Smart Headset, Computer Vision and Machine Learning for Efficient Prawn Farm Management
This addresses labor-intensive and costly sampling for prawn farmers, offering a practical but incremental improvement over manual methods.
The paper tackled the problem of inefficient prawn growth monitoring in aquaculture by developing a smart headset system using computer vision and machine learning to automate data collection from feed trays, achieving detection of growth trends across 4 ponds over a season.
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.