S
mall and midsize unmanned aerial system (aka drone) technology has generated a perfect storm in the agribusiness industry for the past several
years. To some extent, it has become a viable alternative to traditional crop scouting and farm operations monitoring. The drone industry has given an impression that everything is possible with this technology. This article is an effort to assess where we really stand when it comes to utilizing UAS imagery and its potential applications for irrigated crops monitoring and management, including specific examples from a Washington State University research team.
Platforms & imaging sensors UAS is a somewhat established technology with various platforms available in the market. Such platforms allow repeated season-long mapping of entire field blocks through waypoint (GPS points) guided flight missions. Regulations have also eased to an extent that most agricultural farm areas can be flown, while in compliance with FAA Part 107 or with required certificates of authorization.
Active (with its own source of illumination) and passive (with sunlight as the source of illumination) optical imaging sensors can be integrated with a UAS. Various commercial RGB, multispectral, hyperspectral and thermal imaging sensors are available in the market. In general, sensor weight (payload), form factor, image capture rate for adequate overlaps (for fixed-wing UAS types) and field ruggedness have been the key barriers for quality UAS image acquisitions in agriculture.
Due to advances in optics and low cost, general crop scouting, inventory mapping and two-dimensional photogrammetry-based crop canopy characterization are becoming viable with UAS-based RGB imaging. Passive multispectral (3-10 bands) imagers that measure reflectance at specific bands (up to 1,000 nanometers) are also available commercially. Hyperspectral imaging, with hundreds of spectral bands, having narrow spectral resolution (typically < 30 nanometers), allows detailed soil and crop analysis in the ranges of 380 to 2,500 nanometers.
In terms of multispectral imagery data use, certain vegetation indices can be loosely related to nutrient deficiencies and water stress. They can identify the problematic area but cannot pinpoint specific reasons for crop stress unless additional field management or ground-truth data is available. Hyperspectral sensors can provide a higher level of reliability to detect early signs of crop stress; however, their form factor, weight and cost need to be commercially viable for broader adoption by the agricultural community. For each of these sensor types, calibration to account for changing illumination scenarios due to varied cloud covers is a challenge. Some ways to partially compensate for ambient light changes include the use of 1) a reference calibration panel for data normalization, 2) an integrated incident light sensor to account for sunlight variation, and 3) spectral ratios or vegetation indices during data interpretation.
Where do we really stand when it comes to utilizing UAS imagery and its applications for irrigated crops monitoring and management?
Figure 1. The small UAS (far left) gathers multispectral and thermal imaging of subsurface deficit-irrigated Cabernet Sauvignon grapevines. The resulting normalized difference vegetation index image (left) depicts vigor variations due to treatment effects at 45 days before harvest. (Project is funded by the Washington State Department of Agriculture-Specialty Crop Block Grant; investigators: P. Jacoby, S. Sankaran and L. Khot, Washington State University.)
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