Semantic Image Segmentation with Deep Learning for Vine Leaf Phenotyping

Abstract

Plant phenotyping refers to a quantitative description of the plant's properties, however in image-based phenotyping analysis, our focus is primarily on the plant's anatomical, ontogenetical and physiological properties. This technique reinforced by the success of Deep Learning in the field of image based analysis is applicable to a wide range of research areas making high-throughput screens of plants possible, reducing the time and effort needed for phenotypic characterization. In this study, we use Deep Learning methods (supervised and unsupervised learning based approaches) to semantically segment grapevine leaves images in order to develop an automated object detection (through segmentation) system for leaf phenotyping which will yield information regarding their structure and function. In these directions we studied several deep learning approaches with promising results as well as we reported some future challenging tasks in the area of precision agriculture. Our work contributes to plant lifecycle monitoring through which dynamic traits such as growth and development can be captured and quantified, targeted intervention and selective application of agrochemicals and grapevine variety identification which are key prerequisites in sustainable agriculture.