Detecting fruit surface wetness using a custom-built low-resolution thermal-RGB imager

•We developed and used a custom-built low-resolution thermal-RGB imager to monitor cherry fruit skin temperature.•We developed an algorithm to calculate fruit surface temperature from thermal and RGB images.•We successfully linked fruit skin temperatures before and after simulated rainfall to surface wetness.•The in-field imaging system was successfully used to detect cherry fruit surface wetness. Sweet cherry fruit cracking caused by seasonal rains is a major source of crop loss in the U.S. Pacific Northwest region and around the globe. In-field monitoring of cherry fruit surface wetness and temperature is, therefore, very important in fruit loss management. To determine the feasibility of low-resolution thermal-RGB imagery for detecting sweet cherry surface wetness, an experiment was carried out in plots of Skeena and Selah cherry varieties with Y-trellised and vertical architecture, respectively, at the Roza Farm of Washington State University, Prosser, WA. To wet cherries, 5 mm of rain was applied by running a rain simulator for 4 min (1.25 mm min−1) above canopies. Rainwater samples were collected using five rain gauges to quantify the applied amount of water. The in-field sensing setup included two custom-built thermal-RGB imagers, a microclimate-measuring unit and two leaf wetness sensors. The imagers were installed at a height of 2.1 m above the ground surface and were about 20 cm from the target cherries. The leaf wetness sensors were next to the cherries in the field of view of the imagers. A custom computer vision algorithm was developed and used to identify leaves and cherries in thermal and RGB images and extract the surface temperatures. The relationship between raw and normalized surface temperature, and wetness level and duration was investigated. The applicability of normalized cherry surface and air temperature difference was also studied. The results revealed that low-resolution thermal-RGB imagery can be used for detecting cherry fruit wetness level and duration. There was also a high correlation between the surface temperature of leaves and cherry fruits during the wetness period suggesting the temperature of leaves as reliable surrogate for cherry surface wetness and temperature monitoring. By utilizing the proposed imagery-based system, decision aid tools may be developed for efficient rainwater removal to prevent fruit cracking.