Marzougui Afef, McGee Rebecca J, Van Vleet Stephen, Sankaran Sindhuja
Department of Biological Systems Engineering, Washington State University, Pullman, WA, United States.
United States Department of Agriculture-Agricultural Research Service (USDA-ARS), Grain Legume Genetics and Physiology Research Unit, Washington State University, Pullman, WA, United States.
Front Plant Sci. 2023 Mar 3;14:1111575. doi: 10.3389/fpls.2023.1111575. eCollection 2023.
Remote sensing using unmanned aerial systems (UAS) are prevalent for phenomics and precision agricultural applications. The high-resolution data for these applications can provide useful spectral characteristics of crops associated with performance traits such as seed yield. With the recent availability of high-resolution satellite imagery, there has been growing interest in using this technology for plot-scale remote sensing applications, particularly those related to breeding programs. This study compared the features extracted from high-resolution satellite and UAS multispectral imagery (visible and near-infrared) to predict the seed yield from two diverse plot-scale field pea yield trials (advanced breeding and variety testing) using the random forest model.
The multi-modal (spectral and textural features) and multi-scale (satellite and UAS) data fusion approaches were evaluated to improve seed yield prediction accuracy across trials and time points. These approaches included both image fusion, such as pan-sharpening of satellite imagery with UAS imagery using intensity-hue-saturation transformation and additive wavelet luminance proportional approaches, and feature fusion, which involved integrating extracted spectral features. In addition, we also compared the image fusion approach to high-definition satellite data with a resolution of 0.15 m/pixel. The effectiveness of each approach was evaluated with data at both individual and combined time points.
The major findings can be summarized as follows: (1) the inclusion of the texture features did not improve the model performance, (2) the performance of the model using spectral features from satellite imagery at its original resolution can provide similar results as UAS imagery, with variation depending on the field pea yield trial under study and the growth stage, (3) the model performance improved after applying multi-scale, multiple time point feature fusion, (4) the features extracted from the pan-sharpened satellite imagery using intensity-hue-saturation transformation (image fusion) showed better model performance than those with original satellite imagery or high definition imagery, and (5) the green normalized difference vegetation index and transformed triangular vegetation index were identified as key features contributing to high model performance across trials and time points. These findings demonstrate the potential of high-resolution satellite imagery and data fusion approaches for plot-scale phenomics applications.
使用无人机系统（UAS）进行遥感在植物表型组学和精准农业应用中很普遍。这些应用的高分辨率数据可以提供与诸如种子产量等性能性状相关的作物有用光谱特征。随着高分辨率卫星图像的近期可得，人们越来越有兴趣将这项技术用于地块尺度的遥感应用，特别是那些与育种计划相关的应用。本研究比较了从高分辨率卫星和无人机多光谱图像（可见光和近红外）中提取的特征，以使用随机森林模型预测来自两个不同地块尺度的豌豆产量试验（高级育种和品种测试）的种子产量。
评估了多模态（光谱和纹理特征）和多尺度（卫星和无人机）数据融合方法，以提高跨试验和时间点的种子产量预测准确性。这些方法包括图像融合，例如使用强度 - 色调 - 饱和度变换和加法小波亮度比例方法对卫星图像与无人机图像进行全色锐化，以及特征融合，即整合提取的光谱特征。此外，我们还将图像融合方法与分辨率为0.15米/像素的高清卫星数据进行了比较。在个体和组合时间点的数据上评估了每种方法的有效性。
主要发现可总结如下：（1）包含纹理特征并未提高模型性能；（2）使用原始分辨率的卫星图像光谱特征的模型性能可以提供与无人机图像相似的结果，其变化取决于所研究的豌豆产量试验和生长阶段；（3）应用多尺度、多个时间点特征融合后模型性能有所提高；（4）使用强度 - 色调 - 饱和度变换（图像融合）从全色锐化卫星图像中提取的特征显示出比原始卫星图像或高清图像更好的模型性能；（5）绿色归一化差异植被指数和变换后的三角植被指数被确定为在跨试验和时间点有助于高模型性能的关键特征。这些发现证明了高分辨率卫星图像和数据融合方法在地块尺度植物表型组学应用中的潜力。
