Quality assurance procedures for stationary infrared thermometers viewing crops

Authors: Colaizzi, Paul; O`Shaughnessy, Susan; Evett, Steven - Steve; Marek, Gary; Brauer, David; Copeland, Karen; Ruthardt, Brice

Submitted to: Meeting Abstract
Publication Type: Abstract Only
Publication Acceptance Date: 3/17/2023
Publication Date: 7/11/2023
Citation: Colaizzi, P.D., O'Shaughnessy, S.A., Evett, S.R., Marek, G.W., Brauer, D.K., Copeland, K.S., Ruthardt, B.B. 2023. Quality assurance procedures for stationary infrared thermometers viewing crops [abstract]. 2023 ASABE Annual International Meeting, July 7-12, 2023, Omaha, Nebraska. Paper No. 2300046.

Interpretive Summary: Infrared thermometers can measure the surface temperature of plant leaves. For irrigated crops, this is useful for scheduling irrigation timing and amounts. This is also useful for detecting crop diseases. These applications have been shown to conserve water. This is important because most agricultural regions have limited and declining water available for irrigation. The recent availability of wireless infrared thermometers has resulted in more widespread adoption by farmers. This has resulted in much greater volumes of plant temperature data. However, the large volumes of data required a new method to test for data quality. Scientists at USDA-ARS, Bushland, Texas, developed a computer model to rapidly test the quality of large volumes of plant temperature data. Ensuring data quality will improve the effectiveness of irrigation management. This can result in increased water conservation without compromising crop yield or farm income.

Technical Abstract: The directional brightness temperatures of soil and vegetated surfaces (Ts) can be sensed by infrared thermometers (IRTs), and Ts can be used in thermal-based energy balance models to detect crop water stress and calculate evaporation, transpiration, and evapotranspiration. The use of these variables for irrigation management decision support have resulted in increased crop water productivity. First developed in the 1960s, IRTs are being increasingly adopted with the recent availability of wireless data transmission and node and gateway systems. The increased adoption of IRTs has resulted in increasingly large surface temperature datasets, resulting in a need for quality assurance (QA) procedures similar to those developed for agricultural weather station data. A procedure was developed to test for seven common data conditions, including not deployed, missing, too high, too low, upward spike, downward spike, or stuck. The conditions of “too high” or “too low” used a simple energy balance procedure similar to the crop water stress index, where the theoretical lower and upper temperature limits of a surface were calculated, accounting for the vegetation view factor appearing in the IRT field-of-view. After passing the seven tests, data were assigned as plausible, and further tested as confirmed or confirmed+. The confirmed or confirmed+ tests assumed that surface temperature variation +/- 2 h of sunrise (dTs) were at the diurnal minima, where dTs < 0.5 and dTs < 0.25 degrees C were applied, respectively. Large dTs values (> ~5 degrees C) could detect ice, dirty or obscured lenses, or failing IRTs that other tests did not detect.