AUTOMATED IDENTIFICATION AND DETECTION OF SPECIES
Estimating the quantity of fish and its presence from image sources may help biologists to understand the underwater habitats and natural environment to aid preservation.The problem of object classification lies in the main challenge of estimating the prevalence of each species of fish. Solutions to automatically detect the fish classification should be able to overcome problems related to fish size and orientation, feature variability, picture quality.The data of harvested fish are key indicators for marine resource management and sustainability. Automated Species Detection is used to record the fishing practices of vessels. The data of the harvested fish are manually read and recorded nowadays. However, this manual recording is time consuming and labour intensive. This study proposed an automatic approach for pre screening harvested fish in the Fisherman App using convolutional neural networks (CNNs).In this study, harvested fish species name in the frames of the API were detected and segmented from the background at the pixel level and its body weights is recorded in the Fisherman database.