Submitted to: Applied Engineering in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/10/2004
Publication Date: 6/7/2005
Citation: Thomson, S.J., Zimba, P.V., Bryson, C.T., Alarcon, V.J. 2005. Potential for remote sensing from agricultural aircraft using digital video. Applied Engineering in Agriculture. 21(3):531-537. Interpretive Summary: Aircraft routinely used for agricultural spray application are being investigated for collection of remote plant stress variables, detection of significant weed populations, and detection of algae in catfish ponds. A study was conducted to evaluate a digital video-based remote sensing system for weed detection in crop fields and algae detection in catfish ponds. Where weeds are detected in a field, a map could later be created from the images so only those areas requiring herbicide are sprayed, a potential savings in labor, cost, and reduction in pollutant loading. Effusive growth of algae in catfish ponds has shown detrimental effects on catfish populations. Toxin-producing algae were detected using the digital video-based imaging system and data were highly correlated with pond sampling data. Potential advantages of agricultural aircraft-based remote sensing are that finer delineation of features is possible than from conventional aircraft because a spray plane can be flown at very low altitudes and flights can be easily scheduled.
Technical Abstract: An imaging system for remote sensing was developed for agricultural aircraft. The system uses a digital video camera, GPS, and a video mapping system (VMS) as the GPS interface to video. Remote control and monitoring was implemented to allow the pilot to image only field areas of interest, facilitating image acquisition and post-processing. Example applications include detection of weeds in early cotton and detection of chlorophyll a in catfish ponds, a constituent of algae. For the weed detection study, spotted spurge (E. maculata L.) and hyssop spurge (Euphorbia hyssopifolia L.) were distinguishable from both early cotton (Gossypium hirsutum L.) and johnsongrass (Sorghum halepense (L.) Pers.) using supervised classification algorithms in image analysis software. For the pond study, significant correlation was seen between digital numbers derived from analysis of images and laboratory determination of chlorophyll a from water samples obtained at the catfish ponds.