Research on efficient pest identification system for edge computing terminals based on Transformer-ConvLSTM

This study proposes a domestic edge computing terminal pest identification system based on large model compression and lightweight technology, integrating new algorithms based on Transformer and ConvLSTM, and optimizing its performance in resource-constrained environments through adaptive deployment strategies. By evaluating the performance of four different models (Transformer ConvLSTM, CNN, LSTM and Transformer) in terms of recognition accuracy, inference speed, computing resource consumption and adaptive deployment effect, this study reveals the advantages and disadvantages of each model in edge computing tasks. The model based on Transformer ConvLSTM performed best in recognition accuracy, reaching 94.5%, significantly better than other models; while the CNN model was the most efficient in inference speed, reaching 52 frames per second (FPS). The adaptive deployment strategy of the Transformer ConvLSTM model, the Transformer ConvLSTM model can dynamically adjust the model complexity to effectively cope with higher computing resource requirements, thereby improving inference speed and reducing resource consumption. On resource-constrained devices, the optimized Transformer ConvLSTM model can maintain high recognition accuracy and significantly reduce latency. Although the compression strategy slightly affects the accuracy, it greatly improves the inference speed, especially in the CNN model.