The decisions made regarding traditional fish feeding systems mainly depend on experience and simple time control. Most previous works have focused on image-based analysis of the leftover feed at the bottom of the pond to determine whether to continue or to stop feeding. However, the feasibility of such a method in an actual outdoor aquaculture pond is low. The main reason for this is that real outdoor aquaculture ponds have turbid water quality, small feed targets, interference from intense fish activity, overlapping images of fish and feed, etc. Therefore, image-based recognition is not easy to implement in actual outdoor aquaculture. To overcome this problem, this article proposes an automatic fish feeding system based on deep learning computer vision technology. In contrast to traditional computer-vision-based systems for recognizing fish feed underwater, the proposed system uses deep learning technology to recognize the size of the waves caused by fish eating feed to determine whether to continue or to stop feeding. Furthermore, several water quality sensors are adopted to assist in feeding decisions. As a result, the proposed system uses deep learning technology to recognize the size of the water waves caused by fish eating feed to determine whether to continue to cast feed or to stop feeding. Experimental results show that an accuracy of up to 93.2% can be achieved.

In aquaculture, controlling water quality parameters is an important challenge. The water quality parameters affect the growth of aquatic organisms. Thus, maintaining water quality balance has become the primary goal of aquaculture operators. However, the traditional water quality inspection method is low in accuracy and consumes considerable time and human resources. On the other hand, since water quality sensors are immersed in seawater for a long time, algae will grow on the sensors, affecting their accuracy. Therefore, to solve the abovementioned problems, this paper reports the design and implementation of a full-time artificial intelligence of things (AIoT)-based water quality inspection and prediction system, which uses a simple recurrent unit (SRU) model to predict water quality data. With the proposed system, it is possible to collect water quality sensing data 24 hours a day and further use the SRU model for sensor data prediction to assist aquaculture farmers in managing and controlling outdoor aquaculture ponds. Moreover, a 24-hour water quality sampling tank is designed to overcome the problem of sensor error. Throughout the whole process, data are transmitted to a water quality monitoring cloud platform for further inspection and prediction. In this paper, SRU-based prediction is used to obtain predictions of water quality parameters, and three popular metrics (MAE, RMSE, and MAPE) are used to evaluate the performance. As a result, experimental results show that the proposed method offers good performance for prediction of water quality.

In the process of white shrimp farming, shrimp larvae trading is a time-consuming and laborious process. In the traditional shrimp larvae counting process, it is often necessary to count the number of shrimp larvae for sale through manual counting. However, there is not a unified standard method for counting. Computer counting methods can only identify a small number of non-overlapping shrimp larvae, and the accuracy of the methods is not high. In recent years, with the advent of deep learning, there have been many applications of counting objects. It is desirable to develop a method for counting shrimp larvae that is less labor-intensive and can achieve fast results. In this paper, a deep learning-based fast counting methodology using density estimation for counting shrimp larvae is proposed, with noteworthy advantages over traditional shrimp larvae counting methods. The experimental results showed that the average accuracy of the proposed methodology was as high as 98.72% when the number of shrimps was approximately 1,000. Moreover, the average execution time of the proposed methodology was around 12.35 s, so that the average execution time can be accepted in the real world.