Related Experiment Video
Updated: Apr 16, 2026
Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes
Published on: March 28, 2025
Using satellite multispectral imagery for damage mapping of armyworm (Spodoptera frugiperda) in maize at a regional
Jingcheng Zhang1,2,3,4, Yanbo Huang5, Lin Yuan1,2,3,4
1Beijing Research Centre for Information Technology in Agriculture, Beijing, China.
Background:
Armyworm, a destructive insect for maize, has caused a wide range of damage in both China and the United States in recent years. To obtain the spatial distribution of the damage area, and to assess the damage severity, a fast and accurate loss assessment method is of great importance for effective administration. The objectives of this study were to determine suitable spectral features for armyworm detection and to develop a mapping method at a regional scale on the basis of satellite remote sensing image data.
Results:
Armyworm infestation can cause a significant change in the plant's leaf area index, which serves as a basis for infestation monitoring. Among the number of vegetation indices that were examined for their sensitivity to insect damage, the modified soil-adjusted vegetation index was identified as the optimal vegetation index for detecting armyworm. A univariate model relying on two-date satellite images significantly outperformed a multivariate model, with the overall accuracy increased from 0.50 to 0.79.
Conclusion:
A mapping method for monitoring armyworm infestation at a regional scale has been developed, based on a univariate model and two-date multispectral satellite images. The successful application of this method in a typical armyworm outbreak event in Tangshan, Hebei Province, China, demonstrated the feasibility of the method and its promising potential for implementation in practice.
