Monitoring rice bacterial blight based on UAV images of different ground sampling distances (GSD)

ZHAO Gaoyuan; ZHANG Yali; ZHANG Zichao; LI Zhiyong; DENG Jizhong

doi:10.7671/j.issn.1001-411X.202401003

Journal of South China Agricultural University > 2025 > 46(1): 115-123. > DOI: 10.7671/j.issn.1001-411X.202401003

ZHAO Gaoyuan, ZHANG Yali, ZHANG Zichao, et al. Monitoring rice bacterial blight based on UAV images of different ground sampling distances (GSD)[J]. Journal of South China Agricultural University, 2025, 46(1): 115-123. DOI: 10.7671/j.issn.1001-411X.202401003

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Monitoring rice bacterial blight based on UAV images of different ground sampling distances (GSD)

College of Engineering, South China Agricultural University/National Center for International Collaboration Research on Precision Agricultural Aviation Pesticides Spraying Technology, Guangzhou 510642, China

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Received Date: January 01, 2024
Available Online: December 08, 2024
Published Date: December 12, 2024

Graphical Abstract

Abstract

Abstract

Objective
In order to monitor rice bacterial blight quickly and non-destructively, and guide field operations.
Method
High-resolution images of rice canopy under bacterial blight stress were acquired using utilized unmanned aerial vehicles (UAVs). Color features and texture features were extracted from the images, and multiple regression models based on color features, texture features, and the fusion of color and texture features were constructed to predict the infection level of rice bacterial blight. The influence of different ground sampling distances (GSD) on the accuracy of the models was also explored.
Result
The determination coefficient (R²) of the monitoring model based on color features was 85.9%, root mean square error (RMSE) was 1.43 and relative RMSE (RRMSE) was 19.1%. The R² had increased by 2.4 percentage points and RRMSE had increased by 4.6 percentage points compared with the model based on texture features. Compared with single-feature models, the prediction model based on the fusion of color and texture features (R²=89.6%, RMSE=1.06, RRMSE=15.1%) exhibited significant improvement in accuracy. By constructing models with different GSDs, it was found that when the GSD was 0.2, 0.5 or 0.8 cm, the models achieved higher accuracy with R² all above 80%.
Conclusion
The color and texture features extracted from low-altitude remote sensing images captured by UAVs can be used for monitoring rice bacterial blight. The results can provide effective technical support for UAV remote sensing monitoring of rice bacterial blight.
- Rice,
- Bacterial leaf blight,
- UAV,
- Color feature,
- Texture feature,
- Ground sampling distance (GSD)

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References

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Monitoring rice bacterial blight based on UAV images of different ground sampling distances (GSD)

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