AUTHOR=Li Longyu , Liu Tao , Huang Hui , Song Hong , He Shuangyan , Li Peiliang , Gu Yanzhen , Chen Jiawang 

TITLE=An early warning model for starfish disaster based on multi-sensor fusion

JOURNAL=Frontiers in Marine Science

VOLUME=Volume 10 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/marine-science/articles/10.3389/fmars.2023.1167191

DOI=10.3389/fmars.2023.1167191

ISSN=2296-7745

ABSTRACT=Starfish feed on shellfish and crustaceans. The starfish outbreak in coastal areas will lead to severe economic losses in marine ranching and damage the ecological environment. However, the current monitoring methods are still artificial, which is time-consuming and laborious. This study used an underwater observation platform with multiple sensors to observe the starfish outbreak in Weihai, Shandong Province. The platform could collect the temperature, salinity, depth, dissolved oxygen, conductivity, other water quality data, and underwater video data. Based on these data, The paper proposed an early warning model for starfish disasters(EWSD) based on multi-sensor fusion. A deep learning-based object detection method extracts time-series information on the number of starfish from underwater video data. For the extracted starfish quantity information, the model uses the k-means clustering algorithm to divide the outbreak risk into four levels: no risk, mild risk, medium risk, and high risk. Correlation analysis concluded that the water quality factors most closely related to the risk level are temperature and salinity. Therefore, the selected water quality factor and the number of historical starfish are inputted. The future risk level of the starfish outbreak is used as an output to train the neural network to build EWSD based on multi-sensor fusion. Experiments show that the accuracy rate of this model is 96.83%, which precision meets the needs of early warning for starfish outbreaks and has specific application feasibility.