Monitoring of Wheat Fusarium Head Blight on Spectral and Textural Analysis of UAV Multispectral Imagery

Chunfeng Gao, Xingjie Ji, Qiang He, Zheng Gong, Heguang Sun, Tiantian Wen and Wei Guo ()
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Chunfeng Gao: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Xingjie Ji: Henan Key Laboratory of Agrometeorological Support and Applied Technique, Zhengzhou 450003, China
Qiang He: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Zheng Gong: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Heguang Sun: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Tiantian Wen: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China
Wei Guo: College of Information and Management Science, Henan Agricultural University, Zhengzhou 450046, China

Agriculture, 2023, vol. 13, issue 2, 1-16

Abstract: Crop disease identification and monitoring is an important research topic in smart agriculture. In particular, it is a prerequisite for disease detection and the mapping of infected areas. Wheat fusarium head blight (FHB) is a serious threat to the quality and yield of wheat, so the rapid monitoring of wheat FHB is important. This study proposed a method based on unmanned aerial vehicle (UAV) low-altitude remote sensing and multispectral imaging technology combined with spectral and textural analysis to monitor FHB. First, the multispectral imagery of the wheat population was collected by UAV. Second, 10 vegetation indices (VIs)were extracted from multispectral imagery. In addition, three types of textural indices (TIs), including the normalized difference texture index (NDTI), difference texture index (DTI), and ratio texture index (RTI) were extracted for subsequent analysis and modeling. Finally, VIs, TIs, and VIs and TIs integrated as the input features, combined with k-nearest neighbor (KNN), the particle swarm optimization support vector machine (PSO-SVM), and XGBoost were used to construct wheat FHB monitoring models. The results showed that the XGBoost algorithm with the fusion of VIs and TIs as the input features has the highest performance with the accuracy and F1 score of the test set being 93.63% and 92.93%, respectively. This study provides a new approach and technology for the rapid and nondestructive monitoring of wheat FHB.

Keywords: unmanned aerial vehicle; multispectral imagery; fusarium head blight; texture indices; machine learning (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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