Image Segmentation and Measurement of Trout Using a Convolutional Neural Network and Transformer Architecture
Aquaculture plays a vital role in meeting global food demands, necessitating technological innovations for sustainable production. This study investigates deep learning-based semantic image segmentation for enhanced monitoring of rainbow trout (<i>Oncorhynchus mykiss</i>) in Puno, Peru....
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2025-06-01
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| author | Jose Cruz Luis Baca Raul Castillo Eudes Apaza Christian Romero Ferdinand Pineda |
| author_facet | Jose Cruz Luis Baca Raul Castillo Eudes Apaza Christian Romero Ferdinand Pineda |
| author_sort | Jose Cruz |
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| description | Aquaculture plays a vital role in meeting global food demands, necessitating technological innovations for sustainable production. This study investigates deep learning-based semantic image segmentation for enhanced monitoring of rainbow trout (<i>Oncorhynchus mykiss</i>) in Puno, Peru. We conducted three experiments using UNET and UNETR architectures, varying image resolution, loss functions, and optimizers on a dataset of 1200 high-resolution images. Experiment 1, with UNET and 256 × 256 pixel images, achieved an IoU of 0.942854 after 20 epochs, using MSELoss and Adam, demonstrating superior segmentation accuracy. Experiment 2, utilizing UNET with 512 × 512 pixel images, resulted in an IoU of 0.803244 after 50 epochs, with L1Loss and Adam, indicating satisfactory performance despite increased complexity. Experiment 3, employing UNETR with 256 × 256 pixel images, yielded lower IoU scores, with a best IoU of 0.253928, highlighting the challenge of training Transformer-based models with limited data. A critical aspect of this study was the use of a coin as a scale reference in all experiments, enabling precise conversion of pixel measurements to physical dimensions. This, combined with OpenCV for contour detection, allowed for accurate fish size estimations, validated by comparisons with real images. The results underscore UNET’s effectiveness for aquaculture image segmentation, while also emphasizing data requirements for UNETR. This approach provides a non-invasive, automated method for monitoring fish growth and health, contributing to sustainable aquaculture practices. |
| format | Article |
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| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-06-01 |
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| spelling | doaj-art-fd6fbae2726142adaed84a050dcf32e32025-08-20T03:26:10ZengMDPI AGApplied Sciences2076-34172025-06-011512687310.3390/app15126873Image Segmentation and Measurement of Trout Using a Convolutional Neural Network and Transformer ArchitectureJose Cruz0Luis Baca1Raul Castillo2Eudes Apaza3Christian Romero4Ferdinand Pineda5E.P. Ingenieria Electronica, Universidad Nacional del Altiplano, Puno 21002, PeruE.P. Ingenieria Electronica, Universidad Nacional del Altiplano, Puno 21002, PeruE.P. Ingenieria Electronica, Universidad Nacional del Altiplano, Puno 21002, PeruE.P. Ingenieria Electronica, Universidad Nacional del Altiplano, Puno 21002, PeruE.P. Ingenieria Electronica, Universidad Nacional del Altiplano, Puno 21002, PeruE.P. Ingenieria Electronica, Universidad Nacional del Altiplano, Puno 21002, PeruAquaculture plays a vital role in meeting global food demands, necessitating technological innovations for sustainable production. This study investigates deep learning-based semantic image segmentation for enhanced monitoring of rainbow trout (<i>Oncorhynchus mykiss</i>) in Puno, Peru. We conducted three experiments using UNET and UNETR architectures, varying image resolution, loss functions, and optimizers on a dataset of 1200 high-resolution images. Experiment 1, with UNET and 256 × 256 pixel images, achieved an IoU of 0.942854 after 20 epochs, using MSELoss and Adam, demonstrating superior segmentation accuracy. Experiment 2, utilizing UNET with 512 × 512 pixel images, resulted in an IoU of 0.803244 after 50 epochs, with L1Loss and Adam, indicating satisfactory performance despite increased complexity. Experiment 3, employing UNETR with 256 × 256 pixel images, yielded lower IoU scores, with a best IoU of 0.253928, highlighting the challenge of training Transformer-based models with limited data. A critical aspect of this study was the use of a coin as a scale reference in all experiments, enabling precise conversion of pixel measurements to physical dimensions. This, combined with OpenCV for contour detection, allowed for accurate fish size estimations, validated by comparisons with real images. The results underscore UNET’s effectiveness for aquaculture image segmentation, while also emphasizing data requirements for UNETR. This approach provides a non-invasive, automated method for monitoring fish growth and health, contributing to sustainable aquaculture practices.https://www.mdpi.com/2076-3417/15/12/6873segmentationtroutUNETtransformer |
| spellingShingle | Jose Cruz Luis Baca Raul Castillo Eudes Apaza Christian Romero Ferdinand Pineda Image Segmentation and Measurement of Trout Using a Convolutional Neural Network and Transformer Architecture Applied Sciences segmentation trout UNET transformer |
| title | Image Segmentation and Measurement of Trout Using a Convolutional Neural Network and Transformer Architecture |
| title_full | Image Segmentation and Measurement of Trout Using a Convolutional Neural Network and Transformer Architecture |
| title_fullStr | Image Segmentation and Measurement of Trout Using a Convolutional Neural Network and Transformer Architecture |
| title_full_unstemmed | Image Segmentation and Measurement of Trout Using a Convolutional Neural Network and Transformer Architecture |
| title_short | Image Segmentation and Measurement of Trout Using a Convolutional Neural Network and Transformer Architecture |
| title_sort | image segmentation and measurement of trout using a convolutional neural network and transformer architecture |
| topic | segmentation trout UNET transformer |
| url | https://www.mdpi.com/2076-3417/15/12/6873 |
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