AI-Driven Crop Breeding-revolutionizing Agriculture with Smart Technologies: A Review

Abstract

This review aims to provide an overview of the current state of AI-driven crop breeding, highlighting its applications, benefits, challenges, and future directions. The integration of artificial intelligence (AI) into crop breeding is transforming agricultural innovation by leveraging big data, machine learning (ML), and deep learning (DL) techniques. Advances in genomics, phenomics, and environmental sensing have enabled the development of high-dimensional datasets, fostering more precise and efficient breeding strategies. AI-driven approaches, including ML models like random forests and convolutional neural networks, enhance phenotypic predictions and yield forecasting. Deep learning further accelerates genotype-to-phenotype mapping by extracting key traits from large-scale datasets. Additionally, AI-powered genomic selection and gene editing tools, such as CRISPR-Cas9, are revolutionizing targeted breeding. Automation, including UAVs and high-throughput phenotyping platforms, streamlines data collection and analysis, reducing costs and improving accuracy. Despite these advancements, challenges such as data standardization, computational demands, and ethical concerns remain. Overcoming these hurdles will be critical in harnessing AI’s full potential for sustainable agriculture and global food security.

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