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In aquaculture ponds, wireless sensor networks (WSNs) with uneven temperature distribution and low collection efficiency may lead to poor monitoring effects. To improve the performance of temperature monitoring, a high-precision fusion strategy for a hierarchical WSN is proposed. In the bottom layer, the temperature data collected by the sensors are preprocessed by an improved unscented Kalman filter. In the middle layer, each cluster head sensor, as a local fusion center, is used to fuse the data collected from the sensors by a sequential analysis and fast inverse covariance intersection (ICI) algorithm. In the top layer, a global fusion center is utilized to fuse the temperature data from the middle layer to reflect the global temperature by an improved seagull algorithm to optimize the extreme learning machine (ELM) algorithm. Through calculation and simulation, the results show that the fusion strategy not only reduces external interference but also improves the accuracy of global optimal temperature state estimation while ensuring the stability and accuracy of data fusion.

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