Use of airborne hyperspectral imagery to determine quality of sorghum crops

Wells, Natasha and Kelly, Rob and Phinn, Stuart and Apan, Armando ORCID: and Jensen, Troy and Cooper, John and Strong, Wayne (2004) Use of airborne hyperspectral imagery to determine quality of sorghum crops. In: 12th Australasian Remote Sensing and Photogrammetry Conference (ARSPC 2004): To Measure is to Manage, 18-22 Oct 2004, Fremantle, Western Australia.

Text (Accepted Version)

Download (712kB)


Remote sensing has shown promise for predicting grain protein content for winter cereals, with satellite image data acquired near flowering, being significantly correlated with grain protein of wheat and barley crops. The use of commercially available satellite or airborne imagery to map grain protein content would allow growers and marketers the ability to evaluate crop performance and provide useful guidelines on harvest logistics and potential
segregation to maximise returns. Sorghum is exposed to the air during ripening and this may provide an opportunity to identify grain protein content, a key agronomic indicator of the success or otherwise of nitrogen application to the
crop. Our aim was to assess whether airborne hyperspectral imagery could be used to determine grain protein content of sorghum in the northern grains region (Darling Downs) of Australia. The first stage in this process is to determine if variations in the grain crop's protein content produce detectable variations in image data of the grain crop. The HymapTM sensor was used to acquire a 126 band, 3m pixels data set on 16 April 2004 for several sorghum fields at different growth stages. Availability of concurrent grain protein data restricted the analysis to one of these fields, which was at the end of the grainfilling stage. Grain protein was mapped within four and eight weeks of the
HymapTM image by interpolating point samples collected from a near-infrared(NIR) protein sensor mounted on a combine harvester. Preliminary analysis of the image spectral reflectance and field data revealed grain protein content in
sorghum was moderately correlated (r=-0.57) with red to near-infrared band (750nm) reflectance. Principal component bands derived from the HymapTM data were weakly correlated (r=0.43) with grain protein. Grain protein content was moderately (r<-0.5) correlated with variations in image spectral reflectance in bands falling between 730nm–1135nm. This information was then used to develop an inverse model, to predict grain protein content from HymapTM image data. A stepwise regression indicated that five bands in the red-edge and NIR regions (750-1150nm) explained the maximum variation in grain protein content (adjusted r2=0.36). The results of this study are exploratory and will be refined in future papers.

Statistics for USQ ePrint 7773
Statistics for this ePrint Item
Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Additional Information: No evidence of copyright restrictions.
Faculty/School / Institute/Centre: Historic - Faculty of Engineering and Surveying - Department of Surveying and Land Information (Up to 30 Jun 2013)
Faculty/School / Institute/Centre: Historic - Faculty of Engineering and Surveying - Department of Surveying and Land Information (Up to 30 Jun 2013)
Date Deposited: 16 May 2010 06:08
Last Modified: 17 Sep 2013 06:14
Uncontrolled Keywords: sorghum; grain growing; satellite imagery; grain protein
Fields of Research (2008): 07 Agricultural and Veterinary Sciences > 0701 Agriculture, Land and Farm Management > 070104 Agricultural Spatial Analysis and Modelling
09 Engineering > 0909 Geomatic Engineering > 090905 Photogrammetry and Remote Sensing
07 Agricultural and Veterinary Sciences > 0703 Crop and Pasture Production > 070302 Agronomy
Fields of Research (2020): 30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3002 Agriculture, land and farm management > 300206 Agricultural spatial analysis and modelling
40 ENGINEERING > 4013 Geomatic engineering > 401304 Photogrammetry and remote sensing
30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3004 Crop and pasture production > 300403 Agronomy
Socio-Economic Objectives (2008): E Expanding Knowledge > 97 Expanding Knowledge > 970107 Expanding Knowledge in the Agricultural and Veterinary Sciences

Actions (login required)

View Item Archive Repository Staff Only