| WHEAT BIOMASS ESTIMATION FROM UAV IMAGERY USING AN ENSEMBLE LEARNING APPROACH WITH BAYESIAN OPTIMIZATION |
| Paper ID : 1047-GEOSPATIAL (R6) |
| Authors: |
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Arastou Zarei *1, Fatemeh Moradi2, Sadegh Ranjbar3, Saeid Homayouni4 1University of TehranDepartment of Surveying and Geomatics Engineering 2School of Surveying and Geospatial Engineering, University of Tehran 3Department of Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI, USA 4Centre Eau Terre Environnement Research, Institut National de la Recherche Scientifique (INRS), Quebec G1K 9A9, Canada |
| Abstract: |
| Wheat is one of the most important food supply and food security globally, especially in developing countries. Therefore, predicting the performance and determining the factors that affect the production of this product is very important. Biomass is one of the crop’s most important biophysical parameters, and its correct estimation can help improve accurate monitoring of growth and crop performance forecasting. With the recent advances in remote sensing, access to aerial images taken by unmanned aerial vehicles (UAV) for monitoring crops has been provided. This study investigates the potential of visible UAV images and the resulting vegetation indices to estimate the dry biomass of two types of Brazilian wheat. For this purpose, the performance of three regression algorithms, including Random Forest (RF), eXtreme Gradient Boosting (XGB), and Gradient Boosting Machine (GBM), to estimate wheat biomass was evaluated. Also, to improve the performance of regression models, Bayesian optimization (BO) was used to adjust the Hyper-parameters, and random forest feature selection was used to select the optimal subset of features. Based on the results, the XGB algorithm with the Root Mean Square Error (RMSE) of about 911.86 (Kg/ha) and coefficient of determination (R2) of about 0.89% showed better performance in biomass estimation than other algorithms. |
| Keywords: |
| Wheat Biomass, UAV Images, Ensemble Learning, Bayesian Optimization, Feature Importance. |
| Status : Paper Accepted (Oral Presentation) |
