Submitted to: American Society of Agronomy Special Publication
Publication Type: Book / Chapter
Publication Acceptance Date: 9/15/2002
Publication Date: 11/1/2003
Citation: Shanahan, J.F., K. Holland, J.S. Schepers, D.D. Francis, M.R. Schlemmer, and R. Caldwell. 2003. Use of crop reflectance sensors to assess corn leaf chlorophyll content. In: VanToai, T., Major, D., McDonald, M., Schepers, J., and Tarpley L., editors. Digital Imaging and Spectral Techniques: Applications to Precision Agriculture and Crop Physiology. ASA Special Pub. 66. ASA, Madison, WI. p. 129-144. Interpretive Summary: Over-application of nitrogen (N) fertilizer on corn has resulted in elevated levels of N in ground and surface waters. Our goal is to reduce these over-applications by using remote sensing to direct fertilizer only to areas needing N at times when the crop can efficiently utilize the N. We have finished an initial cycle of testing of an on-the-go remote sensor system, which when interfaced with a rate controlling mechanism and mounted on a high-clearance tractor is intended to operate as an in-season N applicator. The remote sensor measures light reflected from the crop in four wavebands in the blue, green, red and the near infrared (NIR) regions of the electromagnetic spectrum. These four wavebands have been previously shown to be the most useful and/or sensitive in detecting N deficient plants with diminished chlorophyll levels. The specific goal of this work was to compare the performance of canopy reflectance measured with the remote sensor system to variations in leaf greenness or chlorophyll as determined with a handheld chlorophyll meter. This was accomplished using replicated small plots planted with two corn hybrids receiving five N rates (0, 50, 100, 150, and 200 lbs of N) grown under irrigation near Shelton, NE in 2000 and 2001 seasons. Sensor reflectance data and hand-held chlorophyll meter data were acquired on several dates from the experimental plots during both seasons. The four-color bands from the sensor were used to compute two vegetation indices, which provide an indication of the level of canopy greenness. The first index is the green normalized vegetation index (GNDVI), involving green and NIR bands and second index is the normalized difference vegetation index (NDVI), utilizing the red and NIR bands. Nitrogen application increased chlorophyll meter values by 33%, GNDVI by 15%, and NDVI by 5%. GNDVI was more highly correlated with chlorophyll meter values than NDVI, with maximum correlations of 0.90 and 0.88 in 2000 and 2001, respectively. Our results suggest that the remote sensor system can be used to identify N deficient plants. Given the option of using high-clearance applicators configured with the remote sensor system, the potential for reducing pre-season N applications and emphasizing in-seasons variable N applications exists.
Technical Abstract: A major factor contributing to low nitrogen use efficiency (NUE) and environmental contamination by traditional corn N management schemes is routine pre-season application of large doses of N, before the crop can effectively utilize this N. Previous research with chlorophyll meters has shown that NUE can be improved by moving away from pre-season application and towards in-season applications of N, in amounts that better coincide with crop use. We have finished an initial cycle of testing on a multi-spectral four band (blue, green, red and NIR) crop reflectance sensor system, which when mounted on a high-clearance tractor is intended to operate as an in-season N applicator. The specific goal of this work was to compare the performance of canopy reflectance measured with the sensor system to variations in leaf chlorophyll concentration determined with a chlorophyll meter. Treatments consisting of two hybrids and five N rates were grown under irrigation near Shelton, NE in 2000 and 2001. Sensor bands (green, red, and NIR) and chlorophyll meter data were acquired on several dates during both seasons. Reflectance bands were used to compute two vegetation indices, GNDVI and NDVI. Hybrid treatments produced little effect on chlorophyll meter values or vegetation indices, while N application increased chlorophyll meter values by 33%, GNDVI by 15%, and NDVI by 5%. GNDVI was more highly correlated with chlorophyll meter values than NDVI, with maximum correlations of 0.90 and 0.88 in 2000 and 2001, respectively. Our results suggest that the sensor system is capable of detecting variations in corn leaf chlorophyll content, and could potentially be used in controlling an in-season N applicator.