Combining spectral and textures of digital imagery for wheat aboveground biomass estimation

Ling Zheng; Jianpeng Tao; Qian Bao; Shizhuang Weng; Yakun Zhang; Jinling Zhao; Linsheng Huang

doi:10.1117/12.2639118

23 May 2022 Combining spectral and textures of digital imagery for wheat aboveground biomass estimation

Ling Zheng, Jianpeng Tao, Qian Bao, Shizhuang Weng, Yakun Zhang, Jinling Zhao, Linsheng Huang

Proceedings Volume 12254, International Conference on Electronic Information Technology (EIT 2022); 1225419 (2022) https://doi.org/10.1117/12.2639118
Event: International Conference on Electronic Information Technology (EIT 2022), 2022, Chengdu, China

Abstract

The aboveground biomass (AGB) is a key index for predicting wheat yield. In the case of high biomass, the AGB estimation of single spectral feature or image texture is poor. Therefore, this study evaluated the ability of fusion of spectral reflectance and texture to predict wheat AGB. Among them the reflectance spectrum of the wheat canopy was collected by near-earth spectrometer, and the texture features of three bands of RGB were extracted by gray co-occurrence matrix. Partial least squares regression (PLS) model was used to evaluate the relationship between fusion features and AGB. The experimental results based on the validated data set show that the AGB estimation effect of feature fusion is better than that of single feature (R2 = 0.70; RMSE = 0.06). This shows that the combination of spectral reflectance and texture can improve the accuracy of AGB estimation in the later stage.

Citation Download Citation

Ling Zheng, Jianpeng Tao, Qian Bao, Shizhuang Weng, Yakun Zhang, Jinling Zhao, and Linsheng Huang "Combining spectral and textures of digital imagery for wheat aboveground biomass estimation", Proc. SPIE 12254, International Conference on Electronic Information Technology (EIT 2022), 1225419 (23 May 2022); https://doi.org/10.1117/12.2639118

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