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Related Experiment Video

Updated: Feb 10, 2026

Laser-Capture Microdissection RNA-Sequencing for Spatial and Temporal Tissue-Specific Gene Expression Analysis in Plants

Laser-Capture Microdissection RNA-Sequencing for Spatial and Temporal Tissue-Specific Gene Expression Analysis in Plants

Published on: August 5, 2020

Holistic and component plant phenotyping using temporal image sequence.

Sruti Das Choudhury^1,2, Srinidhi Bashyam², Yumou Qiu³

¹1School of Natural Resources, University of Nebraska-Lincoln, Lincoln, NE USA.

|May 16, 2018

Summary

This summary is machine-generated.

This study introduces a new computer vision algorithm for automated plant phenotyping in maize, enabling detailed analysis of leaf and stem growth. The developed method and dataset facilitate advancements in understanding plant vigor and genetic regulation.

Keywords:

Component phenotypes Holistic phenotypes Image sequence analysis Plant architecture Plant phenotyping

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Area of Science:

Plant Science
Computer Vision
Agricultural Technology

Background:

Image-based plant phenotyping offers non-invasive trait extraction for large plant populations.
Holistic and component phenotypes provide insights into plant biophysical characteristics.
Automated solutions for analyzing maize leaf emergence, count, and growth are needed.

Purpose of the Study:

To develop a novel computer vision algorithm for automated detection of individual leaves and stems in maize.
To introduce new holistic and component phenotypes for plant vigor assessment.
To provide a benchmark dataset for evaluating plant phenotyping algorithms.

Main Methods:

A graph-based approach was used to detect leaves and stems from 2D visible light image sequences.
Leaf count and length were measured to monitor growth.
The University of Nebraska-Lincoln Component Plant Phenotyping Dataset (UNL-CPPD) was introduced for algorithm evaluation.

Main Results:

A novel method for reliable detection of maize leaves and stems was developed.
Temporal variations in component phenotypes were demonstrated on the UNL-CPPD dataset.
Environmental and genetic regulation of holistic phenotypes was analyzed using statistical models on the Panicoid Phenomap-1 dataset.

Conclusions:

A computer vision algorithm for automated leaf and stem detection and new component phenotype computation was created.
The UNL-CPPD dataset was publicly released to support algorithm development and comparison.
Experimental analyses confirmed the significance of environment and genetic variation in regulating maize phenotypes.