Document Type : Research Paper
Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA Cawangan Melaka (Kampus Jasin), 77300 Merlimau, Melaka, Malaysia
Try Smart Bite Sdn Bhd, The Vertical Business Suite (A-28-07), Bangsar South, No 8, Jalan Kerinchi, 59200 Kuala Lumpur, Malaysia
The fishing industry has become an important income source in the world. However, fish diseases are considered a serious problem among the fishermen as it tends to spread quickly through the water. In decades, fish diseases have been diagnosed manually by the naked eyes of experienced fish farmers. Despite being time-consuming since some lab works are required in determining the relevant microorganisms that cause the diseases, this classical method most often leads to an inaccurate and misleading result. Accordingly, a fast and inexpensive method is therefore important and desirable. Convolutional Neural Network (CNN) performance has recently been demonstrated in a variety of computer vision and machine learning problems. Thus, a study on fish diseases detection using CNN is proposed. A total of 90 images of healthy leaf and two types of fish diseases which are White spot and Red spot was tested. The application of CNN to a variety of testing datasets returned good detection accuracy at 94.44\%. It can be inferred that the CNN is relatively good in detecting and classifying the type of diseases among infected fishes. Regardless, a study with a better number of datasets could be done in the future to improve the detection performance.