Lin, Hong YangHong YangLinLee, Hsin-ChenHsin-ChenLeeNg, Woei-LingWoei-LingNgPai, Jyh NainJyh NainPaiYUAN-NAN CHULiou, Chyng-HwaChyng-HwaLiouKUO-CHI LIAOYAN-FU KUO2019-11-042019-11-042019-01-01https://scholars.lib.ntu.edu.tw/handle/123456789/430864The body length of shrimp is the major indicator for feeding management in shrimp aquaculture. Food intake for shrimp is linearly correlated with its body length. Conventionally, the body length of shrimp was measured using naked-eye inspection and relied on the experience of shrimp farmers. However, manual observation might be subjective. Imprecise measurement can lead to mistaken feeding strategies and, hence, economic losses in shrimp aquaculture. This study proposed an automatic method to measure the body length of shrimp in-vivo using underwater camera and deep learning. In the approach, underwater cameras with infrared light source were designed and established to observe shrimp activities. A convolutional neural network model was developed to locate shrimps in the images. Next, image processing algorithms were applied to segment the shrimps from the background and estimate the length of the shrimp. The results achieved a mAP of 85.08% in shrimp detection and localization, and an RMSE of 5.76% in shrimp body length estimation.Convolutional neural network | Deep learning | Image processing | Shrimp body length | Underwater camera[SDGs]SDG14conference paper10.13031/aim.2019007242-s2.0-85072918304https://api.elsevier.com/content/abstract/scopus_id/85072918304