ALS 5984-21396 High-Throughput Phenotyping in Agriculture

Spring 2021

Instructors

Name: Gota Morota
Office: 368 Litton Reaves Hall
Email: morota@vt.edu
Web: http://morotalab.org/
Office Hours: By appointment

Name: Song Li
Office: 508 Latham Hall
Email: songli@vt.edu
Web: https://lilabatvt.github.io/research/
Office Hours: By appointment

Time and Location

Mon/Wed 12:20 - 1:10 pm
Online

Course Description

This course will cover the application of computer vision to plant and animal sciences. Smart agriculture is innovative technology that integrates advances in engineering and computer science into agriculture to optimize production while ensuring sustainability. In particular, the use of image-based high-throughput phenotyping technologies enables us to collect a large number of phenotypes in an automated fashion with less human labor and reduced costs, and evaluate phenotypes that were previously difficult to be measured manually. We will discuss the most recent high-throughput phenotyping technologies that can be used in agriculture. The course will combine instructions and hands-on plant and animal phenotypic data analysis. You will be provided with Python or GNU Octave image processing scripts and learn how to apply them to image data.

Learning Objectives

After taking this course, the student will be able to:

understand computer vision technologies commonly used in high-throughput phenotyping
set up phenotyping systems, perform image acquisition, and analyze image data using image processing tools
critically review current literature in computer vision and high-throughput phenotyping

Texts and Reading Materials

Lecture slides will be provided on the class website. There will be no required textbook.

Syllabus

[PDF]

Schedule

Lectures will be delivered via Zoom.

1/20 (W): Overview of plant and animal phenotyping
1/25 (M): Introduction to plant phenotyping
1/27 (W): Introduction to plant phenotyping
2/1 (M): Introduction to animal phenotyping
2/3 (W): Introduction to image processing
2/8 (M): RGB-D sensor cameras for depth sensing
2/10 (W): Applications of RGB-D sensor cameras in animal science
2/15 (M): Plant phenotyping using UAV
2/17 (W): Plant phenotyping using UAV
2/22 (M): WebODM demonstration
2/24 (W): Drone data processing using GRID
3/1 (M): RGB and multispectral image analysis
3/3 (W): RGB and multispectral image analysis
3/8 (M): Machine learning and neural networks
3/10 (W): Machine learning and neural networks
3/15 (M): Machine learning and neural networks
3/17 (W): Spring break
3/22 (M): Predicting the body weight of animals
3/24 (W): Pig image analysis using GNU Octave
3/29 (M): Pig image analysis using GNU Octave
3/31 (W): Pig image analysis using GNU Octave
4/5 (M): Dairy cattle image analysis using GNU Octave
4/7 (W): Dairy cattle image analysis using GNU Octave
4/12 (M): Dairy cattle image analysis using GNU Octave
4/14 (W): Applications of Plant Phenotyping
4/19 (M): Phenomics in the era of genomics
4/21 (W): No class
4/26 (M): Spring break
4/28 (W): Student presentations