Big data and artificial intelligence-aided crop breeding: Progress and prospects

Area of Science:

• Agricultural Science
• Genetics
• Bioinformatics

Background:

• Rapid progress in gene discovery, biological big data (BBD), and artificial intelligence (AI) is transforming agriculture.
• Increasing global food demand necessitates accelerated crop breeding strategies.

Purpose of the Study:

• To review current crop breeding methods and identify needs for innovation.
• To explore the integration of BBD and AI for advanced genetic analysis and prediction.
• To propose Intelligent Precision Design Breeding (IPDB) as a novel AI-driven breeding paradigm.

Main Methods:

• Review of existing breeding techniques and literature on BBD and AI applications.
• Analysis of AI and BBD integration for genetic dissection, functional gene exploration, and phenotypic prediction.
• Conceptualization and proposal of the IPDB framework and its implementation strategies.

Main Results:

• BBD and AI integration enables enhanced genetic dissection, functional gene discovery, and accurate phenotypic prediction.
• The proposed IPDB framework aims to improve the predictability, efficiency, and cost-effectiveness of crop breeding.
• CropGPT exemplifies IPDB by integrating biological techniques, bioinformatics, and breeding expertise into a cooperative system.

Conclusions:

• IPDB, powered by AI, represents a significant advancement over current crop breeding technologies.
• IPDB offers integrated platforms and services for diverse stakeholders, fostering collaboration in crop improvement.
• The proposed system is well-suited to address future challenges in crop breeding and food security.