A Fish Classification on Images using Transfer Learning and Matlab

Abstract

The Ministry of Marine Affairs and Fisheries (MMAF) carry out the responsibilities and features associated with the policy of marine and fisheries. MMAF carry out the responsibilities and features associated with marine and fisheries policy. One of their duties and functions are organized marine and fishery statistics, in accordance to Law of The Republic of Indonesia Number sixteen of 1997 regarding Statistics. The main problem is the number of enumerators at each Basis Landing Of Fish. This paper proposed a fish classification on fish images using transfer learning and Matlab as the first stage of tackling the problem. FishNet is a modification from AlexNet to classify Katsuwonus Pelamis (Skipjack tuna or Cakalang), Euthynnus Affinis (Tongkol) and Coryphaena Hippurus (Mahi-mahi) that caught by fishermen. There are 15.120 images of 3 type of fishes, 5.040 for each fish. Data is split into 70:30 for training and validation set. The training process is done using Matlab 2018a on Windows 7 operating system in a notebook with single CPU i7 with 8 GB RAM for 124 minutes. The validation accuracy is 99.63%.