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Feed is the most main expenditure in outdoor intensive culture system. The improvement of feeding efficiency has great significance for increasing production and reducing costs. Presently, with the development of precision agriculture, automatic adjustments of the feeding amount according to the demands of the fish has become a developing trend. The objective of this paper was to develop an automatic feeding decision-making system based on the water quality parameters to solve the problem of inefficiency in artificial feeding control. In this study, we proposed an effective control method using adaptive neural fuzzy inference system (ANFIS) to achieve this purpose. First, two input variables (dissolved oxygen saturation [DO]; temperature [T]) and one output variable (feeding percent [FP]) were selected and defined. Second, the model of the linguistic variables and the optimal fuzzy rule base were obtained by the training and learning by utilization of hybrid learning methods. Finally, an ANFIS controller for on-demand feeding was developed and the performance was compared with Fuzzy logic control (FLC) and artificial control (AC) by the Nash-Sutcliffe efficiency coefficient (NS), the root mean squared error (RMSE), and fish growth parameters. The results indicated that the NS and RMSE of the ANFIS model were 0.8539 and 0.0541, respectively, and were better for forecasting feeding decisions compared with the FLC and AC methods. Compared with the AC, there was no significant differences in promoting fish growth (P > 0.05), whereas the feed conversion rate (FCR) was reduced by14.35%. In addition, the mean of ammonia nitrogen concentration decreased by 22.59%, and the mean of turbidity increased 5.5 cm to 28.9 cm, reducing eutrophication and pollution of water in pond. Therefore, applying those approaches based on ANFIS control to the feeding decision system in outdoor intensive culturing is flexible and effective, and has potential for the design of fine feeding equipment and to guide this practice for other species.

In outdoor pond culturing systems, formulated feed is the most main nutrition source and expenditure. Improving the feeding management efficiency is of great significance for reducing economic losses and alleviate ecological environmental pollution. In recent years, with the development of precision agriculture and animal welfare rearing, providing appropriate feed quantity based on the nutrient requirements of fish has become an important developing trend. In this study, a hybrid method, combining the bioenergetics factorial model with fuzzy logic control technology, was proposed to predict the feed requirement of grass carp and construct a precision feeding control (PC) system in outdoor pond culturing conditions. First, prediction of the growth parameter of grass carp using the calibrated TGC (the thermal-unit growth coefficient) model in different growth stanzas; then, prediction of energy and nutrition demands based on the model of the bioenergetics factorial; and then, determination of the feed requirement and feed rate daily according to the nutrient composition of feed and apparent digestibility coefficient (ADC) of grass carp; finally, determination of the proportion of each feeding time in per day using fuzzy logic control technology (FLC) based on dissolved oxygen (DO) and water temperature (WT). In addition, the performance of the PC system and traditional feeding control (TC) system was evaluated by the Nash-Sutcliffe efficiency coefficient (NS), the root mean squared error (RMSE), the water quality parameters, and fish growth parameters, respectively. The results indicated that the PC system was superior to that of the TC treatment for forecasting feed decisions, the NS was promoted 0.534 to 0.936 and the RMSE was reduced 22.512 to 10.973, respectively. Meanwhile, Compared the growth parameters of fish with TC treatment, it did not affect the growth performance of fish but promote it to a certain extent, and the feed conversion rate (FCR) of fish was decreased by 12.24% (P