TinyML-Based Real-Time Drift Compensation for Gas Sensors Using Spectral–Temporal Neural Networks
The implementation of low-cost sensitive and selective gas sensors for monitoring fruit ripening and quality strongly depends on their long-term stability. Gas sensor drift undermines the long-term reliability of low-cost sensing platforms, particularly in precision agriculture. We present a real-ti...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
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| Series: | Chemosensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-9040/13/7/223 |
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