Moradi, Fatemeh; Zarei, Arastou; Ranjbar, Shivendu et Homayouni, Saeid 
ORCID: https://orcid.org/0000-0002-0214-5356
(2023).
Wheat biomass estimation from uav imagery using an ensemble learning approach with bayesian optimization.
In: Sensors and Models in Photogrammetry and Remote Sensing Conference 2023, 19-22 février 2023, Tehran, Iran.
Résumé
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.
| Type de document: | Document issu d'une conférence ou d'un atelier |
|---|---|
| Mots-clés libres: | wheat biomass; UAV images; ensemble learning; bayesian optimization; feature importance |
| Centre: | Centre Eau Terre Environnement |
| Date de dépôt: | 19 sept. 2025 19:22 |
| Dernière modification: | 19 sept. 2025 19:26 |
| URI: | https://espace.inrs.ca/id/eprint/16050 |
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