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Research paper | Open Access

Estimation of biomass in wheat using random forest regression algorithm and remote sensing data

Li'ai Wanga, Xudong Zhoub, Xinkai Zhua, Zhaodi Donga, Wenshan Guoa( )
Key Laboratory of Crop Genetics and Physiology of Jiangsu Province, Yangzhou University, Yangzhou 225009, China
Information Engineering College of Yangzhou University, Yangzhou 225009, China

Peer review under responsibility of Crop Science Society of China and Institute of Crop Science, CAAS.

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Abstract

Wheat biomass can be estimated using appropriate spectral vegetation indices. However, the accuracy of estimation should be further improved for on-farm crop management. Previous studies focused on developing vegetation indices, however limited research exists on modeling algorithms. The emerging Random Forest (RF) machine-learning algorithm is regarded as one of the most precise prediction methods for regression modeling. The objectives of this study were to (1) investigate the applicability of the RF regression algorithm for remotely estimating wheat biomass, (2) test the performance of the RF regression model, and (3) compare the performance of the RF algorithm with support vector regression (SVR) and artificial neural network (ANN) machine-learning algorithms for wheat biomass estimation. Single HJ-CCD images of wheat from test sites in Jiangsu province were obtained during the jointing, booting, and anthesis stages of growth. Fifteen vegetation indices were calculated based on these images. In-situ wheat above-ground dry biomass was measured during the HJ-CCD data acquisition. The results showed that the RF model produced more accurate estimates of wheat biomass than the SVR and ANN models at each stage, and its robustness is as good as SVR but better than ANN. The RF algorithm provides a useful exploratory and predictive tool for estimating wheat biomass on a large scale in Southern China.

Keywords

Triticum aestivumWheatVegetation indicesAbove-ground dry biomass

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The Crop Journal
Volume 4 Issue 3,
June 2016
Pages 212-219
DOI: 10.1016/j.cj.2016.01.008
Cite this article:
Wang L, Zhou X, Zhu X, et al. Estimation of biomass in wheat using random forest regression algorithm and remote sensing data. The Crop Journal, 2016, 4(3): 212-219. https://doi.org/10.1016/j.cj.2016.01.008

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Received: 15 October 2015
Revised: 29 January 2016
Accepted: 15 March 2016
Published: 30 March 2016
© 2016 Crop Science Society of China and Institute of Crop Science, CAAS.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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