A Machine Learning Approach for Early Detection of Fish Diseases by Analyzing Water Quality

Early detection of fish diseases and identifying the underlying causes are crucial for farmers to take necessary steps mitigate potential outbreak thus avert financial losses with apparent negative implications national economy. Typically, caused by viruses bacteria; according biochemical studies, presence certain bacteria may affect level pH, DO, BOD, COD, TSS, TDS, EC, PO43-, NO3-N, NH3-N in water, resulting death fishes. Besides, natural processes, e.g., photosynthesis, respiration, decomposition, also contribute alteration water quality that adversely affects health. Being motivated recent successes machine learning techniques, a state-of-art algorithm has been adopted this paper detect predict degradation timely accurately. Thus, it helps preemptive against diseases. The experimental results show high accuracy detecting specific based on real datasets.