Optimizing window size and directional parameters of GLCM texture features for estimating rice AGB based on UAVs multispectral imagery

Jikai Liu; Yongji Zhu; Lijuan Song; Xiangxiang Su; Jun Li; Jing Zheng; Xueqing Zhu; Lantian Ren; Wenhui Wang; Xinwei Li

doi:10.3389/fpls.2023.1284235

Optimizing window size and directional parameters of GLCM texture features for estimating rice AGB based on UAVs multispectral imagery

Front Plant Sci. 2023 Dec 19:14:1284235. doi: 10.3389/fpls.2023.1284235. eCollection 2023.

Authors

Jikai Liu^#^{1

2}, Yongji Zhu^#¹, Lijuan Song^{3

4}, Xiangxiang Su¹, Jun Li¹, Jing Zheng⁵, Xueqing Zhu¹, Lantian Ren^{2

6}, Wenhui Wang⁵, Xinwei Li^{1

2}

Affiliations

¹ College of Resource and Environment, Anhui Science and Technology University, Chuzhou, Anhui, China.
² Anhui Province Crop Intelligent Planting and Processing Technology Engineering Research Center, Anhui Science and Technology University, Chuzhou, Anhui, China.
³ Institute of Agricultural Remote Sensing and Information, Heilongjiang Academy of Agricultural Sciences, Harbin, Heilongjiang, China.
⁴ School of Management, Heilongjiang University of Science and Technology, Harbin, Heilongjiang, China.
⁵ College of Life Science, Langfang Normal University, Langfang, Hebei, China.
⁶ College of Agriculture, Anhui Science and Technology University, Chuzhou, Anhui, China.

^# Contributed equally.

Abstract

Aboveground biomass (AGB) is a crucial physiological parameter for monitoring crop growth, assessing nutrient status, and predicting yield. Texture features (TFs) derived from remote sensing images have been proven to be crucial for estimating crops AGB, which can effectively address the issue of low accuracy in AGB estimation solely based on spectral information. TFs exhibit sensitivity to the size of the moving window and directional parameters, resulting in a substantial impact on AGB estimation. However, few studies systematically assessed the effects of moving window and directional parameters for TFs extraction on rice AGB estimation. To this end, this study used Unmanned aerial vehicles (UAVs) to acquire multispectral imagery during crucial growth stages of rice and evaluated the performance of TFs derived with different grey level co-occurrence matrix (GLCM) parameters by random forest (RF) regression model. Meanwhile, we analyzed the importance of TFs under the optimal parameter settings. The results indicated that: (1) the appropriate window size for extracting TFs varies with the growth stages of rice plant, wherein a small-scale window demonstrates advantages during the early growth stages, while the opposite holds during the later growth stages; (2) TFs derived from 45° direction represent the optimal choice for estimating rice AGB. During the four crucial growth stages, this selection improved performance in AGB estimation with R² = 0.76 to 0.83 and rRMSE = 13.62% to 21.33%. Furthermore, the estimation accuracy for the entire growth season is R² =0.84 and rRMSE =21.07%. However, there is no consensus regarding the selection of the worst TFs computation direction; (3) Correlation (Cor), Mean, and Homogeneity (Hom) from the first principal component image reflecting internal information of rice plant and Contrast (Con), Dissimilarity (Dis), and Second Moment (SM) from the second principal component image expressing edge texture are more important to estimate rice AGB among the whole growth stages; and (4) Considering the optimal parameters, the accuracy of texture-based AGB estimation slightly outperforms the estimation accuracy based on spectral reflectance alone. In summary, the present study can help researchers confident use of GLCM-based TFs to enhance the estimation accuracy of physiological and biochemical parameters of crops.

Keywords: aboveground biomass (AGB); grey level co-occurrence matrix (GLCM); multispectral imagery; rice; texture features (TFs); unmanned aerial vehicles (UAVs).

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This research was funded by scientific research projects in higher education institutions of Anhui Province (no. 2022AH051623; 2023AH051855); Provincial Scientific Research Service Expense Project (no. CZKYF2021-2-B010); Anhui Province Crop Intelligent Planting and Processing Technology Engineering Research Center Open Research Project(no. ZHZZKF202306); Natural Science Foundation of Hebei Province (no. C2020408006; C2023408010), and College Students' Innovation and Entrepreneur ship Training Project (no. 202210879043).