Plant Leaf Segmentation and Phenotypic Analysis Based on Fully Convolutional Neural Network

HighlightsModify the U-Net segmentation network to reduce the loss of segmentation accuracy. Reducing the number of layers U-net network, modifying the loss function, and the increase in the output layer dropout. It can be well extracted after splitting blade morphological model and color feature.Abstract. From the perspective of computer vision, the shortcut to extract phenotypic information from a single crop in the field is image segmentation. Plant segmentation is affected by the background environment and illumination. Using deep learning technology to combine depth maps with multi-view images can achieve high-throughput image processing. This article proposes an improved U-Net segmentation network, based on small sample data enhancement, and reconstructs the U-Net model by optimizing the model framework, activation function and loss function. It is used to realize automatic segmentation of plant leaf images and extract relevant feature parameters. Experimental results show that the improved model can provide reliable segmentation results under different leaf sizes, different lighting conditions, different backgrounds, and different plant leaves. The pixel-by-pixel segmentation accuracy reaches 0.94. Compared with traditional methods, this network achieves robust and high-throughput image segmentation. This method is expected to provide key technical support and practical tools for top-view image processing, Unmanned Aerial Vehicle phenotype extraction, and phenotype field platforms. Keywords: Deep learning, Full convolution neural network, Image segmentation, Phenotype analysis, U-Net.