  Plant phenotyping refers to a quantitative description of the plants
properties, however in image-based phenotyping analysis, our focus is primarily
on the plants 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.
