Deep Learning for Vegetation Image Segmentation in LAI Measurement

For the measurement of LAI (Leaf Area Index) by DHP (Digital Hemispherical Photography) method, imprecise segmentation is the key error source. In this paper, to our knowledge, a deep learning algorithm is used for the first time to segment upward hemispherical image of vegetation. Pix2pix, a general mapping learning model, was improved in our study to make it more suitable for processing segmentation problem. Thousands of images collected in the field were labeled to train the model, and the conventional methods based on pattern recognition, such as the Otsu and HSV, were compared. The result shows that the improved pix2pix algorithm significantly improved the accuracy of the segmentation, which reached to 0.9834. Furthermore, this model has a good performance in processing pictures of complex environments, and the segmentation of edge details has also been optimized. Those results show that the method has great potential to improve the LAI measurement accuracy.