Remote Sensing Technology and Application, Volume. 40, Issue 1, 69(2025)
Remote Sensing Monitoring of Wheat Stripe Rust Using Constrained Random Forest and Bayesian Optimization Algorithm
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Fig. 2. Constructing a CO-RFR prediction model using a training set
Fig. 5. Precision deviation between training and validation sets of each model
Table 1.
Accuracy testing of four models View tableView in ArticleTable 1.
Accuracy testing of four models 模型 a组 b组 c组 平均 R2 RMSE R2 RMSE R2 RMSE R2 RMSE CART 0.57 0.24 0.58 0.22 0.56 0.25 0.57 0.24 RFR 0.63 0.19 0.48 0.23 0.51 0.21 0.54 0.21 CO-RFR 0.85 0.12 0.84 0.13 0.83 0.12 0.84 0.12 MLR 0.59 0.20 0.60 0.20 0.60 0.19 0.60 0.20
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Yiyang XUE, Xia JING, Qixing YE, Kaiqi Du, Bingyu Li. Remote Sensing Monitoring of Wheat Stripe Rust Using Constrained Random Forest and Bayesian Optimization Algorithm[J]. Remote Sensing Technology and Application, 2025, 40(1): 69
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Received: May. 30, 2023
Accepted: --
Published Online: May. 22, 2025
The Author Email: Xia JING (jingxiaxust@163.com)
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