Using UAV and Multispectral Images to Assess Late-Season Physiological Responses of Peanuts to Different Planting Dates.

Several physiological measurements are used to identify and quantify plant status over the season. These methods have high accuracy, but they are time consuming, especially on a large-scale field. Recent research has shown that alternative methods, such as using remote sensing can be successfully correlated with many physiological parameters in different crops. The current study aims to identify vegetation indices calculated from UAV-based multispectral images that can be correlated to physiological data in order to identify the status of peanut plants. A field experiment was conducted in Tifton, Georgia using the cultivar Georgia-06G. Three planting dates were selected to generate differences in environmental conditions, with 12 replications per planting date. Physiological measurements associated with the photosynthetic apparatus were assessed. Chlorophyll a fluorescence transient and pigment contents were measured weekly starting when the plants had accumulated 1470 growing degree days (GDD) until 2300 GDD, before the field was dug. In addition, a 3DR solo with a Parrot Sequoia multispectral camera was flown every week on the same day physiological measurements were taken. The physiological data and UAV images were used to determine vegetation indices that could potentially reduce or replace manual measures of physiological status of peanut plants and introduce a method based on multispectral images. This would simplify and hasten assessment of plant status on large-scale fields. The results from this trial will be presented at the meeting.

Keywords: peanut physiology, UAV, multispectral images, plant health, vegetation indices.