| Prediction model on chlorophyll content in maize leaf based on several high spectral parameters |
|
View Fulltext View/Add Comment Download reader |
| |
| DOI：10.7606/j.issn.1000-7601.2016.01.31 |
| Key Words: maize leaf chlorophyll content prediction models |
| Author Name | Affiliation | | WU Qian-wen | College of Resources & Environment Science, Xinjiang University, Key Laboratory of Oasis Ecology Ministry of Education, Urumqi, Xinjiang 830046, China | | XIONG Hei-gang | College of Resources & Environment Science, Xinjiang University, Key Laboratory of Oasis Ecology Ministry of Education, Urumqi, Xinjiang 830046, China
College of Applied Arts & Science, Beijing Union University, Beijing 100083, China | | JIN Yan-hua | College of Resources & Environment Science, Xinjiang University, Key Laboratory of Oasis Ecology Ministry of Education, Urumqi, Xinjiang 830046, China | | WANG Li-feng | College of Resources & Environment Science, Xinjiang University, Key Laboratory of Oasis Ecology Ministry of Education, Urumqi, Xinjiang 830046, China | | WANG Kai-long | College of Resources & Environment Science, Xinjiang University, Key Laboratory of Oasis Ecology Ministry of Education, Urumqi, Xinjiang 830046, China |
|
| Hits: 1456 |
| Download times: 907 |
| Abstract: |
| In this research, FieldSpecPro3 spectroscopy and SPAD-502 chlorophyll meter were used to measure the spectrum of corn leaf chlorophyll content and its amount respectively. Through the analysis on the relationships between chlorophyll content and parameters of red, blue, and green edge positions and spectral peak positions, single, double and multivariate spectral prediction models about the chlorophyll content were established. The results showed that in the visible spectrum, the higher corn chlorophyll content, the lower reflectance spectrum. However, exact opposite was observed in the infrared spectrum. Spectral parameters of red edge, green peak and blue edge positions were in significant correlations with the chlorophyll content, reaching up to 0.84 between red edge position and the chlorophyll content. As a result, single, double and three-variable models using three high spectral parameters were established. Although the accuracy of R2 were mostly greater than 0.71, it was further suggested that three-variable model had the best accuracy in R2, the minimal standard deviation (S) and root mean square error (RMSE), which might provide better prediction results than the other two models. |
|
|
|