Multi‑modal, Multi‑task Data Analysis for Automated Plant Phenotyping

• Sponsor: Texas A&M University AgriLife Research
• Role: PI: Dr. Joshua Peeples
• Dates: October 2022 – Present
• Students: Omar Khater, Fahimeh Orvati Nia, and Michael Morse
• Media links: PBS Interview, Texas A&M Today, Texas Architect Cover Story

Overview: Plant phenotyping has a profound impact on real-world problems such as food security and energy demands (i.e., biofuel). Recent advances in artificial intelligence (AI) have been successfully integrated to automate plant phenotyping, resulting in reductions of human error and improved efficiency. Texas A&M AgriLife Research’s new state-of-the-art facility provides several opportunities for global leadership in automated plant phenotyping. To achieve this, our group is developing centralized hub to analyze data for multiple tasks and across various data modalities.