Artificial Neural Network-Based Decision Support for Shrimp Feed Type Classification

Gamara, Rex Paolo C.; Loresco, Pocholo James M.; Neyra, Romano Q.

doi:10.1109/hnicem48295.2019.9072876

Cited by 8 publications

(1 citation statement)

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“…The findings show a positive response, with four degrees of water quality classification: excellent, good, regular, and bad. Better management approaches, combined with suitable growth monitoring and shrimp feed control, can boost agricultural profitability, according to [18]. Manual shrimp growth measurement on a large population is a time-consuming and difficult task that requires the use of back propagating artificial neural networks.…”

Section: Related Workmentioning

confidence: 99%

Transfer Learning-based Optimal Feature Selection with DLCNN for Shrimp Recognition and Classification

2022

IJIES

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Shrimp is a main international food item with a significant economic value, as well as one of the most vital animal protein sources. The variety of shrimps can be found in aquaculture. Thus, it is necessary to categorize each shrimp. The conventional machine learning approaches are failed to classify the multiple classes of shrimp, which causes large economic losses in the shrimp farming industry. Therefore, this article proposes a hybrid mechanism for shrimp recognition and classification (SRC), which is named as transfer learning-based optimal feature selection (TLOFS) with deep learning convolutional neural network (DLCNN). Initially, transfer learning based AlexNet is used to extract the features from the shrimp samples. Then, machine learning based iterative random forest algorithm (IRFA) is utilized to select the optimal features from the AlexNet extracted features, which can also identify the relationship between various shrimp classes. Finally, DLCNN is trained and tested with the optimal features and classifies the various shrimp categories, hereafter the proposed hybrid model is named as TLOFS with DLCNN. Obtained simulations discloses the effectiveness of proposed TLOFS with DLCNN model with 99.98% of accuracy, and 99.97% of F1-score as compared to state-of-art SRC approaches.

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Section: Related Workmentioning

confidence: 99%