Development of Water-Wheel Tail Measurement System Based on Image Projective Transformation

Fishery is vital for Taiwan’s economy, and over 40% of the fishery products come from aquaculture. Traditional aquaculture relies on the visual observation of a water-wheel tail length to assess water quality. However, the aging population, lack of young labor, and difficulty in passing down experie...

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Bibliographic Details
Published in:Water (Basel) 2023-11, Vol.15 (22), p.3889
Main Authors: Chen, Xin-Ting, Liu, Chien-Sheng, Yen, Jung-Hong
Format: Article
Language:English
Subjects:
Accuracy
Aquaculture
Aquaculture industry
Automation
Calibration
Cameras
Control systems
Decision making
Deep learning
Farms
Fish industry
Fisheries
Machine learning
Mariculture
Measuring instruments
Neural networks
Quality control
Sensors
Water quality
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Summary:Fishery is vital for Taiwan’s economy, and over 40% of the fishery products come from aquaculture. Traditional aquaculture relies on the visual observation of a water-wheel tail length to assess water quality. However, the aging population, lack of young labor, and difficulty in passing down experience pose challenges. There is currently no systematic method to determine the correlation between the water quality and water-wheel tail length, and adjustments are made based on visual inspection, relying heavily on experience without substantial data for transmission. To address the challenge, a precise and efficient water quality control system is proposed. This study proposes a water-wheel tail length measurement system that corrects input images through image projective transformation to obtain the transformed coordinates. By utilizing known conditions of the water-wheel, such as the length of the base, the actual water-wheel tail length is deduced based on proportional relationships. Validated with two different calibration boards, the projective transformation performance of specification A is found to be better, with an average error percentage of less than 0.25%. Data augmentation techniques are employed to increase the quantity and diversity of the dataset, and the YOLO v8 deep learning model is trained to recognize water-wheel tail features. The model achieves a maximum mAP50 value of 0.99013 and a maximum mAP50-95 value of 0.885. The experimental results show that the proposed water-wheel tail length measurement system can be used feasibly to measure water-wheel tail length in fish farms.
ISSN:2073-4441
2073-4441
DOI:10.3390/w15223889