A method overview in smart aquaculture

The application and future of biofloc technology (BFT) in aquaculture industry: A review

This review describes the applicability of biofloc technology (BFT) to future aquaculture technologies. BFT is considered an innovative alternative for solving the problems of traditional aquaculture (for example, environmental pollution, high maintenance costs, and low productivity). Extensive research is being conducted to apply BFT to breed and raise many aquatic animal species. In BFT, maintaining an appropriate C:N ratio by adding a carbon source promotes the growth of microorganisms in water and maintains the aquaculture water quality through microbial processes such as nitrification. For the efficient use and sustainability of BFT, various factors such as total suspended solids, water turbidity, temperature, dissolved oxygen, pH, and salinity, stocking density, and light should be considered. The application of the transformative fourth industrial revolution technologies, Information and Communications Technology (ICT) and Internet of Things (IoT), to aquaculture can reduce the risk factors and manual interventions in aquaculture through automation and intelligence. The combination of ICT/IoT with BFT can enable real-time monitoring of the necessary elements of BFT farming using various sensors, which is expected to increase productivity by ensuring the growth and health of the organisms being reared.

Adaptive Recognition of Bioacoustic Signals in Smart Aquaculture Engineering Based on r-Sigmoid and Higher-Order Cumulants

In recent years, interest in aquaculture acoustic signal has risen since the development of precision agriculture technology. Underwater acoustic signals are known to be noisy, especially as they are inevitably mixed with a large amount of environmental background noise, causing severe interference in the extraction of signal features and the revelation of internal laws. Furthermore, interference adds a considerable burden on the transmission, storage, and processing of data. A signal recognition curve (SRC) algorithm is proposed based on higher-order cumulants (HOC) and a recognition-sigmoid function for feature extraction of target signals. The signal data of interest can be accurately identified using the SRC. The analysis and verification of the algorithm are carried out in this study. The results show that when the SNR is greater than 7 dB, the SRC algorithm is effective, and the performance improvement is maximized when the SNR is 11 dB. Furthermore, the SRC algorithm has shown better flexibility and robustness in application.

Gaussian Distribution Model for Detecting Dangerous Operating Conditions in Industrial Fish Farming

The development of better monitoring technologies, the early combat of outbreaks, massive mortality, and promoting sustainability are challenges that the aquaculture industry still faces, and the development of solutions for this is an open problem. In this paper, focusing our attention on monitoring technologies as a promising solution to these issues, we report a Gaussian distribution model for detecting dangerous operating conditions in industrial fish farming. This approach allows us to indicate through a 2D image visualization when fish production is under normal, warning, or dangerous operating conditions. Furthermore, our proposed method has promising possibilities for application in the most varied fields of science, given that the mathematical procedure described allows us to discover the fundamental statistical structure of physical, chemical, and biological systems governed by laws of a probabilistic nature.

Intelligent Measurement of Morphological Characteristics of Fish Using Improved U-Net

In the smart mariculture, batch testing of breeding traits is a key issue in the breeding of improved fish varieties. The body length (BL), body width (BW) and body area (BA) features of fish are important indicators. They are of great significance in breeding, feeding and classification. To accurately and intelligently obtain the morphological characteristic sizes of fish in actual scenes, data augmentation is first used to greatly expand the published fish dataset, thereby ensuring the robustness of the training model. Then, an improved U-net segmentation and measurement algorithm is proposed, which uses a dilated convolution with a dilation rate 2 and a convolution to partially replace the convolution in the original U-net. This operation can enlarge the partial convolution receptive field and achieve more accurate segmentation for large targets in the scene. Finally, a line fitting method based on the least squares method is proposed, which is combined with the body shape features of fish and can accurately measure the BL and BW of inclined fish. Experimental results show that the Mean Intersection over Union (mIoU) is 97.6% and the average relative error of the area is 0.69%. Compared with the unimproved U-net, the average relative error of the area is reduced to about half. Moreover, with the improved U-net and the line fitting method, the average relative error of BL and the average relative error of BW of inclined fish decrease to 0.37% and 0.61%, respectively.