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

Chang, Sheng ORCID: https://orcid.org/0000-0001-7870-7047, Chi, Zelong, Chen, Hong ORCID: https://orcid.org/0000-0002-8287-7329, Hu, Tongle, Gao, Caixia ORCID: https://orcid.org/0000-0003-1571-7381, Meng, Jihua and Han, Liangxiu ORCID: https://orcid.org/0000-0003-2491-7473 (2024) Development of a Multiscale XGBoost-based Model for Enhanced Detection of Potato Late Blight Using Sentinel-2, UAV, and Ground Data. IEEE Transactions on Geoscience and Remote Sensing, 62. 4415014. ISSN 0196-2892

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Abstract

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, satellitescale identification of diseases like Potato Late Blight (PLB) remains limited. This study employs a multi-scale 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 and Narrow near Infrared, accompanied by a consistent decrease in reflectance correlating with increasing disease severity. Furthermore, the study highlights scaledependent spectral variations, with changes in bands of Vegetation Red Edge2, Vegetation Red Edge3, Near Infrared and Narrow Near Infrared 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 a 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 Rsquared value of 0.74 and an RMSE of 18.27. Moreover, the application of Sentinel-2 data for disease detection at 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 PLB-SI-XGBoost model in detecting PLB suggests its utility in enhancing agricultural decisionmaking with more accurate and reliable data support.