Applied machine vision in agriculture at the NCEA

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.