数字农科院2.0

Improved Estimation of Cotton Aboveground Biomass Using a New Developed Multispectral Vegetation Index and Particle Swarm Optimization

文献类型： 外文期刊

作者： Wu, Guanyu;Hou, Mingyu;Wang, Yuqiao;Sun, Hongchun;Liu, Liantao;Zhang, Ke;Zhu, Lingxiao;Jin, Xiuliang;Li, Cundong;Zhang, Yongjiang

作者机构：

关键词： unmanned aerial multispectral remote sensing;cotton;aboveground biomass;vegetation index;machine learning algorithm

期刊名称： AGRICULTURE-BASEL

ISSN：

年卷期： 2025 年 15 卷 24 期

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收录情况： SCIE（2025版）

摘要： Accurate and rapid estimation of aboveground biomass (AGB) in cotton is crucial for precise agricultural management. However, current AGB estimation methods are limited by data homogeneity and insufficient model accuracy, which fail to comprehensively reflect the cotton growth status. This study introduces a novel approach by coupling cotton canopy Soil and Plant Analyzer Development (SPAD) values with multispectral (MS) data to achieve precise estimation of cotton AGB. Two experimental treatments, involving varied nitrogen fertilizer rates and organic manure applications, were conducted from 2022 to 2023. MS data from UAVs were collected across multiple cotton growth stages, while AGB and canopy SPAD values were synchronously measured. Using the coefficient of variation method, SPAD values were coupled with existing vegetation indices to develop a novel vegetation index termed CGSIVI. Moreover, the applicability of various machine learning algorithms-including Random Forest Regressor (RFR), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Particle Swarm Optimization-XGBoost (PSO-XGBoost), and Particle Swarm Optimization-CatBoost (PSO-CatBoost)-was evaluated for inverting cotton AGB. The results indicated that, compared to the original vegetation indices, the correlation between the improved vegetation index (CGSIVI) and AGB was enhanced by 13.60% overall, with the CGSICIre exhibiting the highest correlation with cotton AGB (R2 = 0.87). The overall AGB estimation accuracy across different growth stages, spanning the entire growth period, ranged from 0.768 to 0.949, peaking during the flowering stage. Furthermore, when the CGSIVI was used as an input parameter in comparisons of different machine learning algorithms, the PSO-XGBoost algorithm demonstrated superior estimation accuracy across the entire growth stage and within individual growth stages. This high-throughput crop phenotyping analysis method enables rapid and accurate estimation. It reveals the spatial heterogeneity of cotton growth status, thereby providing a powerful tool for accurately identifying growth differences in the field.

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