Internet of Things enabled crop physiology sensing system for abiotic crop stress management in apple and sweet cherry

This study discusses development and field validation of an abiotic stress-monitoring system, i.e. crop physiological sensing system (CPSS) for perennial speciality crops. In current form, CPSS acquires and processes the thermal-RGB imagery as well as site-specific weather data through field sensing nodes and predicts apple and cherry crop specific abiotic stress indicators. The system processes the data on a single board computer to perform real-time prediction of the fruit surface temperature (FST, °C), a prominent indicator for sunburn susceptibility in apple and the fruit wetness (%) that is related to cracking susceptibility in sweet cherry. The developed system was validated in the field condition and results indicate that imagery derived apple FST (Ti) had strong correlation (R
²
= 0.64) with ground truth measured FST (Tg) with no significant difference. Cherry FST data as a predictor variable also had a strong correlation between actual and predicted wetness (R
²
= 0.80). Overall, developed CPSS could be reliably utilized for sunburn and wetness prediction in respective apple and sweet cherry.