Addressing Fisheries Policies in Pakistan by Using Deep Learning

Abstract

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Pakistan has been facing an acute decline in the aquamarine population, especially fisheries, over the last few years. Therefore, the current study attempted to facilitate policymakers in identifying and addressing fish shortage using Artificial Intelligence (AI), particularly deep learning (DL). Policymakers can achieve this objective with the help of biologists and experts working in the field of AI. Presently, manual identification methods are still utilized in fish breeding, however, aquaculture is the only sector where the use of DL is increasing rapidly. For this, marine biologists and ichthyologists must have a precise taxonomy of fish species, if they intend to understand fish behavior deeply. Most current algorithms are designed to recognize fish in dry environments due to various challenges, such as background noise, picture distortion, the existence of other water bodies in images, low image quality, and occlusion. Due to the rapid growth of DL, the use of computer vision in agriculture and farming to generate agricultural intelligence has become a current hotspot in the field of research. Automatic classification of fish is severely limited by the inability to reliably distinguish between different fish species and taxonomic groups. Once the data is split into a "train" and "test" set, the former's features may be retrieved. Layer-specific feature extraction was performed for the current study. Subsequently, the model was trained using AlexNet and several machine-learning classification methods were compared to improve the classification accuracy. To demonstrate the use of DL in order to address the extinction of fisheries in Pakistan, a dataset of different types of fish was used, taken from Kaggle which measured 541MB. After choosing the dataset, AlexNet was used for the classification and to split the data into test (70%) and train data (30%). Afterwards, the features of train data were extracted on layers and AlexNet was used to train the model. Later, different machine learning classification algorithms were used to find the best classification accuracy, which may help to identify the fish breeds that are facing the threat of extinction. Moreover, policymakers may use the results to formulate policies in order to address the problem.