zhpsЖурнал прикладной спектроскопииZhurnal Prikladnoii Spektroskopii0514-7506B. I. Stepanov Institute of Physics of the National Academy of Scienceszhps-1192Research ArticleАННОТАЦИИ АНГЛОЯЗЫЧНЫХ СТАТЕЙABSTRACTS ENGLISH-LANGUAGE ARTICLESМониторинг заражения грызунами пастбищ на основе плотных остаточных сетей и дистанционного зондирования беспилотными летательными аппаратамиResearch on Grassland Rodent Infestation Monitoring Methods Based on Dense Residual Networks and Unmanned Aerial Vehicle Remote SensingZhangT.ZhangT.

Колледж механики и электротехники,

Хоххот

Mechanical and Electrical Engineering College,

Hohhot

DuJ.DuJ.

Колледж механики и электротехники,

Хоххот

Mechanical and Electrical Engineering College,

Hohhot

nndjwc202@imau.edu.cnZhuX.ZhuX.

Колледж механики и электротехники,

Хоххот

Mechanical and Electrical Engineering College,

Hohhot

GaoX.GaoX.

Колледж механики и электротехники,

Хоххот

Mechanical and Electrical Engineering College,

Hohhot

Сельскохозяйственный университет Внутренней МонголииКитайInner Mongolia Agricultural UniversityChina202202122022896905905Copyright © Zhang T., Du J., Zhu X., Gao X., 20222022Zhang T., Du J., Zhu X., Gao X.Zhang T., Du J., Zhu X., Gao X.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://zhps.ejournal.by/jour/article/view/1192

Для мониторинга поражения пастбищ грызунами разработана система сбора гиперспектральных изображений с низковысотного беспилотного летательного аппарата. Модель плотной трехмерной сверточной сети (3D-DenseNet) улучшена за счет использования остаточной структуры и асимметричной свертки, а также предложена модель 3D-глубокой плотной остаточной сети (3D-DDRNet), которая использована для классификации информации о мониторинге луговых грызунов. Общая точность классификации модели 3D-DDRNet составляет 96.68 %, размер модели 6.12 МБ. Точность модели улучшена на 1.46%, размер уменьшен на 15.5 % по сравнению с 3D-DenseNet. 

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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The authors declare that there are no conflicts of interest present.