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无人机在作物生物量监测中的应用:综述

Applications of UAS in Crop Biomass Monitoring: A Review.

作者信息

Wang Tianhai, Liu Yadong, Wang Minghui, Fan Qing, Tian Hongkun, Qiao Xi, Li Yanzhou

机构信息

College of Mechanical Engineering, Guangxi University, Nanning, China.

College of Civil Engineering and Architecture, Guangxi University, Nanning, China.

出版信息

Front Plant Sci. 2021 Apr 9;12:616689. doi: 10.3389/fpls.2021.616689. eCollection 2021.

Abstract

Biomass is an important indicator for evaluating crops. The rapid, accurate and nondestructive monitoring of biomass is the key to smart agriculture and precision agriculture. Traditional detection methods are based on destructive measurements. Although satellite remote sensing, manned airborne equipment, and vehicle-mounted equipment can nondestructively collect measurements, they are limited by low accuracy, poor flexibility, and high cost. As nondestructive remote sensing equipment with high precision, high flexibility, and low-cost, unmanned aerial systems (UAS) have been widely used to monitor crop biomass. In this review, UAS platforms and sensors, biomass indices, and data analysis methods are presented. The improvements of UAS in monitoring crop biomass in recent years are introduced, and multisensor fusion, multi-index fusion, the consideration of features not directly related to monitoring biomass, the adoption of advanced algorithms and the use of low-cost sensors are reviewed to highlight the potential for monitoring crop biomass with UAS. Considering the progress made to solve this type of problem, we also suggest some directions for future research. Furthermore, it is expected that the challenge of UAS promotion will be overcome in the future, which is conducive to the realization of smart agriculture and precision agriculture.

摘要

生物量是评估作物的重要指标。对生物量进行快速、准确且无损的监测是智慧农业和精准农业的关键。传统检测方法基于破坏性测量。尽管卫星遥感、有人驾驶航空设备和车载设备能够无损地收集测量数据,但它们受到精度低、灵活性差和成本高的限制。作为具有高精度、高灵活性和低成本的无损遥感设备,无人机系统(UAS)已被广泛用于监测作物生物量。在这篇综述中,介绍了无人机系统平台和传感器、生物量指数以及数据分析方法。介绍了近年来无人机系统在监测作物生物量方面的改进,并对多传感器融合、多指数融合、对与监测生物量不直接相关的特征的考虑、采用先进算法以及使用低成本传感器进行了综述,以突出无人机系统监测作物生物量的潜力。考虑到在解决这类问题方面取得的进展,我们还提出了一些未来研究的方向。此外,预计未来将克服无人机系统推广面临的挑战,这有利于实现智慧农业和精准农业。

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