Intelligent Measurement of Frontal Area of Leaves in Wind Tunnel Based on Improved U-Net

Abstract

Research on the aerodynamic characteristics of leaves is part of the study of wind-induced tree disasters and has relevance to plant biological processes. The frontal area, which varies with the structure of leaves, is an important physical parameter in studying the aerodynamic characteristics of leaves. In order to measure the frontal area of a leaf in a wind tunnel, a method based on improved U-Net is proposed. First, a high-speed camera was used to collect leaf images in a wind tunnel; secondly, the collected images were corrected, cut and labeled, and then the dataset was expanded by scaling transformation; thirdly, by reducing the depth of each layer of the encoder and decoder of U-Net and adding a batch normalization (BN) layer and dropout layer, the model parameters were reduced and the convergence speed was accelerated; finally, the images were segmented based on the improved U-Net to measure the frontal area of the leaf. The training set was divided into three groups in the experiment. The experimental results show that the MIoUs were 97.67%, 97.78% and 97.88% based on the improved U-Net training on the three datasets, respectively. The improved U-Net model improved the measurement accuracy significantly when the dataset was small. Compared with the manually labeled image data, the RMSEs of the frontal areas measured by the models based on the improved U-Net were 1.56%, 1.63% and 1.60%, respectively. The R2 values of the three measurements were 0.9993. The frontal area of a leaf can be accurately measured based on the proposed method.