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.

了解对虾的生长和分布对于优化饲料和收获策略至关重要。对虾生长的不充分了解会导致经济收益减少，例如，作物收获过早。保持对虾生长的良好了解的关键是经常取样。然而，最常用的采样方法，即撒网法，由于昂贵且费力，无法对虾进行高频采样。另一种方法是从养殖场工人每天检查的饲料盘中对虾进行取样。这将允许以高频率（每天）收集生长数据。但每天手动测量虾是一项费力的工作。在这篇文章中，我们提出了一种利用智能眼镜、深度相机、计算机视觉和机器学习来检测饲料托盘中虾的分布和生长。开发了一款智能耳机，让农民可以在执行日常饲料托盘检查时收集对虾数据。开发并演示了计算机视觉 + 机器学习管道，以检测 4 个虾池中虾在一个生长季节的生长趋势。