Evaluating the Canopy Chlorophyll Density of Maize at the Whole Growth Stage Based on Multi-Scale UAV Image Feature Fusion and Machine Learning Methods
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- Yahui Guo & J. Senthilnath & Wenxiang Wu & Xueqin Zhang & Zhaoqi Zeng & Han Huang, 2019. "Radiometric Calibration for Multispectral Camera of Different Imaging Conditions Mounted on a UAV Platform," Sustainability, MDPI, vol. 11(4), pages 1-24, February.
- Zhou, Yongcai & Lao, Congcong & Yang, Yalong & Zhang, Zhitao & Chen, Haiying & Chen, Yinwen & Chen, Junying & Ning, Jifeng & Yang, Ning, 2021. "Diagnosis of winter-wheat water stress based on UAV-borne multispectral image texture and vegetation indices," Agricultural Water Management, Elsevier, vol. 256(C).
- Mohamad M. Awad, 2019. "Toward Precision in Crop Yield Estimation Using Remote Sensing and Optimization Techniques," Agriculture, MDPI, vol. 9(3), pages 1-13, March.
- Ofer Harel, 2009. "The estimation of R2 and adjusted R2 in incomplete data sets using multiple imputation," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(10), pages 1109-1118.
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- Wenfeng Li & Kun Pan & Wenrong Liu & Weihua Xiao & Shijian Ni & Peng Shi & Xiuyue Chen & Tong Li, 2024. "Monitoring Maize Canopy Chlorophyll Content throughout the Growth Stages Based on UAV MS and RGB Feature Fusion," Agriculture, MDPI, vol. 14(8), pages 1-22, August.
- Liyuan Zhang & Aichen Wang & Huiyue Zhang & Qingzhen Zhu & Huihui Zhang & Weihong Sun & Yaxiao Niu, 2024. "Estimating Leaf Chlorophyll Content of Winter Wheat from UAV Multispectral Images Using Machine Learning Algorithms under Different Species, Growth Stages, and Nitrogen Stress Conditions," Agriculture, MDPI, vol. 14(7), pages 1-17, July.
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Keywords
machine learning; canopy chlorophyll density; multi-scale feature fusion; maize;All these keywords.
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