Experimental Weather Station Dashboard

Live packets received at cosseboomlab.com/lora

This dashboard shows live-updated data from temperature, relative humidity, and leaf wetness data from custom weather stations. The weather stations are esp32 LoRa boards connected to sht-45 temperature/relative humidity and phytos 31 leaf wetness sensors.

Two small studies are currently being conducted. One is a shade study, where shade cloth is placed over apple trees. One sensor, sht45-3 is under the shade cloth (name = Shade) and one sensor, sht45-2 is in the canopy of an unshaded apple tree (name = Sun). A second study is a pruning study, where sht45-1 is in an optimally pruned high-density apple canopy (name = Pruned) and the other sensor, sht45-5, is in a non-pruned canopy in the same planting (name = Non-pruned).

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Temperature and Relative Humidity

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Figure. Temperature and relative humidity are both highly correlated with plant fungal disease development.

Leaf Wetness Duration (LWD)

Figure. Leaf wetness duration, in combination with temperature are two key metrics for disease development. This leaf wetness duration is informed by leaf wetness sensors or can be approximated by relative humidity being greater than 90%. In general, longer LWD values relate to increased disease infection risk.

Bitter rot risk

Model selection:

Figure. Current experiments are underway for validating the use of models that use temperature and LWD for predicting risk of bitter rot disease of apple. The curve shown uses the Cosseboom (2024) DF2-NN model from Cosseboom and Hu 2022 (trained with LWD up to 24 h). Risk above 0.45 is considered high. This model has also been validated for predicting ripe rot of grapes and is implemented in NEWA.

Chart settings

Leaf wetness duration and the bitter rot risk chart use “wet” vs “dry” from your packets. Choose per station whether wetness follows PHYTOS31 (≥ 360 mV when present; otherwise RH ≥ 90%) or RH only (≥ 90%, ignores PHYTOS).

Data Table

This is a table of received data from the wireless weather stations. Node is the name of the weather station, seq is the sequential sending identifier of the data packet, temp is temperature in degrees Celcius, RH is relative humidity, VBAT is the voltage of the battery (3.2 V is dead and 4.0 V is fully charged), and Phytos31 is the raw millivolt reading from the PHYTOS 31 leaf wetness sensor. SD is the sender-side microSD save status (S = success, E = error). RSSI and SNR are the wireless signal quality metrics. RSSI is the signal strength (closer to 0 is better), SNR is the signal to noise ratio (closer to 0 is better). Payload is the raw text data from the wireless LoRa transmission.
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Received Node Seq Temp RH VBAT Phytos31 SD RSSI SNR Payload
Table: … Chart: …