Research on maize leaves surface action potential recognition method based on ResNet-18SE
Plant action potentials are rapid changes in cellular electrical potentials, serving as critical indicators of plant physiological activity. This study introduces a novel maize leaf surface action potential recognition method by leveraging the ResNet-18SE model combined with Short-Time Fourier Trans...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-03-01
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| Series: | Smart Agricultural Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S277237552500053X |
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| Summary: | Plant action potentials are rapid changes in cellular electrical potentials, serving as critical indicators of plant physiological activity. This study introduces a novel maize leaf surface action potential recognition method by leveraging the ResNet-18SE model combined with Short-Time Fourier Transform (STFT) to convert one-dimensional electrophysiological signals into two-dimensional time-frequency domain images. The dataset was enhanced using advanced techniques, including random cropping, transformations, and generative adversarial networks (GANs). The ResNet-18SE model, integrated with Squeeze-and-Excitation (SE) blocks, achieved superior classification accuracy (95.60%), precision (96.80%), and recall (98.90%), surpassing VGG16, ResNeXt, SqueezeNet, and AlexNet. These results highlight the robustness and efficiency of combining deep learning with frequency-domain analysis in plant electrophysiological signal processing, providing a scalable framework for real-time plant health monitoring and advancing the study of plant physiology. |
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| ISSN: | 2772-3755 |