Abstract
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.
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Claims 5
Claim · model-written summary
The system classified Aloe Vera signal features with 90.1% accuracy.
Aloe veraOrganisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“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%”
Claim · model-written summary
System detection time was 43.2 ms.
Not reportedTaxon
Verbatim
e0“a detection time of 43.2 ms”
Claim · model-written summary
System power consumption was 4.95 W.
Not reportedTaxon
Verbatim
e0“a power consumption of 4.95 W”
Claim · model-written summary
The approach improved data processing speed.
Not reportedTaxon
Verbatim
e0“This approach effectively improves data processing speed”
Claim · model-written summary
The approach reduced system power consumption while maintaining high classification accuracy.
Not reportedTaxon
Verbatim
e0“reduces system power consumption while maintaining high classification accuracy”