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Research on Grassland Rodent Infestation Monitoring Methods Based on Dense Residual Networks and Unmanned Aerial Vehicle Remote Sensing

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Journal of Applied Spectroscopy Aims and scope

Grassland rodent infestations are important factors that limit the healthy development of grassland ecosystems. Understanding the spatial distributions of rodent populations in relation to vegetation and soil is a prerequisite for implementing ecological prevention and control measures to alleviate rodent infestations. A low-altitude unmanned aerial vehicle hyperspectral image data acquisition system has been developed for monitoring grassland rodent infestations. The three-dimensional dense convolutional network (3D-DenseNet) model is improved by using a residual structure and asymmetric convolution, and a 3D deep dense residual network (3D-DDRNet) model is proposed and used to classify the features of grassland rodent monitoring information. The results show that the overall classification accuracy of the 3D-DDRNet model is 96.68%, and the model size is 6.12 MB. The overall accuracy is improved by 1.46%, and the model size is reduced by 15.5% compared with that achieved before the improvement. This study can be used as a benchmark for the extraction and inversion of rodent information acquired from grassland remote sensing images, and it provides a theoretical basis for grassland rodent pest control.

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Authors and Affiliations

  1. Mechanical and Electrical Engineering College, Inner Mongolia Agricultural University, Hohhot, China

    T. Zhang, J. Du, X. Zhu & X. Gao

Authors
  1. T. Zhang
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  2. J. Du
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  3. X. Zhu
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  4. X. Gao
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Corresponding author

Correspondence to J. Du.

Additional information

Abstract of article is published in Zhurnal Prikladnoi Spektroskopii, Vol. 89, No. 6, p. 905, November–December, 2022.

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Zhang, T., Du, J., Zhu, X. et al. Research on Grassland Rodent Infestation Monitoring Methods Based on Dense Residual Networks and Unmanned Aerial Vehicle Remote Sensing. J Appl Spectrosc 89, 1220–1231 (2023). https://doi.org/10.1007/s10812-023-01489-8

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  • DOI: https://doi.org/10.1007/s10812-023-01489-8

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