Fish disease detection system using fuzzy logic approach

Muhammad Amri Ambosakka (2022) Fish disease detection system using fuzzy logic approach. Universiti Malaysia Sabah. (Unpublished)

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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.

Item Type: Academic Exercise
Keyword: Fuzzy logic , Disease diagnosis , Aquaculture , Fish farming
Subjects: Q Science > Q Science (General) > Q1-390 Science (General)
S Agriculture > SH Aquaculture. Fisheries. Angling > SH1-691 Aquaculture. Fisheries. Angling > SH20.3-191 Aquaculture > SH151-179 Fish culture > SH171-179 Diseases and adverse factors
Department: FACULTY > Faculty of Computing and Informatics
Depositing User: DG MASNIAH AHMAD -
Date Deposited: 18 Jul 2022 19:43
Last Modified: 18 Jul 2022 19:43

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