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The field of plant breeding has witnessed significant transformations over millennia evolving from rudimentary selection strategies (Breeding 1.0) in ancient times to sophisticated techniques in the modern era (Breeding 4.0) which can identify the desirable alleles and engineer the plant to contain them all in a short amount of time, in essence, creating 'designer plants'. This evolution aims to enhance crop variety and improve food security. However, challenges, such as climate change, population growth and limited arable land, necessitate more precise and efficient breeding methods. Here, artificial intelligence (AI) emerges as a promising solution. By mimicking human intelligence, AI can process vast datasets efficiently, addressing the complexities of modern plant breeding. In this context, AI facilitates high-throughput phenotyping, gene functional analysis and the processing of extensive environmental data. It revolutionises decision-making by transforming fragmented market information into systematic breeding strategies. This review explores the historical journey of plant breeding, emphasising the shift from traditional methods to AI-driven approaches. It highlights AI's critical role in developing climate-resilient and pest-resistant crops, ensuring that key staples like maize, wheat, rice, tomato, potato and cotton can meet global food security challenges effectively.
Ansari et al. (Sun,) studied this question.
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