Early detection of arthropod-induced stress in strawberry using innovative remote sensing technology
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1
University of California, Davis, United States
Innovative imaging technologies are being promoted to automate and optimize crop scouting and detection of emerging pest outbreaks. Due to the large size of farm operations, pest monitoring is time consuming; pests are often detected late, allowing populations to increase. Early, accurate pest detection is key for effective management and control. Spectral imaging, or remote sensing, can be used to indirectly detect pest presence, through stress-induced changes in leaf reflectance (1). However, only a limited number of studies showed that leaf reflectance data could be used to differentiate between stressors (2, 3). We aim to develop a feasible, cost-effective, accurate method for early detection of stress induced by different pests in strawberry, using innovative airborne remote sensing technology.
We hypothesized that herbivores with different feeding biology cause significantly different leaf reflectance responses. To address this hypothesis, potted strawberry plants were experimentally infested with three arthropod pests with different feeding modes: two-spotted spider mite, a sucking pest; silverleaf whitefly, a piercing-sucking pest; and beet armyworm, a chewing pest. Control plants remained non-infested. In a greenhouse setting, we acquired data on leaf reflectance over a 14-day period with a novel, greenhouse-suitable setting, containing a hyperspectral camera mounted onto a rail system. Furthermore, in a commercial strawberry field, we acquired data on leaf reflectance using the same hyperspectral camera, mounted on a drone (4). Leaf samples were collected and inspected for arthropod presence or absence.
Results showed that in a greenhouse setting, leaf reflectance profiles differed between strawberry plants exposed to feeding by the different arthropod herbivores. In a field setting, leaf reflectance profiles differed between areas in the presence or absence of two-spotted spider mite. These results indicate that reflectance in specific wavelengths can be used to detect early pest infestations in strawberry.
Current experiments involve expanded greenhouse trails, aiming to distinguish stress caused by biotic and abiotic stressors in strawberry. Also, we’re performing additional field trials at local strawberry farms. Ultimately, we aim to develop a remote sensing-based decision support tool for improved sustainable pest management on commercial strawberry farms.
Acknowledgements
We thank Alison Stewart, Machiko Murdock, Robert Starnes, April Van Hise, Keshav Singh, Charissa Tseng, Yasmeen Haider, and Rachel Purington (UC Davis) for technical assistance. We also thank Mark Edsall, Daniel Olivier (California Strawberry Commission) and Hillary Thomas (Naturipe Berry Growers), and the commercial growers who made their fields available. This research is supported by Western Sustainable Agriculture Research and Education (project SW17-060, http://www.westernsare.org/).
References
1. Nansen C & Elliott N (2016) Remote sensing and reflectance profiling in entomology. Annual Review of Entomology 61(1):139-158.
2. Yang Z, Rao MN, Elliott NC, Kindler SD, & Popham TW (2009) Differentiating stress induced by greenbugs and Russian wheat aphids in wheat using remote sensing. Computers and Electronics in Agriculture 67(1):64-70.
3. Backoulou GF, Elliott NC, Giles KL, & Mirik M (2015) Processed multispectral imagery differentiates wheat crop stress caused by greenbug from other causes. Computers and Electronics in Agriculture 115:34-39.
4. Singh KD & Nansen C (2017) Advanced calibration to improve robustness of drone-acquired hyperspectral remote sensing data. 2017 6th International Conference on Agro-Geoinformatics, pp 1-6.
Keywords:
hyperspectral remote sensing,
Unmanned aerial systems,
Fragaria x ananassa,
two-spotted spider mite,
Sustainable pest management,
precision agriculture
Conference:
GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data, Davis, United States, 8 Oct - 10 Oct, 2019.
Presentation Type:
Regular oral presentation
Topic:
Emerging GIS, data science and sensor technologies adapted to animal, plant and human health, including precision medicine and precision farming
Citation:
De Lange
ES and
Nansen
C
(2019). Early detection of arthropod-induced stress in strawberry using innovative remote sensing technology.
Front. Vet. Sci.
Conference Abstract:
GeoVet 2019. Novel spatio-temporal approaches in the era of Big Data.
doi: 10.3389/conf.fvets.2019.05.00104
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Received:
28 Aug 2019;
Published Online:
27 Sep 2019.
*
Correspondence:
Dr. Elvira S De Lange, University of California, Davis, Davis, United States, esdelange@ucdavis.edu