关键词: ANFIS, 精准投喂, 水温, 体重, 水产养殖

Abstract: In intensive aquaculture, feeding level is directly related to production efficiency and breeding cost. How to achieve precise feeding is the key issue of aquaculture. In this paper, a prediction method of feeding quantity based on adaptive network fuzzy inference system was proposed. Taking the tilapia as  research object, 2 factors of water temperature and average weight of fish were selected as input variables, and an adaptive network fuzzy inference system prediction model was established. The temperature sensor and weight scale were used to record the water temperature and the average weight of the fish, respectively. The experiment was carried out for two months. Results showed that the root mean square error (RMSE), the mean absolute error (MAE) and mean absolute percentage error (MAPE) from the ANFIS feeding prediction models were 1.18, 0.74 and 0.003 1, respectively, which were far less than the index values of the fuzzy reasoning feeding amount prediction model. The validity and rationality of the feeding forecasting model were proved. It was clear that the ANFIS network forecasting ability was better than the fuzzy reasoning forecasting model. This model not only saved labor costs, but also provided technical support and theoretical support for scientific and reasonable feeding.

Key words: ANFIS, precise feeding, water temperature, weight, aquaculture