Development of a consumer-grade scanning platform for fruit thermal and position data collection

Abstract

Climate change and more frequent heatwaves exacerbate the issue of fruit sunburn in orchards. To facilitate fruit temperature dynamics investigation, in relation to fruit sunburn damage occurrence, a low-cost thermal scanning platform, based on depth and thermal consumer-grade cameras, was developed to collect position and temperature fruit information. The platform exploits the Robotic Operating System (ROS) to synchronize data collection from the sensors, the YOLOv5 object detection algorithms to automatically detect fruits to be analyzed, and a Python based pipeline to align images and extract temperature and position information of the fruits (apple and grape cluster). Results referred to a first version of the system shown a high correlation between estimated and actual temperature $(r\gt0.92)$ and an acceptable positional error $(\sim 0.15 \mathrm{~m})$. Many improvements of the system are currently on-going to reach the expected performance on a second version of the platform.