Abstract
The Phalaenopsis orchid is highly valued in the ornamental flower market and is primarily cultivated in greenhouses. In a traditional commercial greenhouse, farmers must manually check daily for any signs of disease among the plants. Sick plants must be removed immediately to prevent the spread of diseases to healthy ones. In precision agriculture, farmers are expected to be alerted when a certain percentage (e.g., less than 2%) of the plants are infected so that they can be removed at the right time. Many experiments have been conducted in laboratories with constant temperature and humidity to investigate the spore germination rate, where spores typically germinate within a few days. However, these findings cannot be directly applied to large-scale greenhouses with long growth periods (over 200 days) and varying temperatures and humidity. The contribution of this letter is that we are the first to propose a sensor specifically designed for use in large-scale greenhouse environments to determine the spore germination rate for orchids. We have designed a simple yet novel algorithm to dynamically calibrate the spore germination sensor. Our experiments indicate that with the calibrated spore germination sensor, the outbreak probability can be completely eliminated, and human checking overhead can be reduced by up to 97.8%.
| Original language | English |
|---|---|
| Article number | 6001804 |
| Journal | IEEE Sensors Letters |
| Volume | 9 |
| Issue number | 2 |
| DOIs | |
| State | Published - 2025 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
Keywords
- Internet of Things
- Sensor applications
- orchid disease
- smart agriculture
- spore germination sensor
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