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

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.

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Publication Details

Journal
Precision Agriculture
Published
2024-08-11
DOI
https://doi.org/10.1007/s11119-024-10168-3
Citations
28
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
12.85

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article

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

Egor Prikaziuk, Bastian Siegmann, Michal Antala, Nitzan Malachy et al.
28 citations
Precision Agriculture
Remote Sensing in Agriculture
12.85
article

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

Egor Prikaziuk, Bastian Siegmann, Michal Antala, Nitzan Malachy, Anshu Rastogi, Marlena Kycko, Shawn C. Kefauver, Sebastian Wieneke, Lorenz Hänchen, Uwe Rascher, Juliane Bendig, Quanxing Wan, Martin Schlerf, Paul Naethe, Hamadou Balde, Offer Rozenstein, José A. Jiménez-Berni, Erekle Chakhvashvili, Miriam Machwitz, Na Wang, Verónica Sobejano Paz, Henning Buddenbaum, Jan Komárek, Tomáš Klouek, Amit Weinman, Maria-Luisa Buchaillot
article en
28 citations

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.

Precision AgricultureVol. 25(5)
Consejo Superior de Investigaciones Científicas (ES), Universitat de Lleida (ES), The University of Melbourne (AU), Forschungszentrum Jülich (DE), Technion – Israel Institute of Technology (IL), Universität Innsbruck (AT), Czech University of Life Sciences Prague (CZ), Agricultural Research Organization (IL), Sorbonne Université (FR), Laboratoire de Météorologie Dynamique (FR), Luxembourg Institute of Science and Technology (LU), Agriculture and Food (AU), National Research Council (IT), University of Warsaw (PL), University of Life Sciences in Poznań (PL), Universitat de Barcelona (ES), Universität Trier (DE), Wageningen University & Research (NL), University of Twente (NL)
European Cooperation in Science and Technology, Deutsche Forschungsgemeinschaft
Responsible consumption and production
Openalex Percentile: Top 1%
Remote Sensing in Agriculture
12.85
DOIOpen Access PDFOpenAlex
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