Detection of scab in wheat ears using in situ hyperspectral data and support vector machine optimized by genetic algorithm

Authors

  • Linsheng Huang 1. National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China
  • Hansu Zhang 1. National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China; 2. Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
  • Chao Ruan 1. National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China; 2. Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
  • Wenjiang Huang 1. National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China; 2. Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
  • Tingguang Hu 1. National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China; 2. Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
  • Jinling Zhao 1. National Engineering Research Center for Agro-Ecological Big Data Analysis & Application, Anhui University, Hefei, China

Keywords:

wheat scab, hyperspectral data, correlation analysis, genetic algorithm, wavelet transform, support vector machine

Abstract

A new method was proposed to extract sensitive features and to construct a monitoring model for wheat scab based on in situ hyperspectral data of wheat ears to achieve effective prevention and control and provide theoretical support for its large-scale monitoring. Eight sensitive features were selected through correlation analysis and wavelet transform. These features were as follows: three original bands of 350-400 nm, 500-600 nm, and 720-1000 nm; three vegetation indices of modified simple ratio (MSR), normalized difference vegetation index, and structural independent pigment index; and two wavelet features of WF01 and WF02. By combining the selected sensitive features with support vector machine (SVM) and SVM optimized by genetic algorithm (GASVM), a total of 16 monitoring models were built, and the monitoring accuracies of the two types of models were compared. The ability of the monitoring models built by GASVM to identify scab was better than that of SVM algorithm under the same characteristic variables. Among the 16 models, MSR combined with GASVM had an overall accuracy of 75% and a Kappa coefficient of 0.47. GASVM can be used to monitor wheat scab and its application can improve the accuracy of disease monitoring. Keywords: wheat scab, hyperspectral data, correlation analysis, genetic algorithm, wavelet transform, support vector machine DOI: 10.25165/j.ijabe.20201302.5331 Citation: Huang L S, Zhang H S, Ruan C, Huang W J, Hu T G, Zhao J L. Detection of scab in wheat ears using in situ hyperspectral data and support vector machine optimized by genetic algorithm. Int J Agric & Biol Eng, 2020; 13(2): 182–188.

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Published

2020-04-10

How to Cite

Huang, L., Zhang, H., Ruan, C., Huang, W., Hu, T., & Zhao, J. (2020). Detection of scab in wheat ears using in situ hyperspectral data and support vector machine optimized by genetic algorithm. International Journal of Agricultural and Biological Engineering, 13(2), 182–188. Retrieved from https://www.ijabe.migration.pkpps06.publicknowledgeproject.org/index.php/ijabe/article/view/5331

Issue

Vol. 13 No. 2 (2020): IJABE

Section

Information Technology, Sensors and Control Systems

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