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Related Experiment Video

Updated: Jan 4, 2026

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

A Telemetric, Gravimetric Platform for Real-Time Physiological Phenotyping of Plant–Environment Interactions

Published on: August 5, 2020

Assessing plant performance in the Enviratron.

Yin Bao^1,2, Scott Zarecor³, Dylan Shah^1,4

¹1Department of Agricultural and Biosystems Engineering, Iowa State University, Ames, IA USA.

|October 30, 2019

Summary

This summary is machine-generated.

The Enviratron facility enables precise environmental control for plant phenotyping. Its robotic rover system automates detailed plant measurements, offering new insights into genotype × environment interactions.

Keywords:

Climate change Crop plants Environment Growth chambers Hyperspectral imaging PAM-fluorometry Robot

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Area of Science:

Plant Science
Agricultural Engineering
Environmental Science

Background:

Understanding genotype × environment (G × E) interactions is crucial for plant performance assessment.
Controlled environmental conditions are necessary for accurate G × E studies.
The Enviratron facility at Iowa State University provides a controlled environment for plant growth and development studies.

Purpose of the Study:

To describe the Enviratron facility and its capabilities for plant phenotyping.
To highlight the automated, sensor-based measurement system.
To emphasize the in situ measurement approach.

Main Methods:

Utilizes custom-designed plant growth chambers to simulate diverse environmental conditions.
Employs a robotic rover equipped with an industrial robotic arm and multiple sensors (RGB, hyperspectral, thermal, ToF cameras, laser profilometer, PAM fluorometer).
Features automated leaf probing and "eye-in-hand" robotic control for precise, in situ phenotypic measurements.

Main Results:

The Enviratron facilitates continuous, 24/7 monitoring of plant growth and physiological parameters.
The robotic rover autonomously collects detailed phenotypic data using advanced sensors and computer vision.
Automated leaf probing offers precise sensor placement, a key advantage over other phenotyping systems.

Conclusions:

The Enviratron provides unprecedented control over plant growth parameters.
It optimizes the timing and positioning of sensor-based phenotypic measurements.
The system enables in situ measurement, bringing sensors to the plants for non-disruptive data collection.