Automatic measuring shrimp body length using CNN and an underwater imaging system

Abstract

Farming of the Pacific white shrimp (Penaeus vannamei Boone, 1931) is among the largest aquaculture industries worldwide. Pond culture is an efficient and frequently used method in shrimp cultivation. Shrimp body length is a key indicator for feeding management. Conventionally, the shrimp body length is determined by observing the shrimp on feeding trays; however, this conventional approach is a time-consuming and labour-intensive process, and relies on the experience of shrimp farmers. This study proposes an automated approach for detecting shrimps and measuring their body lengths at the bottom of aquaculture ponds using the images captured from an underwater video system. The system included a camera equipped with an infrared LED illuminator. The shrimp in the images were classified into two categories, measurable and visible. A convolutional neural network model, YOLOv4-tiny, was trained to detect shrimps in the images. Subsequently, image processing algorithms were applied to segment the detected shrimps from their background and to estimate the shrimp body lengths. The trained YOLOv4-tiny model achieved an average precision of 93.24% in detecting the measurable shrimps. The proposed body length estimation approach achieved a mean absolute error and a mean absolute relative error of 3.5 mm and 5.09%, respectively. The proposed approach overcomes the challenge of directly measuring shrimp body length at the bottom of aquaculture ponds without disrupting their feeding process. The proposed approach can thus benefit aquaculture operations by providing information about the underwater behaviours of shrimps.