| Inversion model of summer maize plant height and biomass under different water and fertilizer treatments based on UAV spectra |
|
View Fulltext View/Add Comment Download reader |
| |
| DOI：10.7606/j.issn.1000-7601.2023.04.21 |
| Key Words: summer maize UAV spectrum water and fertilizer management machine learning |
| Author Name | Affiliation | | CHEN Zhen | Farmland Irrigation Research Institute of Chinese Academy of Agricultural Sciences/Henan Key Laboratory of Water\|saving Agriculture, Xinxiang, Henan 453002, China | | CHENG Qian | Farmland Irrigation Research Institute of Chinese Academy of Agricultural Sciences/Henan Key Laboratory of Water\|saving Agriculture, Xinxiang, Henan 453002, China | | XU Honggang | Farmland Irrigation Research Institute of Chinese Academy of Agricultural Sciences/Henan Key Laboratory of Water\|saving Agriculture, Xinxiang, Henan 453002, China | | HUANG Xiuqiao | Farmland Irrigation Research Institute of Chinese Academy of Agricultural Sciences/Henan Key Laboratory of Water\|saving Agriculture, Xinxiang, Henan 453002, China |
|
| Hits: 715 |
| Download times: 585 |
| Abstract: |
| Summer maize under different water and fertilizer treatments was used as the main line of research. Images were collected by using UAV remote sensing system. By analyzing the spectral data and the maize growth indexes measured on the ground, we clarify the effects of different water and fertilizer treatments on the growth indexes of summer maize and construct a crop growth index monitoring model based on spectral perception.The results showed that different irrigation and fertilization treatments significantly affected summer maize plant height, and the spectrally calculated plant height values were all highly significantly correlated with the measured values at the P<0.0001 level. R2 of the coefficient of determination for different periods in 2020 were 0.354, 0.483, 0.672, and 0.702, respectively and in 2021 were 0.314, 0.410, 0.426, and 0.466, respectively. Growth period data fusion can greatly improve inversion accuracy of plant height, with goodness of fit of 0.946 and 0.906, respectively in 2020 and 2021. There was a good correlation between multi\|spectral vegetation index and summer maize biomass under different water and fertilizer treatments. The inversion model for 2020 maize biomass constructed using the Cubist algorithm performed best. Growth period data fusion greatly improved the inversion superiority of the model. Three models （SVR, Cubist and RF model） constructed by the three algorithms all had excellent performance on the 2021 data set, with R2 on the test set reaching 0.942, 0.941 and 0.934, respectively. |
|
|
|