pp. 2061-2080
S&M4042 Research Paper of Special Issue https://doi.org/10.18494/SAM5564 Published: May 30, 2025 Automated AI Approach for Noninvasive Shrimp Length and Weight Estimation Using Underwater Imaging and Feeding Induction [PDF] Shao-Yong Lu, Yu-Sheng Tu, and Wen-Ping Chen (Received January 27, 2025; Accepted April 25, 2025) Keywords: image processing, object detection, convolutional neural network, shrimp body length
We aim to develop an objective method for measuring shrimp length in traditional white shrimp farming using images from an automated underwater camera system, reducing reliance on subjective assessment. Traditional manual methods often suffer from subjectivity and inaccuracies, leading to inefficient feed management strategies. Computer vision techniques were used for object detection and image preprocessing. A deep learning network was used to classify completeness and assess the length and weight of shrimps. We also analyzed the correlation between the body length and weight of shrimps. The dataset consists of 8,401 images categorized as measurable (3112) and visible (5289). An accuracy of 95.0% was obtained with an average error rate of 8.4%, highlighting the effectiveness of the proposed method. The weight estimation exhibited an average error rate of 22%. This system can optimize feed management and enhance aquaculture sustainability, reducing both resource waste and operational costs. Furthermore, we utilized underwater cameras as sensing devices, combined with specially designed feeding platforms and observation materials, to enable real-time image-based monitoring and noninvasive shrimp measurement.
Corresponding author: Shao-Yong Lu![]() ![]() This work is licensed under a Creative Commons Attribution 4.0 International License. Cite this article Shao-Yong Lu, Yu-Sheng Tu, and Wen-Ping Chen, Automated AI Approach for Noninvasive Shrimp Length and Weight Estimation Using Underwater Imaging and Feeding Induction, Sens. Mater., Vol. 37, No. 5, 2025, p. 2061-2080. |