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AgWeatherNet ports Fruit Surface Temperature (FST) Model

Posted by Bernardita Sallato | June 7, 2022

Written by Sean Hill, Lav Khot and R. Troy Peters, WSU IAREC, Prosser, WA, June 2022


Registered users of AgWeatherNet (AWN) can now access the Fruit Surface Temperature (FST) model through the web portal at FST Model (Beta) | AgWeatherNet at Washington State University (  Users can also find the tool in the left-hand menu by clicking the “Models” option and finding the “FST Model”. The AWN portal also allows user to setup an email or text-based alert by sign-in to account and customizing a new alert, see left figure, so they get real-time notification when estimated FST surpasses the user’s defined threshold temperature.

web page screen shot

The AgWeatherNet ported FST model was developed by Dr. Troy Peter’s team, and it has been collaboratively refined and validated for Honeycrisp cultivar through USDA-NIFA funding. The model employs a heat energy balance approach to predict apple FST. The model utilizes grower-subscribed, AWN station-specific weather data (e.g., air temperature, dew point temperature, solar radiation, and wind speed) and crop parameters (e.g., fruit diameter, emissivity, albedo, and shading condition) to estimate apple fruit surface temperature. This model also forecasts future apple fruit surface temperature, up to 7 days, for effective heat stress and sunburn management decision making.

The 7-day model forecast is based on weather parameters from the ‘National Digital Forecast Database’, and Clear Sky solar radiation calculations. The Clear Sky calculation provides a maximum FST estimate, while lower FST values could be expected in cloudy or overcast conditions.

This model has default input values for the Honeycrisp cultivar, while research continues for additional cultivars.  As the model is extremely sensitive to input values, users may need to tune the model based on their own data including fruit size, albedo, or emissivity estimates. The model output can be visualized in tabular or graphical format and pertinent data can be downloaded to share with your field team. Feedback about effectiveness, user interface, questions, or comments on the beta page can be sent by email to

Additional reading:

Li, L., Peters, R.T., Zhang, Q., Zhang, J., Huang, D., 2014. Modeling Apple Surface Temperature Dynamics Based on Weather Data. Sensors. 14, 20217-20234. doi:

Ranjan, R., Khot, L. R., Peters, R. T., Salazar-Gutierrez, M.R., Shi, G., 2020. In-field crop physiology sensing aided real-time apple fruit surface temperature monitoring for sunburn prediction. Computers and Electronics in Agriculture. 175, 105558. doi:

Washington State University