Using UAV Multispectral Remote Sensing with Appropriate Spatial Resolution and Machine Learning to Monitor Wheat Scab

Wenjing Zhu, Zhankang Feng, Shiyuan Dai, Pingping Zhang and Xinhua Wei ()
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Wenjing Zhu: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Zhankang Feng: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Shiyuan Dai: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Pingping Zhang: Institute of Food Crops, Jiangsu Academy of Agricultural Sciences, Nanjing 210014, China
Xinhua Wei: School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China

Agriculture, 2022, vol. 12, issue 11, 1-16

Abstract: This study took the wheat grown in the experimental area of Jiangsu Academy of Agricultural Sciences as the research object and used the unmanned aerial vehicle (UAV) to carry the Rededge-MX multispectral camera to obtain the wheat scab image with different spatial resolutions (1.44 cm, 2.11 cm, 3.47 cm, 4.96 cm, 6.34 cm, and 7.67 cm). The vegetation indexes (VIs) and texture features (TFs) extracted from the UAV multispectral image were screened for high correlation with the disease index (DI) to investigate the impact of spatial resolution on the accuracy of UAV multispectral wheat scab monitoring. Finally, the best spatial resolution for UAV multispectral monitoring of wheat scab was determined to be 3.47 cm, and then, based on the 3.47 cm best resolution image, VIs and TFs were used as input variables, and three algorithms of partial least squares regression (PLSR), support vector machine regression (SVR), and back propagation neural network (BPNN) was used to establish wheat scab, monitoring models. The findings demonstrated that the VIs and TFs fusion model was more appropriate for monitoring wheat scabs by UAV remote sensing and had better fitting and monitoring accuracy than the single data source monitoring model during the wheat filling period. The SVR algorithm has the best monitoring effect in the multi-source data fusion model (VIs and TFs). The training set was identified as 0.81, 4.27, and 1.88 for the coefficient of determination (R 2 ), root mean square error (RMSE), and relative percent deviation (RPD). The verification set was identified as 0.83, 3.35, and 2.72 for R 2 , RMSE, and RPD. In conclusion, the results of this study provide a scheme for the field crop diseases in the UAV monitoring area, especially for the classification and variable application of wheat scabs by near-earth remote sensing monitoring.

Keywords: UAV; diseases monitoring; spatial resolution; wheat; remote sensing; vegetation index; texture features (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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