1. Identification and Counting of Pirapitinga Piaractus brachypomus Fingerlings Fish Using Machine Learning
- Author
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Alene Santos Souza, Adriano Carvalho Costa, Heyde Francielle do Carmo França, Joel Jorge Nuvunga, Gidélia Araújo Ferreira de Melo, Lessandro do Carmo Lima, Vitória de Vasconcelos Kretschmer, Débora Ázara de Oliveira, Liege Dauny Horn, Isabel Rodrigues de Rezende, Marília Parreira Fernandes, Rafael Vilhena Reis Neto, Rilke Tadeu Fonseca de Freitas, Rodrigo Fortunato de Oliveira, Pedro Henrique Viadanna, Brenno Muller Vitorino, and Cibele Silva Minafra
- Subjects
aquaculture ,automation ,detection algorithm ,neural network ,South American round fish ,Veterinary medicine ,SF600-1100 ,Zoology ,QL1-991 - Abstract
Identifying and counting fish are crucial for managing stocking, harvesting, and marketing of farmed fish. Researchers have used convolutional networks for these tasks and explored various approaches to enhance network learning. Batch normalization is one technique that improves network stability and accuracy. This study aimed to evaluate machine learning for identifying and counting pirapitinga Piaractus brachypomus fry with different batch sizes. The researchers used one thousand photographic images of Pirapitinga fingerlings, labeled with bounding boxes. They trained the adapted convolutional network model with batch normalization layers added at the end of each convolution block. They set the training to one hundred and fifty epochs and tested batch sizes of 5, 10, and 20. Furthermore, they measured network performance using precision, recall, and mAP@0.5. Models with smaller batch sizes performed less effectively. The training with a batch size of 20 achieved the best performance, with a precision of 96.74%, recall of 95.48%, mAP@0.5 of 97.08%, and accuracy of 98%. This indicates that larger batch sizes improve accuracy in detecting and counting pirapitinga fry across different fish densities.
- Published
- 2024
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