Phenome 2020 Digital Phenotyping Workshop

This repository contains materials from the Phenome 2020 Digital Phenotyping workshop.

The Phenome 2020 Digital Phenotyping Workshop gave participants hands-on experience processing data from state-of-the-art image-based phenotyping technologies with domain experts. Topics covered included: Image processing of 2D images, hyperspectral image processing, and machine learning.

To clone the repository, including linked repositories using git:

git clone --recurse-submodules https://github.com/danforthcenter/phenome2020-workshop.git

Session Topics

Session 1: Image Analysis using PlantCV

In this hands-on section we will use open-source open development image analysis software, PlantCV, to segment and extract trait data from visible (RGB) images.

Instructors: Haley Schuhl, Malia Gehan, and Noah Fahlgren (Donald Danforth Plant Science Center)

Session 2: Deep Learning using Deep Plant Phenomics

This hands-on section will guide participants through training a deep convolutional neural network for counting sorghum panicles in aerial field images using the Deep Plant Phenomics software.

Instructor: Jordan Ubbens (University of Saskatchewan)

Session 3: Hyperspectral Image Analysis

This hands-on section will explore techniques and algorithms used in hyperspectral image analysis including spectral unmixing, target detection, and classification methods using tools developed by the Machine Learning and Sensing Lab at the University of Florida.

Instructors: Alina Zare, Susan Meerdink (University of Florida)