WHEAT BIOMASS ESTIMATION FROM UAV IMAGERY USING AN ENSEMBLE LEARNING APPROACH WITH BAYESIAN OPTIMIZATION

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 param...

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Bibliographic Details
Published in:ISPRS annals of the photogrammetry, remote sensing and spatial information sciences remote sensing and spatial information sciences, 2023-01, Vol.X-4/W1-2022, p.515-522
Main Authors: Moradi, F., Zarei, A., Ranjbar, S., Homayouni, S.
Format: Article
Language:English
Subjects:
Aerial photography
Algorithms
Bayesian analysis
Biomass
Crops
Developing countries
Ensemble learning
Food security
Food supply
LDCs
Machine learning
Monitoring
Optimization
Parameters
Performance enhancement
Performance prediction
Regression models
Remote sensing
Root-mean-square errors
Unmanned aerial vehicles
Vegetation index
Wheat
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Summary: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.
ISSN:2194-9050
2194-9042
2194-9050
DOI:10.5194/isprs-annals-X-4-W1-2022-515-2023