Monitoring Maize Canopy Chlorophyll Content throughout the Growth Stages Based on UAV MS and RGB Feature Fusion
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- Elsayed, Salah & Elhoweity, Mohamed & Ibrahim, Hazem H. & Dewir, Yaser Hassan & Migdadi, Hussein M. & Schmidhalter, Urs, 2017. "Thermal imaging and passive reflectance sensing to estimate the water status and grain yield of wheat under different irrigation regimes," Agricultural Water Management, Elsevier, vol. 189(C), pages 98-110.
- Bazrafshan, Ommolbanin & Ehteram, Mohammad & Moshizi, Zahra Gerkaninezhad & Jamshidi, Sajad, 2022. "Evaluation and uncertainty assessment of wheat yield prediction by multilayer perceptron model with bayesian and copula bayesian approaches," Agricultural Water Management, Elsevier, vol. 273(C).
- Lili Zhou & Chenwei Nie & Tao Su & Xiaobin Xu & Yang Song & Dameng Yin & Shuaibing Liu & Yadong Liu & Yi Bai & Xiao Jia & Xiuliang Jin, 2023. "Evaluating the Canopy Chlorophyll Density of Maize at the Whole Growth Stage Based on Multi-Scale UAV Image Feature Fusion and Machine Learning Methods," Agriculture, MDPI, vol. 13(4), pages 1-22, April.
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Keywords
feature fusion; machine learning; maize ( Zea mays L.); canopy chlorophyll content;All these keywords.
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