AWN CropAI

Functionality & Mechanism

Developed by Washington State University's AgWeatherNet, the system integrates with supported thermal-RGB cameras to capture in-field imagery of fruit. The interface facilitates automated fruit detection via segmentation algorithms, which then calculate fruit surface temperature (FST) using the hottest 20% of pixels to assess sunburn risk. A parallel module conducts HSV color analysis to quantify red, green, and yellow proportions for maturity tracking. The platform also delivers hourly, weather-guided FST forecasts based on user location and regional network data.

Evidence & Research Context

• The application's design and scientific basis are detailed in a development and evaluation paper from the WSU PrecisionAg Laboratory.
• The system leverages fruit surface temperature (FST)—a validated, non-destructive indicator of sunburn susceptibility in apples—as its primary metric for risk assessment.
• Its weather-guided FST forecast model is currently calibrated specifically for the 'Honeycrisp' apple cultivar.
• The platform incorporates an optional crowdsourcing feature, enabling users to contribute anonymized thermal-RGB imagery to refine and expand forecasting models for additional cultivars.

Intended Use & Scope

This system is designed for apple growers, crop consultants, and agricultural researchers as a field-based decision support tool. Its primary utility is the real-time assessment of sunburn risk and fruit color to inform management strategies, such as optimizing the timing of irrigation or shade deployment. The tool provides risk data, not prescriptive treatments.