Plant breeding is crucial for addressing global challenges like food security, climate change resilience, and sustainable agriculture. The integration of Artificial Intelligence (AI) and Machine Learning (ML) techniques has revolutionized traditional breeding methods, enabling the development of improved crop varieties. AI and ML algorithms are used for tasks such as genotype-phenotype prediction, genomic selection, trait discovery, and optimization of breeding schemes. These technologies help identify genetic markers associated with desirable traits, enabling breeders to select plants with desired characteristics more efficiently. AI-driven models can predict the performance of novel genotypes under different environmental conditions, aiding in the development of resilient and high-yielding crop varieties. AI-powered tools can optimize breeding strategies by simulating breeding outcomes, reducing time and resource constraints. However, challenges such as data quality, model interpretability, and ethical considerations need to be addressed. Additionally, the accessibility of advanced computational resources and expertise remains a barrier for many breeders, especially in developing countries. The future of AI and ML in plant breeding holds great promise, with continued advancements in computational biology, genomics, and data analytics. Collaboration between breeders, data scientists, and biotechnologists is essential for leveraging AI and ML technologies to their full potential in addressing global agricultural challenges.
Artificial intelligence, Machine learning, Plant breeding
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