A Fault Detection Method for Automatic Detection of Spawning in Oysters

Using measurements of valve activity (i.e., the distance between the two valves) in populations of bivalves under natural environmental conditions (16 oysters in the Bay of Arcachon, France, in 2007, 2013, and 2014), an algorithm for an automatic detection of the spawning period of oysters is proposed in this brief. Spawning observations are important in aquaculture and biological studies, and until now, such a detection is done through visual analysis by an expert. The algorithm is based on the fault detection approach and it works through the estimation of velocity of valve movement activity, which can be obtained by calculating the time derivative of the valve distance. A summarized description of the methods used for the derivative estimation is provided, followed by the associated signal processing and decision-making algorithm to determine spawning from the velocity signal. A protection from false spawning detection is also considered by analyzing the simultaneity in spawning. Through this study, it is shown that spawning in a population of oysters living in their natural habitat (i.e., in the sea) can be automatically detected without any human expertise, saving time and resources. The fault detection method presented in this brief can also be used to detect complex oscillatory behavior which is of interest to control engineering community.