  Understanding the growth and distribution of the prawns is critical for
optimising the feed and harvest strategies. An inadequate understanding of
prawn growth can lead to reduced financial gain, for example, crops are
harvested too early. The key to maintaining a good understanding of prawn
growth is frequent sampling. However, the most commonly adopted sampling
practice, the cast net approach, is unable to sample the prawns at a high
frequency as it is expensive and laborious. An alternative approach is to
sample prawns from feed trays that farm workers inspect each day. This will
allow growth data collection at a high frequency (each day). But measuring
prawns manually each day is a laborious task. In this article, we propose a new
approach that utilises smart glasses, depth camera, computer vision and machine
learning to detect prawn distribution and growth from feed trays. A smart
headset was built to allow farmers to collect prawn data while performing daily
feed tray checks. A computer vision + machine learning pipeline was developed
and demonstrated to detect the growth trends of prawns in 4 prawn ponds over a
growing season.
