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Related Experiment Video

Updated: Jun 17, 2025

Author Spotlight: Streamlining Rice Breeding with CRISPR/Cas for Obtaining Optimal Phenotypic and Agronomic Traits

Author Spotlight: Streamlining Rice Breeding with CRISPR/Cas for Obtaining Optimal Phenotypic and Agronomic Traits

Published on: January 3, 2025

Artificial intelligence in plant breeding.

Muhammad Amjad Farooq¹, Shang Gao¹, Muhammad Adeel Hassan²

¹State Key Laboratory of Crop Gene Resources and Breeding, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences (CAAS), International Maize and Wheat Improvement Center (CIMMYT) China office, Beijing 100081, China; Nanfan Research Institute, CAAS, Sanya, Hainan 572024, China.

Trends in Genetics : TIG

|August 8, 2024

Summary

This summary is machine-generated.

Artificial intelligence (AI) is revolutionizing plant breeding by enhancing data collection, unlocking genetic diversity, and bridging the genotype-phenotype gap. AI-driven precision breeding optimizes crop traits and agricultural sustainability for future environments.

Keywords:

artificial intelligence big data deep learning genetic gain plant breeding

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Area of Science:

Agricultural Science
Computational Biology
Genetics

Background:

Modern plant breeding aims to increase crop productivity using advanced technologies.
Artificial intelligence (AI) excels in big data analysis and pattern recognition, impacting various scientific fields.
AI offers transformative potential for crop improvement and agricultural sustainability.

Purpose of the Study:

To explore the broad applications of AI in plant breeding.
To highlight AI's role in data collection, genetic diversity, and genotype-phenotype gap analysis.
To demonstrate AI's capacity for developing climate-resilient crop cultivars and optimizing cropping systems.

Main Methods:

Reviewing AI applications in plant breeding data management and analysis.
Investigating AI's utility in accessing and utilizing genetic diversity from genebanks.
Examining AI's role in precision gene editing and phenotype prediction.

Main Results:

AI facilitates efficient data collection and analysis in breeding programs.
AI tools unlock genetic diversity within genebanks for enhanced crop improvement.
AI bridges the genotype-phenotype gap, enabling the development of tailored crop varieties.
AI enhances precision in gene editing and predicts gene variant effects on phenotypes.

Conclusions:

AI-driven precision breeding significantly improves the efficiency of crop development.
AI optimizes cropping systems through inter-cropping and crop-rotation models, boosting sustainability.
AI technologies are crucial for developing climate-resilient crops and ensuring future food security.