Modeling and Calibration of a Multi-Spectral Imaging Sensor for In-Field Crop Nitrogen Assessment

Abstract

Assessment of nitrogen content from crop leaves has been of interest worldwide to help growers adjust N fertilizer rates to meet the demands of the crop. A multi-spectral imaging system was developed for in-field real-time assessment of plant nitrogen stress in corn (Zea mays L.) crops indicated by plant reflectance and measured using a vision-based multi-spectral imaging sensor (MSIS). The objectives of this article were to formulate a mathematical model of reflectance and calibrate the MSIS system. A MSIS response model was formulated based on solar energy transformation. The MSIS was calibrated with a known reflectance target to determine the relationship between the MSIS reflectance response and that of a known target reflectance by setting the MSIS, a sensor for ambient illumination (AI), and an absolute reflectance panel normal to the solar position. Sensor calibration proved the validity of the reflectance model such that dynamic adjustment of the camera parameters according to gain change maintained the linearity of log response of the MSIS. Calibration constants were determined and validated with reflectance responses of four MSIS units paired with AI sensors. Consistent performance was achieved across the units. The difference found in some units was mainly caused by erroneous AI calibration values. Corrected AI sensors improved the MSIS responses by producing nearly identical responses over all units except one unit with image formation problems.