Deep learning-based system for measuring weak electrical signals in plants Article Swipe
Jiahao Wei
,
Zheng En-rang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2897/1/012031
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1088/1742-6596/2897/1/012031
Due to the characteristics of plant electrical signals being weak, low-frequency, and susceptible to interference, this study proposes a hardware solution involving silver chloride medical adhesive electrodes and the design of conditioning circuits to amplify the plant electrical signals and reduce noise. On the software side, deep learning algorithms are proposed to extract the voltage values from a self-built plant electrical signal acquisition system. Experiments were conducted on two aloe vera plants grown in different environments. Voltage signals were synchronously collected by using a high-precision digital multimeter with anti-interference capabilities. The measured signals from the system were used as input signals for a 1D-CNN, and the synchronized high-precision digital multimeter measurements served as network labels. The 1D-CNN network was then trained by using deep learning algorithms to fit the voltage values from the acquisition system to those of the high-precision digital multimeter. This approach effectively reduces noise and extracts accurate voltage values in the self-built measurement system. By combining hardware and software, the precision of the measurements is improved, providing a new method for measuring plant electrical signals.
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Metadata
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- article
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- 11
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4404997397Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1088/1742-6596/2897/1/012031Digital Object Identifier
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Deep learning-based system for measuring weak electrical signals in plantsWork title
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articleOpenAlex work type
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enPrimary language
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2024Year of publication
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2024-11-01Full publication date if available
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Jiahao Wei, Zheng En-rangList of authors in order
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https://doi.org/10.1088/1742-6596/2897/1/012031Publisher landing page
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://doi.org/10.1088/1742-6596/2897/1/012031Direct OA link when available
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Computer science, Artificial intelligence, Environmental scienceTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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11Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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