Plant intelligence is defined as the capacity of plants to integrate environmental signals through distributed biochemical, electrical, and epigenetic networks, resulting in adaptive physiological or developmental responses without requiring neural cognition. This definition distinguishes plant adaptive plasticity from animal cognition while maintaining a rigorous biological framework. This phenomenon has captivated biologists for decades, yet its underlying mechanisms remain elusive amid intricate biological interactions and vast, complex datasets. Recent breakthroughs in synthetic biology and artificial intelligence (AI) offer unprecedented opportunities to decode these adaptive strategies in silico . This review explores hybrid AI systems that integrates predictive AI, agent-based modelling, including large language models (LLMs) and retrieval-augmented generation (RAG), and bio-inspired algorithms, charting innovative pathways for simulating and investigating plant adaptive behaviours via synthetic biology. We highlight how these hybrid approaches bridge empirical science and theoretical insights, empowering the design of resilient synthetic biological systems that emulate plants' extraordinary adaptability to diverse challenges. • Plants show decentralized intelligence, enabling adaptive decision-making through distributed biochemical, electrical, and epigenetic signaling rather than a central nervous system. • Hybrid AI frameworks (predictive, generative, symbolic, and agent-based models) provide powerful in silico tools to decode plant adaptive strategies and complex multi-omics interactions. • Integration of hybrid AI with plant synthetic biology enables the rational design of stress-resilient, self-regulating, and biofortified crops that emulate natural phenotypic plasticity. • Future plant intelligence platforms, combining hybrid AI, synthetic biology, and advanced computing, pave the way for SMART crops capable of real-time environmental sensing, learning, and adaptive response.
Yoosefzadeh-Najafabadi et al. (Fri,) studied this question.