Precise classification of rice blast disease using an improved MCG-net with multi-feature fusion technology for UAV hyperspectral imagery

Key Points

• Precision classification of rice blast disease was achieved using improved MCG-net, enhancing detection capabilities.
• The analysis achieved an accuracy of 92%, indicating the efficacy of the multi-feature fusion approach in the classification process.
• Evaluation utilized UAV hyperspectral imagery to collect detailed data on rice fields and disease conditions.
• Findings highlight potential advancements in agricultural monitoring techniques to combat rice blast disease efficiently.