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Use Self-Training Random Forest for Predicting Winter Wheat Yield

Remote Sensing

2024-12 | Journal article | Author

DOI: 10.3390/rs16244723

Contributors: Yulin Shen; Benoît Mercatoris; Qingzhi Liu; Hongxun Yao; Zongpeng Li; Zhen Chen; Wensheng Wang

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Multidisciplinary Digital Publishing Institute

Estimating Maize Crop Height and Aboveground Biomass Using Multi-Source Unmanned Aerial Vehicle Remote Sensing and Optuna-Optimized Ensemble Learning Algorithms

Remote Sensing

2024-08 | Journal article | Author

DOI: 10.3390/rs16173176

Contributors: Yafeng Li; Changchun Li; Qian Cheng; Fuyi Duan; Weiguang Zhai; Zongpeng Li; Bohan Mao; Fan Ding; Xiaohui Kuang; Zhen Chen

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Multidisciplinary Digital Publishing Institute

Predicting Winter Wheat Yield with Dual-Year Spectral Fusion, Bayesian Wisdom, and Cross-Environmental Validation

Remote Sensing

2024-06 | Journal article | Author

DOI: 10.3390/rs16122098

Contributors: Zongpeng Li; Qian Cheng; Li Chen; Bo Zhang; Shuzhe Guo; xinguo zhou; Zhen Chen

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Multidisciplinary Digital Publishing Institute

Deep Learning Models Outperform Generalized Machine Learning Models in Predicting Winter Wheat Yield Based on Multispectral Data from Drones

Drones

2023-08 | Journal article | Author

DOI: 10.3390/drones7080505

Contributors: li zongpeng; Zhen Chen; Qian Cheng; Shuaipeng Fei; xinguo zhou

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Multidisciplinary Digital Publishing Institute

Enhancing Wheat Above-Ground Biomass Estimation Using UAV RGB Images and Machine Learning: Multi-Feature Combinations, Flight Height, and Algorithm Implications

Remote Sensing

2023-07 | Journal article | Author

DOI: 10.3390/rs15143653

Contributors: Weiguang Zhai; Changchun Li; Qian Cheng; Bohan Mao; Zongpeng Li; Yafeng Li; Fan Ding; Siqing Qin; Shuaipeng Fei; Zhen Chen

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Multidisciplinary Digital Publishing Institute

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https://orcid.org/0000-0002-2847-0042

Activities

Works (5)

Use Self-Training Random Forest for Predicting Winter Wheat Yield

Estimating Maize Crop Height and Aboveground Biomass Using Multi-Source Unmanned Aerial Vehicle Remote Sensing and Optuna-Optimized Ensemble Learning Algorithms

Predicting Winter Wheat Yield with Dual-Year Spectral Fusion, Bayesian Wisdom, and Cross-Environmental Validation

Deep Learning Models Outperform Generalized Machine Learning Models in Predicting Winter Wheat Yield Based on Multispectral Data from Drones

Enhancing Wheat Above-Ground Biomass Estimation Using UAV RGB Images and Machine Learning: Multi-Feature Combinations, Flight Height, and Algorithm Implications

https://orcid.org/0000-0002-2847-0042

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Activities

Works (5)

Use Self-Training Random Forest for Predicting Winter Wheat Yield

Estimating Maize Crop Height and Aboveground Biomass Using Multi-Source Unmanned Aerial Vehicle Remote Sensing and Optuna-Optimized Ensemble Learning Algorithms

Predicting Winter Wheat Yield with Dual-Year Spectral Fusion, Bayesian Wisdom, and Cross-Environmental Validation

Deep Learning Models Outperform Generalized Machine Learning Models in Predicting Winter Wheat Yield Based on Multispectral Data from Drones

Enhancing Wheat Above-Ground Biomass Estimation Using UAV RGB Images and Machine Learning: Multi-Feature Combinations, Flight Height, and Algorithm Implications