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Related Experiment Video

Updated: Jun 10, 2025

Author Spotlight: Unraveling Plant Responses to Abiotic Stresses Using the PlantScreen Robotic Platform

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Published on: June 7, 2024

Enhanced climate change resilience on wheat anther morphology using optimized deep learning techniques.

Arifa Zahir¹, Zulfiqar Ali², Ahmad Sami Al-Shamayleh³

¹Department of Bioscience, COMSATS University, Islamabad, 45550, Pakistan.

Scientific Reports

|October 18, 2024

Summary

Climate change heat stress impacts wheat anther morphology. Deep learning, specifically LeNet, accurately categorizes wheat germplasm records, improving plant breeding management and stress tolerance insights.

Keywords:

Artificial Intelligence Convolution neural network Deep Learning Inception V3 Inception V4 LeNet Machine Learnring ResNet

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Published on: August 5, 2020

Area of Science:

Agricultural Science
Plant Biology
Computational Biology

Background:

Climate change-induced temperature increases threaten global food security by reducing wheat yields.
Terminal heat stress significantly impacts wheat spike fertility, affecting pollen viability and anther development.
Understanding anther morphology variations is crucial for breeding climate-resilient wheat varieties.

Purpose of the Study:

To investigate the effects of heat stress on wheat anther morphology using high-resolution imaging.
To evaluate the efficacy of Deep Learning (DL) algorithms for categorizing agricultural records and monitoring spring wheat germplasm.
To identify optimal DL models for enhancing plant breeding management and understanding abiotic stress tolerance.

Main Methods:

High-resolution images from a DinoLite Microscope were used to measure wheat anther dimensions (length and width) via object identification.
Multiple Deep Learning algorithms, including Convolution Neural Network (CNN), LeNet, and Inception-V3, were implemented for record classification.
Performance metrics such as Precision, Recall, and F1 Measure were employed to assess classification accuracy.

Main Results:

LeNet demonstrated superior accuracy in classifying wheat germplasm records, outperforming CNN by 52% and Inception-V3 by 70%.
Object identification techniques accurately measured anther dimensions, revealing varietal differences under heat stress.
The study provided insights into the genetic basis of abiotic stress tolerance in different wheat varieties.

Conclusions:

Deep Learning, particularly LeNet, offers a powerful, data-driven approach for enhancing agricultural record categorization and plant breeding management.
Accurate measurement of anther morphology using imaging and DL can aid in identifying wheat varieties with improved heat stress tolerance.
This research contributes to developing more resilient wheat germplasm, crucial for ensuring food security in a changing climate.