying an infected leaf, we apply Grabcut-based segmentation to isolate the foreground mask. This mask is then processed in the CIELAB color space to distinguish leaf rust stripes and spores. The disease severity ratio is calculated to measure the extent of infection on each test leaf. This paper introduces a ground-breaking disease severity prediction method, offering a low-cost, accessible and automated solution for wheat rust disease screening in field conditions using digital colour images. Our approach represents a significant advancement in crop disease management, promising timely interventions and better control measures for wheat rust.