Ai/ML Affinity Group

Welcome

Our focus is on emerging methods in artificial intelligence (AI) and machine learning (ML) as they apply to plant phenotyping.

Join the group by filling out the form below.

We want to promote connections among the plant phenotyping community and AI/ML researchers, as well as study challenges, algorithms & methods, theory, software frameworks, and the latest strategies for creating knowledge from phenotyping data.

2024 Webinars & Workshops

Below is a list of the upcoming webinars and workshops. If you would like to propose an event, please get in touch.

Need to contact the group? You can use the contact form here.

 

2024 Webinars & Wokshops

  • Turning the Phenotyping Bottleneck Into a Pipeline

    Valerio Hoyos-Villegas

    Assistant Professor in Plant Breeding and Genetics in the Department of Plant Science at McGill University

    Date: May 17, 2024
    Time: 10:00 AM PST.

    Despite progress in multiple areas, measuring phenotypes in plant breeding remains a bottleneck in the crop improvement cycle. This becomes particularly true when dealing with complex trait dissection. In the coming years, a revolution in plant breeding will be realized thanks to advanced in photo optic, ultrasonic, capacitive sensors, etc. This seminar focuses on the work that the Pulse Breeding and Genetics laboratory carries out in the advancement of phenomics and genomics for the acceleration of genetic gain for the development of pulse crop varieties.

  • Learning the Shape of Root Architectures

    Alexander Bucksch, Ph.D.

    Associate Professor - School of Plant Sciences.
    The University of Arizona

    Date: April 23, 2024.
    TIme: 10:00 AM PST.

    A plant's history can often be inferred from its shape phenotype. This is especially evident in roots, which demonstrate significant plastic responses in their architecture to changing environmental conditions during development. Machine learning and artificial intelligence provide methodologies to organize the complex shape signals detected by sensors, enabling the identification of simple and understandable rules governing root architecture phenotypes. From a shape-oriented perspective, this talk introduces five applications of traditional machine learning and artificial intelligence to measure and understand root architecture both in excavated roots and within the soil.

  • Statistics and AI: Occam vs. Hickam

    Jennifer Clarke, Ph.D.

    Professor, Departments of Statistics, Food Science and Technology.
    University of Nebraska-Lincoln

    Date: March 15, 2024
    Time:     1:00 PM EST (10:00 AM PST)

    In this presentation we will discuss two different philosophies behind scientific modeling that emphasize either simplicity or complexity, respectively. This tension underlies the approach of statisticians to predictive modeling. We will discuss how this tension is handled through a statistical lens with an example from regularization in regression contexts. We will then present how deep learning methods appear to take a different approach, explicitly ignoring this tension with (surprisingly) good results. We follow this with a hypothesis that may explain these results and the associated costs of modeling in high dimensions. We conclude with some challenges to the adoption and use of AI for modeling, with references for further study.

 

 Contact Ai/ML Group

Need to contact the group? You can use the contact form here.

Join the Group

Active NAPPN Members can join the group. Be sure to specify what type of involvement you are looking for, whether its just a member, or if you are interested in a more substantive role.

 
 

Top Image: Romanesco broccoli