Detecting emerging frost crystals on leaf surfaces using microscopic imaging

Abstract

Natural radiation frost is a potential threat or even a serious disaster during the growth of agricultural crops. To avoid or reduce the risk of frost damage to crops, frost detection especially the timely and real-time detection of emerging frost crystals, is of great importance in agricultural frost protection. Therefore, this paper proposes an object detection model Frost-YOLOv5 based on microscopic imaging of emerging frost crystals on the leaf surface. Firstly, a YOLOv5 initial detection model was established by observing frost under natural conditions and using microscopic cameras to acquire images of emerging frost crystals formed on the leaf surface. From these images, the channel attention mechanism and spatial attention mechanism were combined to construct a small frost crystal feature depth extraction network. Secondly, the RGB three-channel Wiener filtering algorithm was used to recover the noise source images from motion blur in the frost crystal dataset. Through experimental validation on different light intensities and different frost crystal cluster datasets, the Frost-YOLOv5 model all showed better robustness compared with various object detection algorithms, and its frost crystal detection accuracy P and average accuracy AP values were 98.89% and 81.72%, respectively. Ultimately, the Frost-YOLOv5 model can effectively detect emerging frost crystal targets and provide theoretical principles and technical support for agrometeorological monitoring research.