Deep Learning Based Greenhouse Image Segmentation and Shoot Phenotyping (DeepShoot).

ABSTRACT:

Background

Automated analysis of large image data is highly demanded in high-throughput plant phenotyping. Due to large variability in optical plant appearance and experimental setups, advanced machine and deep learning techniques are required for automated detection and segmentation of plant structures in complex optical scenes.

Methods

Here, we present a GUI-based software tool (DeepShoot) for efficient, fully automated segmentation and quantitative analysis of greenhouse-grown shoots which is based on pre-trained U-net deep learning models of arabidopsis, maize, and wheat plant appearance in different rotational side- and top-views.

Results

Our experimental results show that the developed algorithmic framework performs automated segmentation of side- and top-view i

SUBMITTER: Narisetti N 

PROVIDER: S-EPMC9328757 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature