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 evaluate 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, NDVI_T (730_mea., 850) at the booting stage, NDVI_T (730_mea., 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.

氮是小麦生长和谷物品质的重要元素。叶片氮含量（LNC）是作物氮素状态的重要监测指标，对后期谷物蛋白质含量（GPC）的估算具有参考作用。无人机 (UAV) 平台和多光谱传感器的发展为 LNC 监测和 GPC 估计提供了新方法，极大地方便了评估植物和谷物的营养状况，而无需传统的破坏性采样。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、昆山和遂宁进行小麦试验，2020-2021年在江苏省如皋进行了不同品种和施氮量的小麦试验。遥感图像是由携带多光谱相机的多旋翼无人机获得的。采用破坏性采样法采集LNC、GPC等现场数据。选择最优指标后，建立小麦LNC监测和GPC估算模型。不同的建模方法被用于比较分析，包括单一线性回归、多元线性回归和人工神经网络（ANN）方法。采用三种技术提高GPC预测精度：（1）多因素代替单因素进行预测；(2) 通过进一步的图像挖掘添加纹理信息；(3)考虑生态因素，完善预测机制。结果表明，使用基于无人机的Airphen多光谱图像对小麦LNC监测和GPC估计有很好的效果。红边和近红外波段构建的植被指数在LNC监测和GPC估计方面具有良好的性能。纹理信息和生态因素的加入进一步提高了建模精度。本研究利用拔节期NDVI(675, 730)建立最佳小麦GPC估计模型，NDVI_Ť（730 _MEA。，850）在孕穗期，NDVI _Ť（730 _MEA。，850）在开花期和NDVI（730，850）在灌浆初期。建模R ²、验证R ²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影像的作物LNC监测和GPC估计提供参考。