Visual dataset of shrimp for support in classification and comparative studies using convolutional neural networks (CNNs)
DOI:
https://doi.org/10.14295/bjs.v5i5.870Keywords:
food computing, image, seafood, convolutional neural networksAbstract
This study presents a dataset for the construction of a neural network from images of refrigerated shrimp. The images were collected at different times, during which the shrimp went from capture to the final degradation stages. After collection, the images were organized chronologically and subjected to a selection process. The Python program was applied for lighting and blur corrections, preparing the data for the modeling phase. The neural network was developed in the Keras program, with diversified learning methods to form a model capable of evaluating shrimp quality in different periods. Therefore, this study resulted in a dataset that is essential for training the CNN, and as the image quality and the processing methodology adopted were crucial to identify the defects and colorimetric modifications of the shrimp, offering an accuracy above 95%, it can therefore be considered a suitable and innovative tool for quality control in the shrimp farming industry.
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Copyright (c) 2026 Rafael Pereira Frota, Francisca Joyce Elmiro Timbó Andrade, Georgia Maciel Dias de Moraes, Raimundo Alan Freire Moreira, Leiliane Teles César, Mirla Dayanny Pinto Farias
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