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United States Department of Agriculture

Agricultural Research Service


item Thomson, Steven
item Smith, Lowrey
item Ray, Jeffery - Jeff
item Zimba, Paul

Submitted to: Proceedings of the International Symposium on Optical Science and Technology
Publication Type: Proceedings
Publication Acceptance Date: 7/30/2003
Publication Date: 8/15/2003
Citation: Thomson, S.J., Smith, L.A., Ray, J.D., Zimba, P.V. 2003. Remote sensing and implications for variable-rate application using agricultural aircraft.Proceedings of the International Symposium on Optical Science and Technology. V5153:13-20.

Interpretive Summary: Precision farming allows site-specific application of pesticide and other field inputs. Only field areas requiring treatment receive application, saving on input costs and pollutant load to the environment. Crops under nutrient stress or weed clusters, for example, can be remotely imaged from general aviation aircraft or satellites so that field areas requiring treatment can be isolated. Either ground equipment or agricultural aircraft can then be used to accomplish prescription pesticide or fertilizer application. This study illustrates two examples of remote sensing and outlines plans for a variable-rate pesticide application system for agricultural aircraft. A study was begun to determine if nitrogen stress occurs in early soybeans. Another experiment was conducted to image field plots with known weed populations. For the soybean study, it was hypothesized that nitrogen availability may be limiting productivity of soybeans, especially in the early vegetative period. Images of soybeans were obtained using digital video from agricultural aircraft to test procedures for image acquisition, image mosaicking, and image analysis. Statistics were also run on digital numbers obtained from images to determine if nitrogen stress could be quantified. Nitrogen status could not be statistically quantified in this preliminary work, but cultivar differences were statistically discernable. Use of a multiple-band imaging system and careful selection of optical filters should improve nutrient stress detection capabilities assuming laboratory plant analysis determines that nutrient stress is occurring. For the weed study, a field was flown at low altitude, and resulting images were analyzed using image-processing software. Patchy weeds were successfully discriminated from early cotton and grass weeds in processed images. A system for agricultural aircraft that integrates remote sensing and variable-rate application of pesticide has been conceptualized and is under development.

Technical Abstract: Aircraft routinely used for agricultural spray application are finding utility for remote sensing. Data obtained from remote sensing can be used for prescription application of pesticides, fertilizers, cotton growth regulators, and water (the latter with the assistance of hyperspectral indices and thermal imaging). Digital video was used to detect weeds in early cotton, and preliminary data were obtained to see if nitrogen status could be detected in early soybeans. Weeds were differentiable from early cotton at very low altitudes (65-m), with the aid of supervised classification algorithms in the ENVI image analysis software. The camera was flown at very low altitude for acceptable pixel resolution. Nitrogen status was not detectable by statistical analysis of digital numbers (DNs) obtained from images, but soybean cultivar differences were statistically discernable (F=26, p=0.01). Spectroradiometer data are being analyzed to identify narrow spectral bands that might aid in selecting camera filters for determination of plant nitrogen status. Multiple camera configurations are proposed to allow vegetative indices to be developed more readily. Both remotely sensed field images and ground data are to be used for decision-making in a proposed variable-rate application system for agricultural aircraft. For this system, prescriptions generated from digital imagery and data will be coupled with GPS-based swath guidance and programmable flow control.

Last Modified: 06/24/2017
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