Cotton root rot (CRR) is a persistent soil-borne fungal disease that is devastating to cotton crops in certain fields, predominantly in Texas. Research has shown that CRR can be prevented or mitigated by applying fungicide during planting, but fungicide application is expensive. The potentially infected area within a field has been shown to be consistent, so it is possible to apply the fungicide only at locations where CRR exists, thus minimizing the amount of fungicide applied across the field. Previous studies have shown that remote sensing from manned aircraft is an effective means of delineating CRR-infected field areas. In 2015, an unmanned aerial vehicle was used to collect high-resolution remote-sensing images in a field known to be infected with CRR. A method was developed to produce a prescription map (PM) from these data, and in 2017, fungicide was applied based on a PM derived from the 2015 image data. The results showed that the PM reduced the fungicide applied by 88.3%, with a reduction in CRR area of 90% compared to 2015. A simple economic model suggested that it is generally better to treat an entire CRR-infested field rather than leaving it untreated, and application based on a PM becomes preferable as the size of the farm and the yield increase while the CRR-infestation level and the number of fields on the farm decrease.
Commercial off-the shelf systems of UAVs and sensors are touted as being able to collect remote-sensing data on crops that include spectral reflectance and plant height. Historically a great deal of effort has gone into quantifying and reducing the error levels in the geometry of UAV-based orthomosaics, but little effort has gone into quantifying and reducing the error of the reflectance and plant-height. We have been developing systems and protocols involving multifunctional ground-control points (GCPs) in order to produce crop phenotypic data that are as repeatable as possible. These multifunctional GCPs aid not only geometric correction, but also image calibration of reflectance and plantheight. The GCPs have known spectral-reflectance characteristics that are used to enable reference-based digital numberto-reflectance calibration of multispectral images. They also have known platform heights that are used to enable reference-based digital surface model-to-height maps. Results show that using these GCPs for reflectance and plantheight calibrations significantly reduces the error levels in reflectance (ca. 50% reduction) and plant-height (ca. 20% reduction) measurements.
The use of UAV (unmanned aerial vehicle) based imaging in agriculture adds the ability to incorporate vast amounts of data into analyses designed to improve efficiency in the use of agricultural inputs. One reason this ability has not yet been realized is that producing UAV based radiometrically calibrated images for the purpose of ensuring data reliability is difficult at the large scale. This paper presents an investigation of field-based image-mosaic calibration procedures using a commercial off-the-shelf fixed-wing small UAV and a five-band multispectral sensor. To determine the quality of the radiometric calibration procedure for UAV image mosaics, images were also collected with an identical camera on a manned aircraft, and ground based radiometric calibration tarps were used to produce high-quality calibrated field images. Satellite images were also collected on the same day as the aircraft images in a two-hour flight window centered on solar noon. The manned aircraft and satellite images were large enough for a single image to cover the entire field. The multispectral camera used enables two kinds of exposure settings; auto exposure allows the camera to automatically select exposure and gain settings for each image in a flight, and manual exposure allows the user to select settings preflight which are used for all the images in that flight. In this work we compare the radiometrically calibrated UAV images, collected with both auto-exposure and manual-exposure methods, to the radiometrically calibrated single-frame image generated with the manned aircraft, as well as to a satellite image.
Field-based high-throughput phenotyping is a bottleneck to future breeding advances. The use of remote sensing with
unmanned aerial vehicles (UAVs) can change the way agricultural research operates by increasing the spatiotemporal
resolution of data collection to monitor status of plant growth. A fixed-wing UAV (Tuffwing) was operated to collect
images of a sorghum breeding research field with 70% overlap at an altitude of 120 m. The study site was located at Texas
A and M AgriLife Research’s Brazos Bottom research farm near College Station, Texas, USA. Relatively high-resolution
(>2.7cm/pixel) images were collected from May to July 2017 over 880 sorghum plots (including six treatments with four
replications). The collected images were mosaicked and structure from motion (SfM) calculated, which involves
construction of a digital surface model (DSM) by interpolation of 3D point clouds. Maximum plant height for each
genotype (plot) was estimated from the DSM and height calibration implemented with aerial measured values of groundcontrol
points with known height. Correlations and RMSE values between actual height and estimated height were
observed over sorghum across all genotypes and flight dates. Results indicate that the proposed height calibration method
has a potential for future application to improve accuracy in plant height estimations from UAVs.
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