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Proceedings Paper

UAV remote sensing for phenotyping drought tolerance in peanuts
Author(s): Maria Balota; Joseph Oakes
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Paper Abstract

Farmers can benefit from growing drought tolerant peanut (Arachis hypogaea L.) cultivars with improved yield when rainfall is sporadic. In the Virginia-Carolina (VC) region, drought is magnified by hot summers and usually occurs in July and Aug when pod and seed growth are intense. At these growth stages, weekly supply of 50 to 75 mm of water is needed to ensure profitability. Irrigation can supplement crop water needs, but only 10% of the peanut farms are irrigated.

In this frame, drought tolerant varieties can be profitable, but breeding for cultivars with improved drought tolerance requires fast yet accurate phenotyping. Our objective was to evaluate the potential of UAV remote sensing technologies for drought tolerance selection in peanut. In this study, we examined the effect of drought on leaf wilting, pod yield, grading characteristics, and crop value of 23 peanut cultivars (Virginia, Runner, and Valencia type). These varieties were arranged in a factorial design, with four replications drought stressed and two replications well-watered. Drought was imposed by covering the drought stressed plots with rainout shelters on July 19; they remained covered until August 29 and only received 38 mm irrigation in mid Aug. The well-watered plots continued to receive rain and supplemental irrigation as needed. During this time, Canopy Temperature Depression (CT) and Normalized Differential Vegetative Index (NDVI) were collected from the ground on all plots at weekly intervals. After the shelters were removed, these measurements were collected daily for approximately 2 weeks. At the same time, Red-Green-Blue (RGB), near-infrared (NIR), and infrared (IR) images taken from an UAV platform were also collected. Vegetation indices derived from the ground and aerial data were compared with leaf wilting, pod yield and crop value. Wilting, which is a common water stress symptom, was best estimated by NDVI and RGB, and least by CT; but CT was best in estimating yield, SMK and crop value in particular when taken on the ground at 15 days water stress imposition. Interestingly, CT predicted well plant wilting even before it occurred, i.e., correlation coefficients were negative and over 0.750 when CT was measured on July 19 and 20 even though wilting was visible only after two weeks. The data, yet preliminary, show promising potential for remote sensing technologies, at the ground and aerial, for peanut variety selection for improved drought tolerance.

Paper Details

Date Published: 16 May 2017
PDF: 7 pages
Proc. SPIE 10218, Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II, 102180C (16 May 2017); doi: 10.1117/12.2262496
Show Author Affiliations
Maria Balota, Virginia Tech Tidewater AREC (United States)
Joseph Oakes, Virginia Tech Tidewater AREC (United States)


Published in SPIE Proceedings Vol. 10218:
Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II
J. Alex Thomasson; Mac McKee; Robert J. Moorhead, Editor(s)

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