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Updated: Jul 15, 2025

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

Integrated transcriptomic meta-analysis and comparative artificial intelligence models in maize under biotic stress.

Leyla Nazari¹, Muhammet Fatih Aslan², Kadir Sabanci²

¹Crop and Horticultural Science Research Department, Fars Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Shiraz, Iran. l.nazari@areeo.ac.ir.

Scientific Reports

|September 23, 2023

Summary

This summary is machine-generated.

This study identifies key maize genes that are upregulated during biotic stress, crucial for developing disease-resistant crops. Machine learning and deep learning models effectively classified these stress-responsive genes, highlighting BiLSTM

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

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Area of Science:

Genetics and Genomics
Plant Pathology
Bioinformatics

Background:

Maize (Zea mays) is susceptible to yield loss from pathogen-induced biotic stress.
Identifying genes involved in disease resistance is critical for developing resilient maize cultivars.
Understanding gene expression patterns under stress enhances knowledge of maize defense mechanisms.

Conclusions:

Deep learning, specifically BiLSTM, offers a powerful approach for classifying stress-responsive genes in maize.
The identified upregulated genes provide valuable targets for breeding disease-resistant maize varieties.
This study advances our understanding of maize's genetic response to biotic stress, aiding in crop improvement strategies.