Development of a Multiscale XGBoost-Based Model for Enhanced Detection of Potato Late Blight Using Sentinel-2, UAV, and Ground Data

Potatoes, a crucial staple crop, face significant threats from late blight, which poses serious risks to food security. Despite extensive research using ground and unmanned aerial vehicle (UAV) hyperspectral data for crop disease monitoring, satellite-scale identification of diseases, such as potato...

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Vydáno v:IEEE transactions on geoscience and remote sensing Ročník 62; s. 1 - 14
Hlavní autoři: Chang, Sheng, Chi, Zelong, Chen, Hong, Hu, Tongle, Gao, Caixia, Meng, Jihua, Han, Liangxiu
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Algorithms
Autonomous aerial vehicles
Band spectra
Crop diseases
Data models
Decision making
Disease detection
Disease recognition
Diseases
Food security
Hyperspectral remote sensing
Infrared analysis
Late blight
Machine learning
Monitoring
multiscale
Multiscale analysis
multispectral remote sensing
Near infrared radiation
Plant diseases
potato late blight (PLB)
Potatoes
Reflectance
Reflectivity
Root-mean-square errors
Satellites
Sentinel-2
Static VAr compensators
Unmanned aerial vehicles
Vegetation
XGBoost
ISSN:0196-2892, 1558-0644
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Shrnutí:Potatoes, a crucial staple crop, face significant threats from late blight, which poses serious risks to food security. Despite extensive research using ground and unmanned aerial vehicle (UAV) hyperspectral data for crop disease monitoring, satellite-scale identification of diseases, such as potato late blight (PLB) remains limited. This study employs a multiscale analysis approach, integrating high-resolution Sentinel-2 multispectral satellite data with UAV and ground spectral data, to monitor and identify PLB. A key finding of this study is the general similarity in spectral patterns across different scales, with consistent valley values in bands of blue and red and peak values in bands of near infrared (NIR) and narrow NIR, accompanied by a consistent decrease in reflectance correlating with increasing disease severity. Furthermore, the study highlights scale-dependent spectral variations, with changes in bands of Vegetation Red Edge2, Vegetation Red Edge3, NIR, and narrow NIR being more pronounced at the ground scale compared to UAV and satellite scales. Based on the developed red edge index and disease stress index with a suite of machine learning algorithms, we proposed an XGBoost-based model integrating spectral indices for PLB monitoring (PLB-SI-XGBoost). Notably, the proposed model demonstrated the highest average evaluation score of 0.88 and the lowest root-mean-square error (RMSE) of 13.50 during ground-scale validation, outperforming other algorithms. At the UAV scale, the proposed model achieved a robust R-squared value of 0.74 and an RMSE of 18.27. Moreover, the application of Sentinel-2 data for disease detection at the satellite scale yielded an accuracy of 70% in the model. The results of the study emphasize the importance of scale in disease monitoring models and illuminate the potential for satellite-scale surveillance of PLB. The exceptional performance of the PLB-SI-XGBoost model in detecting PLB suggests its utility in enhancing agricultural decision-making with more accurate and reliable data support.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2024.3466648