{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T23:43:27Z","timestamp":1774482207842,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,5,14]],"date-time":"2024-05-14T00:00:00Z","timestamp":1715644800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Inner Mongolia Autonomous Region Natural Science Foundation","award":["2023LHMS06013"],"award-info":[{"award-number":["2023LHMS06013"]}]},{"name":"Inner Mongolia Autonomous Region Natural Science Foundation","award":["2023YFHH0081"],"award-info":[{"award-number":["2023YFHH0081"]}]},{"name":"Inner Mongolia Autonomous Region Natural Science Foundation","award":["JY20240009"],"award-info":[{"award-number":["JY20240009"]}]},{"name":"Inner Mongolia Autonomous Region Science and Technology Plan","award":["2023LHMS06013"],"award-info":[{"award-number":["2023LHMS06013"]}]},{"name":"Inner Mongolia Autonomous Region Science and Technology Plan","award":["2023YFHH0081"],"award-info":[{"award-number":["2023YFHH0081"]}]},{"name":"Inner Mongolia Autonomous Region Science and Technology Plan","award":["JY20240009"],"award-info":[{"award-number":["JY20240009"]}]},{"name":"Basic Scientific Research Fund Project of the Autonomous Region Directly Universities","award":["2023LHMS06013"],"award-info":[{"award-number":["2023LHMS06013"]}]},{"name":"Basic Scientific Research Fund Project of the Autonomous Region Directly Universities","award":["2023YFHH0081"],"award-info":[{"award-number":["2023YFHH0081"]}]},{"name":"Basic Scientific Research Fund Project of the Autonomous Region Directly Universities","award":["JY20240009"],"award-info":[{"award-number":["JY20240009"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s24103121","type":"journal-article","created":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T03:35:55Z","timestamp":1715744155000},"page":"3121","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Remote Sensing Monitoring of Grassland Locust Density Based on Machine Learning"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-9275-1946","authenticated-orcid":false,"given":"Qiang","family":"Du","sequence":"first","affiliation":[{"name":"College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, China"},{"name":"Inner Mongolia Key Laboratory of Radar Technology and Application, Hohhot 010051, China"}]},{"given":"Zhiguo","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, China"},{"name":"Inner Mongolia Key Laboratory of Radar Technology and Application, Hohhot 010051, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7720-1183","authenticated-orcid":false,"given":"Pingping","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, China"},{"name":"Inner Mongolia Key Laboratory of Radar Technology and Application, Hohhot 010051, China"}]},{"given":"Yongguang","family":"Zhai","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, China"},{"name":"Inner Mongolia Key Laboratory of Radar Technology and Application, Hohhot 010051, China"}]},{"given":"Xiangli","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Chongqing Jiaotong University, Chongqing 400074, China"}]},{"given":"Shuai","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Inner Mongolia University of Technology, Hohhot 010080, China"},{"name":"Inner Mongolia Key Laboratory of Radar Technology and Application, Hohhot 010051, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,14]]},"reference":[{"key":"ref_1","first-page":"1310","article-title":"Analysis of Insect Diversity in Typical Grasslands of XilinGol League","volume":"50","author":"Chang","year":"2023","journal-title":"J. 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