Emerging AI-Powered Technologies for Plant Tissue Imaging and Phenomics

Abstract

Monitoring, tracking, and analyzing the dynamic growth of a living organism is essential to understanding its response to changes in its surrounding environment. Imaging tools to study these dynamics at spatial and temporal scales with optimal resolution rely on high-performance instrumentations. These systems are generally costly, stationary, and not flexible. In addition, performing non-destructive high-throughput phenotyping to extract roots' structural and morphological features remains challenging. We developed the MultipleXLab: a modular, mobile, and cost-effective robotic root imager to tackle these limitations. Among its advantages associated with a large field-of-view, integrated programmable plant-growth lighting, and high magnification with a high resolving power, the system is useful for a wide range of biological applications. We have also created the MultipleXLab Advanced; this configuration turns the system into a mobile environmental chamber by also featuring temperature control and automated irrigation. Another system we developed was the MultipleXLab Advanced Fluorescence to allow fluorescence imaging with a resolution that competes with a fluorescence binocular or even a fluorescence microscope. Furthermore, we have implemented various technologies and techniques to facilitate 3D imaging and quantification, ranging from X-ray micro-Computed Tomography to 3D segmentation of tissues, cells, and cellular compartments within the cell imaged using Confocal Laser Scanning Microscopy. For future research, we have conceptualized an upscaled system named MultipleXLabXL. This larger system will allow tracking, monitoring, and quantifying root growth of a much higher number of seedlings for more extended periods.

Date of Award	Dec 20 2022
Original language	English (US)
Awarding Institution	Biological, Environmental Sciences and Engineering
Supervisor	Ikram Blilou (Supervisor)