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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- 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).
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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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