A 1DCNN-GRU Hybrid System on FPGA for Plant Electrical Signal Feature Classification

Plant electrical signals are closely related to light conditions, and changes in light intensity lead to variations in the amplitude, frequency, and other characteristics of plant electrical signals. Therefore, real-time analysis of the relationship between plant electrical signals and light factors is crucial for monitoring plant growth status. In this study, Aloe Vera was chosen as the experimental subject, and electrical signal data were collected under different light intensities, followed by preprocessing including wavelet threshold denoising. Furthermore, a hybrid model architecture combining one-dimensional convolutional neural networks (1D-CNNs) and lightweight gated recurrent units (GRUs) was proposed to address the temporal signal characteristics of plant electrical signals and edge computing requirements. The 1D-CNN module extracts local spatial features, which are then modeled in time by the optimized GRU module with channel pruning. Model compression was achieved through parameter quantization. Finally, the computational and storage modules of the model were deployed on an FPGA development board using hardware description language for simulation verification. The results indicate that the system achieved a classification accuracy of 90.1%, a detection time of 43.2 ms, and a power consumption of 4.95 W, demonstrating the comprehensive advantages in terms of accuracy, response speed, and power consumption. This approach effectively improves data processing speed and reduces system power consumption while maintaining high classification accuracy, thereby providing technical support for the development of plant growth monitoring technologies.