Species-specific monitoring offers fundamental tools for natural resource management and conservation but requires techniques that target species-specific traits or markers. Sea lamprey, a destructive invasive species in the Great Lakes in North America and conservation target in Europe, is among very few fishes that rely on oral suction during migration and spawning. Yet attachment by suction has not been exploited for sea lamprey control or conservation. This dissertation is focused on advancing soft pressure sensing systems for underwater sea lamprey detection.First, a pressure sensing panel based on commercial vacuum sensors is developed to measure the suction dynamics of juvenile and adult sea lampreys, such as pressure amplitude, frequency and suction duration. Measurements from an array of sensors indicate that the suction pressure distribution is largely uniform across the mouths of lampreys, and the suction pressure does not differ between static and flowing water conditions when the water velocity is lower than 0.45 m/s. Such biological information could inform the design of new systems to monitor behavior, distribution and abundance of lampreys. Based on the measured biological information, two types of soft pressure sensors are proposed for underwater sea lamprey detection. First, a soft capacitive pressure sensor is developed, which is made using a low-cost screen-printing process and can reliably detect both positive and negative pressures. The sensor is made with a soft dielectric layer and stretchable conductive polymer electrodes. Air gaps are designed and incorporated into the dielectric layer to significantly enhance the sample deformation and the response to pressures especially negative pressure. This soft capacitive pressure sensor can successfully detect non-conductive objects like plastic blocks compressed against it or rubber suction cup attached to it; however, it does not work well underwater since water causes parasitic capacitance on the sensor that interferes with the detection.The second sensor we present is a low-cost and efficient piezoresistive pressure sensor, which consists of a layer of piezoresistive film patch matrix sandwiched between two layers of perpendicular copper tape electrodes. Here, the measured two-point resistance is not equal to the actual cell resistance for that pixel due to the cross-talk effect of the pixels. Several regularized least-squares algorithms are proposed to reconstruct the cell resistance map from the two-point resistance measurements. Experiments show that this pressure sensor is able to capture the pressure profiles during sea lamprey attachment. The performance and computational complexity of the reconstruction algorithms with different regularization functions are also compared.Finally, we design an automated sea lamprey detection system based on the piezoresistive pressure sensor array using machine learning. Three types of object detection algorithms are deployed to learn features of the mapping contours when effective attachment by ''compression'' or ''suction'' is formed on the sensor array. Their validation performance and inference speeds are evaluated and compared in depth, and YOLOv5s proves to be the best detector. Furthermore, a detection approach based on the YOLOv5s model with a confidence filter unit, is proposed. In particular, different optimal detection thresholds are proposed for the compression and suction patterns, respectively, in order to reduce the false positive rate caused by the sensor's memory effect. The efficacy of the proposed method is supported with experimental results on real-time underwater detection of sea lampreys.
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