ArticleFrontiers in plant science2022
Poplar's Waterlogging Resistance Modeling and Evaluating: Exploring and Perfecting the Feasibility of Machine Learning Methods in Plant Science.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Modular, On-Site Solutions with Lightweight Anomaly Detection for Sustainable Nutrient Management in Agriculture.ACS ES&T engineering · 2026Article
- A Precise Apple Quality Prediction Model Integrating Driving Factor Screening and BP Neural Network.Plants (Basel, Switzerland) · 2025Article
- Variation in flooding tolerance in Populus deltoides 'D-124' and P. trichocarpa × P. deltoides hybrid '52-225'.Tree physiology · 2025Article
- Performance comparison of RGB and multispectral vegetation indices based on machine learning for estimatingFrontiers in plant science · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
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Abstract
Floods, as one of the most common disasters in the natural environment, have caused huge losses to human life and property. Predicting the flood resistance of poplar can effectively help researchers select seedlings scientifically and resist floods precisely. Using machine learning algorithms, models of poplar's waterlogging tolerance were established and evaluated. First of all, the evaluation indexes of poplar's waterlogging tolerance were analyzed and determined. Then, significance testing, correlation analysis, and three feature selection algorithms (Hierarchical clustering, Lasso, and Stepwise regression) were used to screen photosynthesis, chlorophyll fluorescence, and environmental parameters. Based on this, four machine learning methods, BP neural network regression (BPR), extreme learning machine regression (ELMR), support vector regression (SVR), and random forest regression (RFR) were used to predict the flood resistance of poplar. The results show that random forest regression (RFR) and support vector regression (SVR) have high precision. On the test set, the coefficient of determination (R
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Registered trials
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