Unmanned aerial vehicle hierarchical detection of leaf blast in rice crops based on a specific spectral vegetation index
Leaf blast is a significant global problem, severely affecting rice quality and yield, making swift, non-invasive detection crucial for effective field management. This study used hyperspectral remote sensing technology via an unmanned aerial vehicle to gather spectral data from rice crops. ANOVA an...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Higher Education Press
2025-06-01
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| Series: | Frontiers of Agricultural Science and Engineering |
| Subjects: | |
| Online Access: | https://journal.hep.com.cn/fase/EN/PDF/10.15302/J-FASE-2024576 |
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| Summary: | Leaf blast is a significant global problem, severely affecting rice quality and yield, making swift, non-invasive detection crucial for effective field management. This study used hyperspectral remote sensing technology via an unmanned aerial vehicle to gather spectral data from rice crops. ANOVA and the Relief-F algorithm were used to identify spectral bands sensitive to the disease and developed a new vegetation index, the rice blast index (RBI). This RBI was compared with 30 established vegetation indexes, using correlation analysis and visual comparison to further shortlist six superior indexes, including RBI. These were evaluated using the K-nearest neighbor (KNN) and random forests (RF) classification models. RBI demonstrated superior detection accuracy for leaf blast in both the KNN model (95.0% overall accuracy and 93.8% kappa coefficient) and the RF model (95.1% overall accuracy and 92.5% kappa coefficient). This study highlights the significant potential of RBI as an effective tool for precise leaf blast detection, offering a powerful new mechanism and theoretical basis for enhanced disease management in rice cultivation. |
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| ISSN: | 2095-7505 |