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ARS Home » Midwest Area » Columbia, Missouri » Cropping Systems and Water Quality Research » Research » Publications at this Location » Publication #329467
Title: Development of a multispectral sensor for crop canopy temperature measurement

Author
item DREW, PHILLIP - University Of Missouri
item Sudduth, Kenneth - Ken
item Sadler, Edward

Submitted to: International Conference on Precision Agriculture Abstracts & Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 5/23/2016
Publication Date: 7/31/2016
Citation: Drew, P., Sudduth, K.A., Sadler, E.J. 2016. Development of a multispectral sensor for crop canopy temperature measurement. In: Proceedings of the 13th International Conference on Precision Agriculture, July 31-August 3, 2016, St. Louis, Missouri. Available: https://ispag.org/proceedings/?action=abstract&id=1921&search=types.

Interpretive Summary: Quantifying spatial and temporal variations in plant stress is important for several precision agriculture applications, including variable rate irrigation and variable rate nutrient application. A common approach to plant stress detection is crop canopy temperature measurement. Canopy temperature measurement can be accomplished using relatively inexpensive infrared thermometers (IRT), but for best results care must be taken that the IRT sees only a representative, sunlit canopy region. Infrared cameras are another option, allowing discrimination of crop from non-crop pixels, but they are generally expensive. Our goal in this research was to develop an infrared imaging system that would allow pixel discrimination at a cost level similar to an IRT. This was done by combining an inexpensive, low-resolution infrared camera with a standard color camera and specialized interface hardware and software. In initial tests the system worked well to discriminate temperature differences in scenes. Ongoing research will calibrate system output to measured temperature, deploy multiple sensors in a field, and evaluate their performance.

Technical Abstract: Quantifying spatial and temporal variability in plant stress has precision agriculture applications in controlling variable rate irrigation and variable rate nutrient application. One approach to plant stress detection is crop canopy temperature measurement by the use of thermographic or radiometric methods, generally in the long wave infrared (LWIR) wavelength range. A confounding factor in LWIR canopy temperature estimation is eliminating the effect of the soil background in the image. One approach to this is time series capture of canopy temperature using single point radiometric sensors, coupled with algorithms to estimate the influence of soil on the measurement. Another approach is imaging a crop canopy using a LWIR imager paired with a visible spectrum camera covering an overlapping field, whereby canopy temperature measurements can be compiled from infrared pixels while eliminating non-crop components from the field of view using reflectance data from the visible image. In this research we developed such a multi-sensor system utilizing a miniaturized LWIR camera paired with a RGB imager. This instrument was designed to have a low enough cost to be able to deploy multiple sensors throughout a field. It is capable of automatic data logging, creating multiple data points throughout a field for the purpose of identifying variability.