Deep learning-empowered crop breeding: intelligent, efficient and promising.

Area of Science:

• Agricultural Science
• Computer Science
• Genetics

Background:

• Traditional crop breeding faces challenges like complex data, data acquisition issues, and low prediction accuracy, leading to inefficiency and long cycles.
• Improving crop yield and quality is crucial for agricultural production.

Purpose of the Study:

• To explore the application of deep learning in crop breeding.
• To discuss the mechanisms, applications, and impact of deep learning on accelerating crop improvement.
• To highlight current challenges and future prospects of deep learning in this field.

Main Methods:

• This perspective reviews existing literature and research on deep learning applications in crop breeding.
• It analyzes the integration of deep learning techniques into various stages of the crop breeding process.
• The discussion covers the impact on data analysis, trait prediction, and variety selection.

Main Results:

• Deep learning significantly enhances prediction accuracy in crop breeding.
• It optimizes the breeding process, leading to accelerated crop improvement and development of superior varieties.
• Applications include genomic selection, trait prediction, and disease resistance breeding.

Conclusions:

• Deep learning offers a powerful strategy to overcome traditional breeding limitations, increasing efficiency and reducing cycle times.
• Its adoption promises the development of higher-yielding, more adaptive, and disease-resistant crop varieties.
• Addressing current challenges will further unlock the potential of deep learning in revolutionizing agricultural production.