Methods and New Research Progress of Remote Sensing Monitoring of Crop Disease and Pest Stress Using Unmanned Aerial Vehicle

doi:10.12133/j.smartag.SA202201008

Abstract

Abstract:

Diseases and pests are main stresses to crop production. It is necessary to accurately and quickly monitor and control the stresses dynamically, so as to ensure the food security and the quality and safety of agricultural products, protect the ecological environment, and promote the sustainable development of agriculture. In recent years, with the rapid development of the unmanned aerial vehicle (UAV) industry, UAV agricultural remote sensing has played an important role in the application of crop diseases and pests monitoring due to its high image spatial resolution, strong data acquisition timeliness and low cost. The relevant background of UAV remote sensing monitoring of crop disease and pest stress was introduced, then the current methods commonly used in remote sensing monitoring of crop disease and pest stress by UAV was summarized. The data acquisition method and data processing method of UAV remote sensing monitoring of crop disease and pest stress were mainly discussed. Then, from the six aspects of visible light imaging remote sensing, multispectral imaging remote sensing, hyperspectral imaging remote sensing, thermal infrared imaging remote sensing, LiDAR imaging remote sensing and multiple remote sensing fusion and comparison, the research progress of remote sensing monitoring of crop diseases and pests by UAV worldwide was reviewed. Finally, the unresolved key technical problems and future development directions in the research and application of UAV remote sensing monitoring of crop disease and pest stress were proposed. Such as, the performance of the UAV flight platform needs to be optimized and upgraded, as well as the development of low-cost, lightweight, modular, and more adaptable airborne sensors. Convenient and automated remote sensing monitoring tasks need to be designed and implemented, and more remote sensing monitoring information can be obtained. Data processing algorithms or software should be designed and developed with greater applicability and wider applicability, and data processing time should be shortened by using 5G-based communication networks and edge computing devices. The applicability of the algorithm or model for UAV remote sensing monitoring of crop disease and pest stress needs to be stronger, so as to build a corresponding method library. We hope that this paper can help Chinese UAV remote sensing monitoring of crop diseases and pests to achieve more standardization, informatization, precision and intelligence.

Key words: unmanned aerial vehicle, remote sensing monitoring, diseases and pests stress, data acquisition, data processing, deep learning, multiple remote sensing fusion

CLC Number:

S-435

YANG Guofeng, HE Yong, FENG Xuping, LI Xiyao, ZHANG Jinnuo, YU Zeyu. Methods and New Research Progress of Remote Sensing Monitoring of Crop Disease and Pest Stress Using Unmanned Aerial Vehicle[J]. Smart Agriculture, 2022, 4(1): 1-16.

Figures/Tables 4

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类型	载荷/kg	续航时间/min	应用范围	优点	缺点
多旋翼无人机^{［10，11］}	1~25	10~120	小面积	自主导航与起降、定点悬停、多载荷、起降方便	续航较短、信号易受干扰
固定翼无人机^{［12，13］}	1~40	30~240	大面积	自主导航、续航长、多载荷、起降方便、飞行速度快	无法定点悬停、飞行速度无法快速改变、起降要求较高
单旋翼无人机^{［14，15］}	1~35	30~240	中等面积	垂直起降、定点悬停、多载荷、起降方便	续航较短、稳定性较差、油耗高、噪声较大、维护复杂、速度较低
混合翼无人机^{［16，17］}	1~30	60~600	中等面积	续航较长、稳定性好、起降方便、飞行速度较快	维护复杂、造价较高

传感器	测量指标	优势	劣势
多光谱相机	氮素、水分、覆盖度、叶面积、生物量、病虫害胁迫等	波段较多、信息量较多、类型多	仅限于有限的几个波段、价格较高、成像速度慢、分辨率较低
高光谱相机	覆盖度、叶面积、生物量、产量、病虫害胁迫等	波段多、分辨率高、类型多	数据处理复杂、价格高
数码相机	倒伏、可见外部伤害、生长状况	分辨率高、使用方便、价格低、类型多	信息量少、波段少
热红外相机	温度、水分等	非接触测温、使用较方便	价格较高、分辨率低、环境影响较大、精度较低
激光雷达	株高、叶面积、生物量等	分辨率较高、抗干扰能力较强、不受光线影响	探测距离和范围较小、价格高

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