Remote Sensing Monitoring of Grassland Locust Density Based on Machine Learning

被引:3
|
作者
Du, Qiang [1 ,2 ]
Wang, Zhiguo [1 ,2 ]
Huang, Pingping [1 ,2 ]
Zhai, Yongguang [1 ,2 ]
Yang, Xiangli [3 ]
Ma, Shuai [1 ,2 ]
机构
[1] Inner Mongolia Univ Technol, Coll Informat Engn, Hohhot 010080, Peoples R China
[2] Inner Mongolia Key Lab Radar Technol & Applicat, Hohhot 010051, Peoples R China
[3] Chongqing Jiaotong Univ, Coll Informat Sci & Engn, Chongqing 400074, Peoples R China
关键词
grassland locust; machine learning; regression; remote sensing; STEPPE; CHINA;
D O I
10.3390/s24103121
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
The main aim of this study was to utilize remote sensing data to establish regression models through machine learning to predict locust density in the upcoming year. First, a dataset for monitoring grassland locust density was constructed based on meteorological data and multi-source remote sensing data in the study area. Subsequently, an SVR (support vector regression) model, BP neural network regression model, random forest regression model, BP neural network regression model with the PCA (principal component analysis), and deep belief network regression model were built on the dataset. The experimental results show that the random forest regression model had the best prediction performance among the five models. Specifically, the model achieved a coefficient of determination (R2) of 0.9685 and a root mean square error (RMSE) of 1.0144 on the test set, which were the optimal values achieved among all the models tested. Finally, the locust density in the study area for 2023 was predicted and, by comparing the predicted results with actual measured data, it was found that the prediction accuracy was high. This is of great significance for local grassland ecological management, disaster warning, scientific decision-making support, scientific research progress, and sustainable agricultural development.
引用
收藏
页数:21
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