Abstract: Nitrogen is an indispensable component for the growth and development of fruit trees. Nitrogen content beyond the normal range will affect the growth and development of trees, and directly or indirectly reduce the yield and quality of fruits. Therefore, rapid and accurate grasp of the nitrogen content of fruit trees can provide technical support for precise fertilization, so as to achieve high quality and high yield of fruit trees. With the rapid development of UAV industry, UAV remote sensing monitoring plays an important role in nitrogen content monitoring because of its advantages of non-destructive, fast, real-time and high efficiency. On the basis of introducing the current mainstream UAVs, this paper combs the data acquisition and subsequent processing methods, and expounds the monitoring of nitrogen content in fruit trees by multispectral, hyperspectral, visible light and other types of sensors. In general, multispectral and hyperspectral sensors have better effect on nitrogen monitoring of fruit trees, and the model constructed by machine learning method has higher accuracy than traditional methods. Finally, the deficiencies and future development direction of UAV remote sensing monitoring of fruit tree nitrogen content in four aspects of UAV flight platform and sensor performance, data acquisition and processing, promotion and application, and use policy were put forward, so as to provide reference for the accuracy, efficiency and intelligence of UAV remote sensing monitoring of fruit tree nitrogen content in China.

Key words: UAV, remote sensing, fruit tree, nitrogen content, machine learning

摘要： 氮素是果树生长发育不可或缺的成分,氮素含量超出正常范围会影响树体生长发育,会直接或间接降低果实产量及品质。快速准确掌握果树氮素含量,可为精准施肥提供技术支撑,从而达到果树的优质丰产。随着无人机产业的快速发展,无人机遥感监测以其无损、快速、实时、高效等优点在氮素含量监测中发挥着重要作用。在介绍目前主流无人机的基础上,梳理数据获取及后续处理方式,阐述多光谱、高光谱、可见光以及其他类型传感器实现果树氮素含量监测研究现状。可以发现,多光谱和高光谱传感器对果树氮素监测效果更佳,且使用机器学习方法构建模型相较于传统方法具有更高精度。提出无人机遥感监测果树氮素含量在无人机飞行平台与传感器性能、数据获取与处理、推广与应用及政策4个方面现阶段存在的不足之处和未来精准化、高效化和智能化的发展方向。

关键词: 无人机, 遥感, 果树, 氮素含量, 机器学习