作物胁迫无人机遥感监测研究评述

作物胁迫是全球农业发展的一个重要制约因素,实现快速、大范围、实时的作物胁迫监测对于农业生产具有重要意义。传统的作物胁迫监测方式,如田间调查、理化检测和卫星遥感监测总是受到各种田间条件或大气条件的制约。随着无人机和各种轻量化传感器的快速发展,其凭借高频、迅捷等优势为各种作物胁迫监测提供了一套全新的解决方案。本文在介绍了目前主流的多种无人机和传感器的基础上,首先对目前无人机遥感用于作物监测的主要胁迫类型进行了梳理,然后重点阐述了基于光谱成像和热红外传感器进行作物胁迫无人机遥感监测的应用和技术方法,最后提出了作物胁迫无人机遥感监测尚需解决的关键问题,并展望了未来无人机遥感用于作物胁迫监测的前景。

The Use of UAV Remote Sensing Technology to Identify Crop Stress: A Review

Crop stress is an important factor restricting global agricultural development. Monitoring and understanding rapid, large-scale and real-time crop stress is of great significance for agricultural production. However, traditional methods of crop stress monitoring (such as fields surveys, physical and chemical detection, and satellite remote sensing), are strongly influenced by field and atmospheric conditions, temporal and spatial resolution, and labor costs. Rapid development of UAV platforms and various lightweight sensors, provide new solutions for various crop stress monitoring. These offer multiple advantages, primarily high frequency and speed. The introduction of various mainstream UAV platforms such as multi-rotor and fixed-wing, and sensors such as visible light digital camera, multispectral camera, hyperspectral camera, and thermal infrared camera has allowed for more efficient crop monitoring. This review explores the main biotic and abiotic stress types used by UAV remote sensing systems for crop monitoring. Biotic stressors mainly include miscellaneous grass stress, plant diseases, and insect pests stress. Abiotic stressors predominantly include water and nutrient stress. The application and technical methods of UAV remote sensing system monitoring of crop stress, based on spectral imaging and thermal infrared sensor technology are discussed. Sensitive bands and common vegetation indices used for crop stress monitoring are identified. Finally, key issues associated with UAV remote sensing and the future use of UAV remote sensing for crop stress monitoring are discussed. The advancement of UAV remote sensing technology, could contribute to improved identification and monitoring of crop stress in the near future.