Real-time protein monitoring - Australian experiences

Paper

Jensen, T., Zeller, L., Fox, G., Town, W. and Kelly, R.. 2005. "Real-time protein monitoring - Australian experiences." Stafford, John V. (ed.) 5th European Conference on Precision Agriculture. Uppsala, Sweden 09 - 12 Jun 2005 Wageningen, The Netherlands.
Paper/Presentation Title

Real-time protein monitoring - Australian experiences

Presentation TypePaper
AuthorsJensen, T. (Author), Zeller, L. (Author), Fox, G. (Author), Town, W. (Author) and Kelly, R. (Author)
EditorsStafford, John V.
Journal or Proceedings TitlePrecision Agriculture '05
Number of Pages5
Year2005
Place of PublicationWageningen, The Netherlands
ISBN9789076998695
Conference/Event5th European Conference on Precision Agriculture
Event Details
5th European Conference on Precision Agriculture
Event Date
09 to end of 12 Jun 2005
Event Location
Uppsala, Sweden
Abstract

A 440 ha paddock of Sorghum (Sorghum bicolor cv Buster and cv MR43) was harvested at 'Well Park' in Queensland, Australia (27.105 degrees S, 151.345 degrees E), in May-June 2004. Prior to harvest, a prototype Cropscan 2000H 'on-the-go' near-infrared (NIR) protein sensor designed by NIR Technology Australia (http://www.nirtech.zip.com.au/), was fitted to the bubble-up auger of the contract harvester's New Holland TR66 combine. More than 5000 readings were taken within a 282 ha sub-sample area of the paddock (approximately every 30-40m). Additionally, 120 grab samples were collected from the bubble-up auger and the position of the sample 'tagged' in the yield monitor file. These samples were analysed using laboratory NIR equipment to compare with the 'on-header' unit.
Unfortunately, the samples were outside the calibration set of the instrument and the results were in error. To rectify this situation, 10 of the samples were processed for protein using wet chemistry and added to the calibration set. The 120 samples were repredicted and compared to the protein sensor values. The correlation between the 'grab samples' protein values and values from the protein sensor for the same location in the field was low (R2=0.32). This may have been due to a low population in the calibration set or an offset in the protein sensor. This is being investigated

Keywordsprotein; on-header; monitor
ANZSRC Field of Research 2020300407. Crop and pasture nutrition
300403. Agronomy
401399. Geomatic engineering not elsewhere classified
Public Notes

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Byline AffiliationsDepartment of Primary Industries, Queensland
Department of Primary Industries and Fisheries, Queensland
Landmark, Australia
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Jensen, Troy, Apan, Armando and Zeller, Leslie. 2009. "The use of a UAV as a remote sensing platform in agriculture." Banhazi, T. M. and Saunders, C. (ed.) SEAg 2009: Agricultural Technologies In a Changing Climate. Brisbane, Australia 13 - 16 Sep 2009 Brisbane, Australia.
Detecting the attributes of a wheat crop using digital 3D imagery acquired from a low-altitude platform
Jensen, T., Apan, A., Young, F. and Zeller, L.. 2007. "Detecting the attributes of a wheat crop using digital 3D imagery acquired from a low-altitude platform." Computers and Electronics in Agriculture. 59 (1-2), pp. 66-77. https://doi.org/10.1016/j.compag.2007.05.004
Assessing grain crop attributes using digital imagery acquired from a low-altitude remote controlled aircraft
Jensen, Troy, Apan, Armando, Young, Frank R., Zeller, Leslie Charles and Cleminson, Ken. 2003. "Assessing grain crop attributes using digital imagery acquired from a low-altitude remote controlled aircraft." 2003 Spatial Sciences Institute Biennial Conference: Spatial Knowledge Without Boundaries (SSC2003). Canberra, Australia 22 - 27 Sep 2003 Canberra, Australia.
Predicting grain protein content in wheat using hyperspectral sensing of in-season crop canopies and partial least squares regression
Apan, Armando, Kelly, Rob, Phinn, Stuart, Strong, Wayne, Lester, David, Butler, David and Robson, Andrew. 2006. "Predicting grain protein content in wheat using hyperspectral sensing of in-season crop canopies and partial least squares regression." International Journal of Geoinformatics. 2 (1), pp. 93-108.
Detection of pests and diseases in vegetable crops using hyperspectral sensing: a comparison of reflectance data for different sets of symptoms
Apan, Armando, Datt, B. and Kelly, Rob. 2005. "Detection of pests and diseases in vegetable crops using hyperspectral sensing: a comparison of reflectance data for different sets of symptoms." Bellman, Chris, Cartwright, William and Shortis, Mark (ed.) 2005 Spatial Sciences Institute Biennial Conference: Spatial Intelligence, Innovation and Praxis (SSC2005). Melbourne, Australia 12 - 16 Sep 2005 Melbourne, Australia.