| Estimation of rapeseed leaf SPAD value based on random forest regression |
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| DOI：10.7606/j.issn.1000-7601.2019.01.10 |
| Key Words: rapeseed SPAD value remote sensing random forest model estimation |
| Author Name | Affiliation | | YOU Ming-ming | College of Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100, China | | CHANG Qing-rui | College of Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100, China | | TIAN Ming-lu | College of Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100, China | | BAN Song-tao | College of Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100, China | | YU Jiao-yang | College of Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100, China | | ZHANG Zhuo-ran | College of Resources and Environment, Northwest A&F University, Yangling, Shaanxi 712100, China |
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| Abstract: |
| The chlorophyll content is an importance parameter for evaluating crop growth and real-time non-destructive and quick estimation of leaf chlorophyll content can provide important information about plant stress, nutritional status, and relationships between plants and their environment, which has great significance in guiding agricultural production and improving crop yield. The conclusions are as follows: 1) With the increase of SPAD value, the leaf spectral reflectance decreased in the visible light region, and the “red shift” phenomena were detected at the red edge position. 2) The correlations between rapeseed leaf SPAD values and spectral indices were significant in all growth periods. Among the spectral indices, TCARI, GRVI, and NPCI were negatively correlated with leaf SPAD values, while the rest of spectral indices were positively correlated with the leaf SPAD values. 3) Leaf SPAD value estimation models using the traditional simple linear regression method, multiple stepwise regression method, and Random Forest method base on the spectral indices all passed the significance tests. In order to evaluate each model’s estimation accuracy and to further compare the performances of the three models for each stage, the coefficient of determination (R2) of each model was calculated respectively for both modeling sets and validation sets. The results indicated that random forest model had the best modeling and verification accuracy in each growth period with the coefficient of determination higher than 0.91 for the modeling and greater than 0.74 for the validation set. Therefore, Random Forest Model is an optimal model for estimating rapeseed leaf SPAD values and may provide a theoretical basis and technical support to improve remote sensing inversion accuracy of rapeseed chlorophyll content. |
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