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Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Published on: March 28, 2025

Deep Learning in Image-Based Plant Phenotyping.

Katherine M Murphy¹, Ella Ludwig¹, Jorge Gutierrez¹

¹Donald Danforth Plant Science Center, St. Louis, Missouri, USA;

Annual Review of Plant Biology

|February 21, 2024

Summary

This summary is machine-generated.

Deep learning accelerates crop improvement by analyzing plant images for faster phenotyping. This artificial intelligence approach reduces manual labor and computational needs in plant science research.

Keywords:

artificial intelligence computer vision convolutional neural networks deep learning imaging machine learning plant phenomics

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Area of Science:

Plant Science
Computer Science
Artificial Intelligence

Background:

Crop improvement is hindered by slow phenotyping methods.
Image-based, high-throughput phenotyping offers non-destructive analysis and reduced labor.
Extracting meaningful data from large image datasets presents a significant challenge.

Purpose of the Study:

To review the fundamentals of deep learning for plant phenomics.
To assess the success and applications of deep learning in this field.
To outline best practices and identify open challenges in deep learning for crop science.

Main Methods:

Review of deep learning principles and techniques.
Analysis of existing literature on deep learning applications in plant phenomics.
Discussion of best practices and future research directions.

Main Results:

Deep learning effectively analyzes complex image data for plant phenotyping.
Numerous successful applications of deep learning in plant science have been identified.
Key challenges and areas for future development are highlighted.

Conclusions:

Deep learning is a powerful tool for overcoming phenotyping bottlenecks in crop improvement.
Further research and adoption of best practices will enhance its impact on plant science.
Addressing open challenges will unlock the full potential of artificial intelligence in agriculture.