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Using Unmanned Aircraft Systems for Early Detection of Soybean Diseases

Published online by Cambridge University Press:  01 June 2017

C. Brodbeck ,
E. Sikora ,
D. Delaney ,
G. Pate  and
J. Johnson
C. Brodbeck*
Affiliation:
Department of Biosystems Engineering, Auburn University, Auburn, AL, USA
E. Sikora
Affiliation:
Department of Entomology and Plant Pathology, Auburn University, Auburn, AL, USA
D. Delaney
Affiliation:
Department Crop, Soil, and Environmental Science, Auburn University, Auburn, AL, USA
G. Pate
Affiliation:
EV Smith Research Center, Auburn University, Auburn, AL, USA
J. Johnson
Affiliation:
Department of Biosystems Engineering, Auburn University, Auburn, AL, USA
*
†E-mail: [email protected]
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Abstract

As the interest in Unmanned Aerial Systems (UAS) has increased, so has the interest in the application of these systems for use in agriculture. A variety of sensors, including Multi-Spectral, Near-Infrared, Thermal, and True-Color have the potential to benefit farmers when mounted to a UAS. But as this is an emerging field, there is little data available to demonstrate their use for early detection of plant diseases in crop production. In 2016, a preliminary study was launched to examine the potential of using aerial imagery from UAS to detect diseases in soybean crops. Two irrigated fields in Alabama were selected: Experiment 1, a 50-hectare field, and Experiment 2, a 5-hectare field. Each trial consisted of replicated plots using two foliar fungicide treatments and an untreated control. Aerial imagery (multi-spectral and true-color) was collected on a biweekly basis during this study. Using multi-spectral imagery, both the Normalized Difference Vegetative Index (NDVI) and Normalized Difference Red Edge Index (NDRE) were generated and compared to direct observations in the field. Disease severity of soybean rust, charcoal rot and Cercospora leaf blight were monitored on a biweekly basis and correlated to the UAS imagery. Preliminary results indicated plant stress can be detected using UAS imagery. In Experiment 1, stress associated with charcoal rot was visible in the NDRE imagery. This was of interest because at the time of flight, while it was noted that plants were yellowing, the root and stem disease itself had not been identified by direct observation. In Experiment 2, soybean rust was observed by direct observation and in both the NDRE and NDVI imagery. Soybean rust did have a negative impact on yield in Experiment 2, however severe drought conditions may have negated the yield loss likely caused by the development of charcoal rot in Experiment 1.

Keywords

unmanned aircraft systemssoybean rustmulti-spectral sensor
Type
UAV applications
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 802 - 806
Copyright
© The Animal Consortium 2017 

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