基于无人机多源数据的花生表型估算模型

Peanut Phenotype Estimation Model Based on Multi-source Data from Unmanned Aerial Vehicles

摘要:

花生作为重要的油料作物，对粮油产量安全起到至关重要的作用，准确、无损、实时的表型监测对花生生产管理具有重要意义。本研究利用无人机平台获取关键生育期多光谱及图像数据，提取冠层光谱（Multispectral, MS）、结构（Canopy height model, CHM）和纹理参数（Textural, TEX）信息，采用偏最小二乘回归（Partial least squares regression, PLSR）、支持向量机（Support vector machine, SVM）、人工神经网络（Artificial neural network, ANN）和随机森林回归（Random forest regression, RFR）4种算法构建花生株高、叶片叶绿素相对含量（SPAD值）、地上部生物量估算模型。研究结果表明：花生地上生物量和株高与近红外波段有强相关性（皮尔森相关系数分别为0.77和0.69），融合纹理、结构和光谱特征后的随机森林模型取得了对生物量最优的模型反演效果（决定系数R2为0.96），融合纹理和光谱特征后的偏最小二乘回归模型对株高的反演效果最优（R2为0.94）；融合纹理和结构特征后的偏最小二乘回归对SPAD值反演效果相对较好(R2为0.39，均方根误差（RMSE）为3.06，归一化均方根误差（nRMSE）为0.062，百分偏差比率（RPD）为1.30）。本研究明确了不同机器学习方法对花生不同表型指标估算所需的特征指标，构建的基于无人机多源数据的表型估算模型可以实现对花生株高和生物量的准确、无损、高效估算，为花生长势监测和生产管理提供了一种有效技术手段。

Abstract:

Peanut (Arachis hypogaea L.), a critical oilseed crop, plays a crucial role in ensuring food and oil production security. Accurate, nondestructive, and real-time phenotypic monitoring is essential for optimizing peanut production management. Multispectral data acquired by an unmanned aerial vehicle (UAV) platform during key growth stages were leveraged to extract canopy multispectral (MS), structural (CHM), and textural (TEX) parameters. Four machine learning algorithms, partial least squares regression (PLSR), support vector machine (SVM), artificial neural network (ANN), and random forest regression (RFR), were employed to construct estimation models for plant height, SPAD values, and aboveground biomass. Results demonstrated strong correlations between peanut aboveground biomass/plant height and the near-infrared band (Pearson correlation coefficients were 0.77 and 0.69, respectively). The random forest model, integrating textural, structural, and spectral features, achieved optimal biomass estimation accuracy (R2=0.96). For plant height inversion, the PLSR model combining textural and spectral features performed best (R2=0.94). SPAD estimation using PLSR with fused textural and structural features yielded moderate accuracy (R2=0.39, RMSE=3.06, nRMSE=0.062, RPD=1.30). The research identified feature-specific requirements for machine learning-based estimation of distinct peanut phenotypic traits and established a UAV multi-source data fusion framework capable of accurate, nondestructive, and efficient assessment of plant height and biomass. These findings can provide a robust technical approach for growth monitoring and precision management in peanut cultivation systems.