Applied machine vision in agriculture at the NCEA

McCarthy, Cheryl and Billingsley, John ORCID: https://orcid.org/0000-0002-7198-5594 (2009) Applied machine vision in agriculture at the NCEA. In: SEAg 2009: Agricultural Technologies in a Changing Climate, 13-16 Sep 2009, Brisbane, Australia.

Preview

PDF (Author Version) McCarthy_Billingsley_SV.pdf Download (278kB)

Abstract

Machine vision involves the use of cameras and other imaging technologies to automatically extract information from a two-dimensional representation of a real-world scene. The technology has the potential to reduce resource usage and increase productivity in agricultural applications by performing repetitive and labour-intensive tasks that are conventionally carried out manually by humans.
The National Centre for Engineering in Agriculture (NCEA) is conducting machine vision research projects that aim to improve productivity for a range of agricultural processes in a changing economic climate. These projects include:
• Macadamia yield monitor: Automated yield assessment of individual trees in macadamia plantations is expected to reduce labour costs of varietal trials by 59%. The NCEA has developed and evaluated vision-based automated yield assessment systems featuring counting of macadamia nuts in a pinwheel harvester.
• Grading of fodder quality: A field prototype for automatic grading of hay samples has been developed that aims to save labour and enable development of repeatable scoring standards that may be implemented throughout the fodder industry.
• Body condition scoring of cattle: Cattle condition sensing developed by the NCEA has potential use in automatic drafting and informing management decisions on cattle properties.

An overview of the NCEA’s machine vision research activities and their implications will be presented.

Statistics for this ePrint Item

Item Type:	Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed:	No
Item Status:	Live Archive
Additional Information:	Authors retain copyright.
Faculty/School / Institute/Centre:	Historic - Faculty of Engineering and Surveying - No Department (Up to 30 Jun 2013)
Faculty/School / Institute/Centre:	Historic - Faculty of Engineering and Surveying - No Department (Up to 30 Jun 2013)
Date Deposited:	14 Mar 2011 02:51
Last Modified:	24 Sep 2013 02:48
Uncontrolled Keywords:	automation; image processing; yield monitor; quality assessment; macadamia; fodder; body condition scoring
Fields of Research (2008):	07 Agricultural and Veterinary Sciences > 0701 Agriculture, Land and Farm Management > 070104 Agricultural Spatial Analysis and Modelling 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified 09 Engineering > 0999 Other Engineering > 099901 Agricultural Engineering
Fields of Research (2020):	30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3002 Agriculture, land and farm management > 300206 Agricultural spatial analysis and modelling 46 INFORMATION AND COMPUTING SCIENCES > 4699 Other information and computing sciences > 469999 Other information and computing sciences not elsewhere classified 40 ENGINEERING > 4099 Other engineering > 409901 Agricultural engineering
Socio-Economic Objectives (2008):	E Expanding Knowledge > 97 Expanding Knowledge > 970109 Expanding Knowledge in Engineering
URI:	http://eprints.usq.edu.au/id/eprint/7964

Actions (login required)

Archive Repository Staff Only