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January 28, 2026Microchemical Journal

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

Tan Liu Peiyan Wu Songlin Guo Qi Yuan Jun Ma Qingyun Yuan Jin Wang

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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.

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Implication

Analysis demonstrates precise classification of rice blast disease using UAV hyperspectral imagery, suggesting enhanced disease monitoring.

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Cite This Study

Liu et al. (2026) studied this question.

synapsesocial.com/papers/69a75c42c6e9836116a24f47 https://doi.org/10.1016/j.microc.2026.117105

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