Fish disease detection system using fuzzy logic approach

• Main Author: Muhammad Amri Ambosakka
• Format: Academic Exercise
• Language: English; English
• Published: 2022
• Subjects: Q1-390 Science (General); SH171-179 Diseases and adverse factors
• Online Access: https://eprints.ums.edu.my/id/eprint/33316/1/Fish%20Disease%20Detection%20System%20Using%20Fuzzy%20Logic%20Approach.24pages.pdf; https://eprints.ums.edu.my/id/eprint/33316/2/Fish%20Disease%20Detection%20System%20Using%20Fuzzy%20Logic%20Approach.pdf; https://eprints.ums.edu.my/id/eprint/33316/
• Institution: Universiti Malaysia Sabah

With recent development of technologies, the scale of aquaculture have been growing steadily. One of the biggest problem with a large scale aquaculture operation is the monitoring and detection of disease. Large operation would need expert knowledge or longer time consumption. Traditional diagnosis require human experts to diagnose the disease and this can be inaccurate. This research aims to solve this problem by providing a system to monitor the fishes remotely and to get a better accuracy of disease detection using the method of fuzzy logic. The system would help the operation to run more smoothly and reduce cost of operation for more profit. 4 common diseases were chosen for the testing of the system which was Dropsy, Fin Rot, Cotton Mouth, and Fish Tuberculosis. The system developed showed a result of 72.25% accuracy for the chosen diseases.