Semantic Image Segmentation with Deep Learning for Vine Leaf Phenotyping

TL;DR

This paper applies deep learning techniques to semantically segment grapevine leaves in images, enabling automated phenotyping that supports sustainable agriculture by monitoring plant traits and identifying varieties.

Contribution

It introduces a deep learning-based system for automated leaf segmentation in plant phenotyping, advancing high-throughput analysis in precision agriculture.

Findings

Deep learning approaches achieved promising segmentation accuracy.

The system facilitates plant lifecycle monitoring and trait quantification.

Potential for improved crop management and variety identification.

Abstract

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.