Crop stress detection from UAVs: best practices and lessons learned for exploiting sensor synergies

IF 5.4 2区 农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY
Erekle Chakhvashvili, Miriam Machwitz, Michal Antala, Offer Rozenstein, Egor Prikaziuk, Martin Schlerf, Paul Naethe, Quanxing Wan, Jan Komárek, Tomáš Klouek, Sebastian Wieneke, Bastian Siegmann, Shawn Kefauver, Marlena Kycko, Hamadou Balde, Veronica Sobejano Paz, Jose A. Jimenez-Berni, Henning Buddenbaum, Lorenz Hänchen, Na Wang, Amit Weinman, Anshu Rastogi, Nitzan Malachy, Maria-Luisa Buchaillot, Juliane Bendig, Uwe Rascher
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引用次数: 0

Abstract

Introduction

Detecting and monitoring crop stress is crucial for ensuring sufficient and sustainable crop production. Recent advancements in unoccupied aerial vehicle (UAV) technology provide a promising approach to map key crop traits indicative of stress. While using single optical sensors mounted on UAVs could be sufficient to monitor crop status in a general sense, implementing multiple sensors that cover various spectral optical domains allow for a more precise characterization of the interactions between crops and biotic or abiotic stressors. Given the novelty of synergistic sensor technology for crop stress detection, standardized procedures outlining their optimal use are currently lacking.

Materials and methods

This study explores the key aspects of acquiring high-quality multi-sensor data, including the importance of mission planning, sensor characteristics, and ancillary data. It also details essential data pre-processing steps like atmospheric correction and highlights best practices for data fusion and quality control.

Results

Successful multi-sensor data acquisition depends on optimal timing, appropriate sensor calibration, and the use of ancillary data such as ground control points and weather station information. When fusing different sensor data it should be conducted at the level of physical units, with quality flags used to exclude unstable or biased measurements. The paper highlights the importance of using checklists, considering illumination conditions and conducting test flights for the detection of potential pitfalls.

Conclusion

Multi-sensor campaigns require careful planning not to jeopardise the success of the campaigns. This paper provides practical information on how to combine different UAV-mounted optical sensors and discuss the proven scientific practices for image data acquisition and post-processing in the context of crop stress monitoring.

Abstract Image

无人机作物胁迫检测:利用传感器协同作用的最佳做法和经验教训
引言 检测和监控作物胁迫对于确保作物的充足和可持续生产至关重要。无人飞行器(UAV)技术的最新进展为绘制指示作物胁迫的关键作物特征图提供了一种前景广阔的方法。虽然使用安装在无人飞行器上的单个光学传感器就足以监测一般意义上的作物状况,但采用覆盖不同光谱光学域的多个传感器,可以更精确地描述作物与生物或非生物胁迫因素之间的相互作用。材料与方法 本研究探讨了获取高质量多传感器数据的关键方面,包括任务规划、传感器特性和辅助数据的重要性。结果多传感器数据的成功获取取决于最佳的时间安排、适当的传感器校准以及辅助数据(如地面控制点和气象站信息)的使用。在融合不同传感器数据时,应在物理单位层面上进行,并使用质量标志排除不稳定或有偏差的测量结果。本文强调了使用核对表、考虑照明条件和进行试飞以发现潜在隐患的重要性。本文提供了关于如何结合不同的无人机光学传感器的实用信息,并讨论了在作物胁迫监测中图像数据采集和后处理的成熟科学实践。
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来源期刊
Precision Agriculture
Precision Agriculture 农林科学-农业综合
CiteScore
12.30
自引率
8.10%
发文量
103
审稿时长
>24 weeks
期刊介绍: Precision Agriculture promotes the most innovative results coming from the research in the field of precision agriculture. It provides an effective forum for disseminating original and fundamental research and experience in the rapidly advancing area of precision farming. There are many topics in the field of precision agriculture; therefore, the topics that are addressed include, but are not limited to: Natural Resources Variability: Soil and landscape variability, digital elevation models, soil mapping, geostatistics, geographic information systems, microclimate, weather forecasting, remote sensing, management units, scale, etc. Managing Variability: Sampling techniques, site-specific nutrient and crop protection chemical recommendation, crop quality, tillage, seed density, seed variety, yield mapping, remote sensing, record keeping systems, data interpretation and use, crops (corn, wheat, sugar beets, potatoes, peanut, cotton, vegetables, etc.), management scale, etc. Engineering Technology: Computers, positioning systems, DGPS, machinery, tillage, planting, nutrient and crop protection implements, manure, irrigation, fertigation, yield monitor and mapping, soil physical and chemical characteristic sensors, weed/pest mapping, etc. Profitability: MEY, net returns, BMPs, optimum recommendations, crop quality, technology cost, sustainability, social impacts, marketing, cooperatives, farm scale, crop type, etc. Environment: Nutrient, crop protection chemicals, sediments, leaching, runoff, practices, field, watershed, on/off farm, artificial drainage, ground water, surface water, etc. Technology Transfer: Skill needs, education, training, outreach, methods, surveys, agri-business, producers, distance education, Internet, simulations models, decision support systems, expert systems, on-farm experimentation, partnerships, quality of rural life, etc.
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