Ingredient
Leaf wetness sensor
Also known as: LWS, Phytos 31
Sensor that measures the duration and degree of leaf-surface wetness — a critical input variable for plant-disease prediction models. Many fungal and bacterial pathogens require a minimum number of consecutive wet leaf hours at a given temperature to infect. The sensor mimics a leaf's thermal mass and surface texture; outputs an analog voltage proportional to surface conductance. The ingredient that turns a weather station into a disease-forecast system. Hobbyist ~$30–80 (DFRobot, generic resistive); research-grade $300+ (METER Phytos 31, capacitive).
Inputs / outputs
- Power: 3.3V or 5V, ~5 mA
- Output: analog voltage; calibrate against known-wet and known-dry reference
- Mounting: at canopy height, oriented at the canopy’s prevailing exposure angle
Solves / unlocks
- Apple-scab disease prediction (Mills curve: leaf-wet hours × temperature)
- Grape downy mildew forecasting
- Tomato early-blight pressure tracking
- Optimized fungicide-application timing (spray only when conditions justify)
- Dew-formation detection (irrigation scheduling avoids wet leaves overnight)
Constraints
- Calibration is essential — sensor surfaces age and get coated; recalibrate seasonally.
- Mounting matters — orientation, height, and shading change the reading materially.
- Resistive sensors corrode in long deployments — capacitive variants last longer.
Source
- DFRobot resistive: https://wiki.dfrobot.com/Capacitive_Soil_Moisture_Sensor_SKU_SEN0193 (similar capacitive principle)
- METER Phytos 31 (research-grade): https://www.metergroup.com/en/meter-environment/products/phytos-31
See also
Auto-generated from this entry’s typed relations: frontmatter, grouped by relation type so the editorial signal isn’t flattened.
- Member of: [[ingredient]]
- Combines with: [[bme280-environmental-sensor]] · [[arduino-uno]] · [[esp32]] · [[lorawan]]
What links here, and how
Inbound connections from across the wiki, grouped by lens and by relationship. These appear automatically — every entity page declares what it links to, and that data populates here on the targets.
Practical
contains
- Farm-tech toolkit sensor / fungal-disease-prediction input variable
combines with
- LoRaWAN remote orchard / vineyard pest-pressure mesh
combines
- Recipe: orchard disease-prediction station the load-bearing variable for the Mills curve
- Recipe: closed-loop greenhouse climate controller fungal-disease pressure — keep leaves dry through evening misting/venting
4 inbound links · 5 outbound