ObjectivePlant leaf segmentation aims to segment leaf regions from backgrounds for removing background object interferes, which is important for plant disease recognition and species identification.

MethodIn this paper, a fully convolutional neural network (FCNN) was designed for plant leaf image segmentation. First, a log-logic function as objective function replaces the complex Softmax function, which transforms the segmentation task into a binary classification problem suitable for plant leaf segmentation. Second, the batch normalization (BN) technology was introduced into the FCNN, which improved the convergence of the whole FCNN. Finally, due to the lack of evaluation index in the research of plant leaf segmentation, receiver operating characteristic (ROC) curve, as a new evaluation protocol, was designed. It reflected the changes between recall rate and false alarm rate of plant leaf segmentation under different threshold settings.

ResultThe method reduced the complexity of parameters and improved the convergence performance of FCNN. Experimental results showed that this method was more complete to segment the leaf image than the color-based method in Leafsnap. The proposed ROC curve adequately evaluated the performance of plant leaf segmentation.

ConclusionCompared with traditional plant leaf segmentation methods, the proposed method based on deep learning realizes input image by end to end processing, and does not require pre-processing like image conversion, noise filter and morphological operations, etc. Therefore, this method can be used for leaf segmentation.