欧亚国际

最近科学家利用Airphen多光谱成像系统，发表了题为Combining UAV multispectral imagery and ecological factors to estimate leaf nitrogen and grain protein content of wheat的文章。

热点

利用光谱和结构信息构建的NDVIT在小麦LNC监测和GPC估计中表现良好。

纹理信息可以辅助光谱信息有效地监测小麦LNC和GPC。

结合光谱、纹理和生态因子的人工神经网络模型在小麦GPC估计中表现良好。

有效生态因子是提高小麦GPC预测精度的良好预测因子。

摘要

氮是小麦生长和品质的基本元素。叶片含氮量（LNC）是作物氮素状况的重要监测指标，对以后估算籽粒蛋白质含量（GPC）具有参考作用。无人机（UAV）平台和多光谱传感器的发展为LNC监测和GPC估算提供了新的方法，在无需传统破坏性采样的情况下，为评估植物和谷物的营养状况提供了极大的便利。本研究的目的是评估基于无人机多光谱图像的小麦LNC监测和GPC估计的可行性。2018-2019年在江苏省兴化、昆山和遂宁进行了小麦实验；2020-2021年在江苏省如皋进行了不同品种和施氮量实验。遥感图像由携带多光谱相机的多旋翼无人机获取。采用破坏性采样法采集LNC、GPC等现场数据。选择最优指标，建立小麦LNC监测和GPC估算模型。比较分析采用了不同的建模方法，包括一元线性回归、多元线性回归和人工神经网络（ANN）方法。为提高预测精度，采用了三种方法：（1）用多因素代替单因素进行预测；（2）通过进一步的图像挖掘增加纹理信息；（3）考虑生态因素，改进预测机制。结果表明，基于无人机的Airphen多光谱图像在小麦LNC监测和GPC估计方面具有良好的效果。由红边和近红外波段构建的植被指数在LNC监测和GPC估计中具有良好的性能。加入纹理信息和生态因子进一步提高了建模精度。本研究以拔节期的NDVI（675nm，730nm）、孕穗期的NDVIT（730nm，850nm）、开花期的NDVIT（730，850）和灌浆前期的NDVI（730nm，850nm）建立了最佳小麦GPC估算模型。建模R2、验证R2和相对均方根误差（RRMSE）分别达到0.662、0.7445和0.0635。研究结果为基于无人机多光谱图像的作物LNC监测和GPC估计提供了参考。

关键词

小麦，无人机，多光谱图像，叶片氮含量，籽粒蛋白质含量，纹理，生态因子，人工神经网络

Combining UAV multispectral imagery and ecological factors to estimate leaf nitrogen and grain protein content of wheat

Highlights

The NDVITs constructed by spectral and textural information performed well in wheat LNC monitoring and GPC estimation.

Textural information can assist spectral information to monitor wheat LNC and GPC effectively.

ANNmodel combining spectra, texture and ecological factor performed well in wheat GPC estimation.

Effective ecological factors are good predictors to improve the prediction accuracy of wheat GPC.

Abstract

Nitrogen is an essential element of wheat growth and grain quality. Leaf nitrogen content (LNC), a critical monitoring indicator of crop nitrogen status, plays a reference role for later estimations of grain protein content (GPC). Developments in unmanned aerial vehicle (UAV) platforms and multispectral sensors have provided new approaches for LNC monitoring and GPC estimation, with great convenience for assessing the nutritional status of plants and grains without traditional destructive sampling. The objective of this study was to evalsuate the feasibility of wheat LNC monitoring and GPC estimation based on UAV multispectral imagery. Wheat experiments were carried out in Xinghua, Kunshan and Suining of Jiangsu Province during 2018−2019 and in Rugao of Jiangsu Province during 2020−2021 with different varieties and nitrogen application rates. Remote sensing images were obtained by a multi-rotor UAV carrying a multispectral camera. The destructive sampling method was used to collect LNC, GPC and other field data. Wheat LNC monitoring and GPC estimation models were established after selection of the optimal indicators. Different modelling methods were used for the comparative analysis, including unitary linear regression, multiple linear regression and artificial neural network (ANN) methods. Three techniques were adopted to improve the GPC prediction accuracy: (1) multiple factors were substituted for single factor for the prediction; (2) texture information was added through further imagery mining; and (3) ecological factors were considered to improve the prediction mechanism. The results showed that the use of UAV-based Airphen multispectral imagery had a good effect on wheat LNC monitoring and GPC estimation. The vegetation indices constructed by red-edge and near-infrared bands had good performances in LNC monitoring and GPC estimation. The addition of texture information and ecological factors further improved the modelling accuracy. In this study, the optimal wheat GPC estimation model was established by NDVI (675, 730) at the jointing stage, NDVIT (730mea., 850) at the booting stage, NDVIT (730mea., 850) at the flowering stage and NDVI (730, 850) at the early filling stage. The modelling R2, validation R2 and relative root mean square error (RRMSE) reached 0.662, 0.7445 and 0.0635, respectively. The results provide a reference for crop LNC monitoring and GPC estimation based on UAV multispectral imagery.

Keywords

Wheat UAV multispectral imagery Leaf nitrogen content Grain protein content Texture Ecological factors Artificial Neural Network