Submitted to: Encyclopedia of Agricultural, Food, and Biological Engineering
Publication Type: Peer reviewed journal
Publication Acceptance Date: 7/29/2004
Publication Date: 9/1/2004
Citation: Kim, Y., Reid, J.F. 2004. Multispectral Imaging Technology for Site-Specific Crop Fertilization. Encyclopedia of Agricultural, Food, and Biological Engineering. 10.1081/E-EAFE-120026899:1-5. Interpretive Summary: Nitrogen (N) management is critical for corn production. On the other hand, N leaching into the groundwater creates serious environmental problems. There is a demand for sensors that can access the plant N deficiency throughout the growing season to allow producers to reach their production goals, while maintaining environmental quality. The paper reports on the performance of a vision-based reflectance sensor for real-time assessment of N stress level of corn crops. Data were collected representing the changes in crop reflectance in various spectral ranges over several stages of development in the growing season. The performance of this non-contact sensor was validated under various filed conditions with reference measurements from a Minolta SPAD meter and stepped nitrogen treatments. Preliminary study of the sensor-based supplemental N treatment promised economical and environmental benefits in crop N management. The multi-spectral imaging technology is a promising tool that can assess plant N deficiency and can enable producers for sensor-based site-specific fertilization.
Technical Abstract: Agricultural fields are variable and require site-specific crop management. Nitrogen (N) is an essential nutrient required for plant growth and is a major component of the chlorophyll molecule enhancing photosynthesis. However, excessive N fertilizer leaches into groundwater and creates serious environmental problems. A sensor-based fertilizer system can provide a possible solution by assessing plant N stress and applies only the required amount of fertilizer. A ground-based remote sensor was used to estimate plant stresses at a high resolution over varying daylight conditions. The change in image intensity because of varying AI is controlled automatically by adjusting the camera parameters to maintain uniform image brightness. In an experimental study of corn field, the sensor provided reliable performance at 2 m/sec vehicle speed, which is considered superior to SPAD chlorophyll measurements.