High-throughput estimation of aboveground peanut biomass with optimized spectral–textural features from unmanned aerial vehicle multispectral imagery

Liya HU; Bo BAI; Juntao YANG; Mingxuan SONG; Youwei LI; Dandan LIU; Zhenhai LI; Yirou LIU; Guowei LI

doi:10.1631/ENG.ITEE.2026.0108

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High-throughput estimation of aboveground peanut biomass with optimized spectral–textural features from unmanned aerial vehicle multispectral imagery

Regular Papers | Updated：2026-08-28

- High-throughput estimation of aboveground peanut biomass with optimized spectral–textural features from unmanned aerial vehicle multispectral imagery
- High-throughput estimation of aboveground peanut biomass with optimized spectral–textural features from unmanned aerial vehicle multispectral imagery
- ENGINEERING Information Technology & Electronic Engineering Vol. 27, Issue 8, Pages: 1-12(2026)
- Affiliations：
  
  College of Geodesy and Geomatics, Shandong University of Science and Technology, Qingdao 266590, China
  Institute of Crop Germplasm Resources, Shandong Academy of Agricultural Sciences, Jinan 250100, China
- Author bio：
  
  ✉Juntao YANG, jtyang@sdust.edu.cn
  Guowei LI, liguowei@sdnu.edu.cn
- Funds：
- DOI：10.1631/ENG.ITEE.2026.0108
  CLC： TP79
- Received：17 April 2026，
  
  Revised：2026-07-18，
  
  Online First：26 August 2026，
  
  Published：01 August 2026
- Accepted：
Scan QR Code
Liya HU, Bo BAI, Juntao YANG, et al. High-throughput estimation of aboveground peanut biomass with optimized spectral–textural features from unmanned aerial vehicle multispectral imagery[J]. ENGINEERING Information Technology & Electronic Engineering, 2026, 27(8): 1-12.
DOI：

Liya HU, Bo BAI, Juntao YANG, et al. High-throughput estimation of aboveground peanut biomass with optimized spectral–textural features from unmanned aerial vehicle multispectral imagery[J]. ENGINEERING Information Technology & Electronic Engineering, 2026, 27(8): 1-12. DOI： 10.1631/ENG.ITEE.2026.0108.

摘要

Abstract

Accurate and efficient estimation of aboveground biomass (AGB) is important for peanut phenotyping and field management. This study evaluates spectral and textural features derived from unmanned aerial vehicle (UAV) multispectral imagery for nondestructive and high-throughput estimation of peanut AGB. Vegetation indices (VIs) and gray-level co-occurrence matrix (GLCM) texture features (TFs) are derived from the green

red

red-edge

and near-infrared bands and assessed via six regression models. The results reveal that near-infrared TFs exhibit the highest sensitivity to AGB. Among the GLCM configurations

a setting of a 7×7 window size

a 45° orientation

and 16 gray levels produces the most stable texture representation. Although combining all VIs and TFs slightly improves prediction accuracy

the relatively large differences between the coefficient of determination

(

http://notExist.jpg

3.04800010

2.53999996

) and adjusted

http://notExist.jpg

3.04800010

2.53999996

in some models suggest that the full feature set contains redundant predictors and has limited model parsimony. An extreme gradient boosting (XGBoost)–Shapley additive explanation (SHAP) feature selection strategy is therefore used to identify five key variables: difference vegetation index (DVI)

variance

mean

energy

and modified soil-adjusted vegetation index (MSAVI). This compact feature set substantially reduces predictor dimensionality

limits the differences between

http://notExist.jpg

3.04800010

2.53999996

and adjusted

http://notExist.jpg

3.04800010

2.53999996

to below 0.020

and achieves consistent predictive accuracy

with

http://notExist.jpg

3.04800010

2.53999996

above 0.840 and the root mean square error (

http://notExist.jpg

8.63599968

2.28600001

) below 0.055 kg/m

. The proposed approach provides a practical tool for high-throughput peanut biomass monitoring

phenotyping

and production management.

关键词

Keywords

references

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