Wheat Leaf Rust Detection and Infected-Area Estimation Using Multi-Scale Fusion and Lab-Based Lesion Localization

Healthcare, education, technological advancement, and farming are the key challenges facing developing countries, with agriculture unquestionably playing an important role in economic growth. Ensuring adequate food production is essential for citizens’ survival, as it is anticipated that efforts in this area would result in increased food productivity. A key approach to enhancing field productivity involves meticulous care of its components, starting with the production of crops. Wheat leaf rust poses a severe threat, particularly to young seedlings, constituting a significant fungal disease that can cause a 25% reduction in wheat productivity. To overcome these issues, this research work proposes a novel image fusion approach called Multi-Scale Discrete Wavelet Transform (MS-DWT). The method uses distinct fusion strategies to extract meaningful details from source images. After that, a Lab Color Space (LCS), followed by a color thresholding method, is employed for the detection and lesion localization of rust in the source images. Furthermore, the proposed model measures the area affected by rust in wheat crops, providing farmers with vital information during the post-medication (anti-rust spray) operation. The experimental findings demonstrate superior performance and achieve a classification accuracy of 98.85%, and the maximum testing accuracy was 99.17% on our generated dataset.