Revolutionizing Plant Tissue Culture: Harnessing Artificial Intelligence for Precision Propagation and Optimization

: Plant tissue culture is a process of in vitro regeneration requiring numerous resources and intensive labour to mass produce disease-free clones. Diverse factors such as sterilizing agents, media composition, and environmental conditions contribute toward successful regeneration and decide the production, such as the total shoot number, shoot length, in vitro rooting, and adaptation of plants to the external environment. Plant tissue culture, the successful induction of rapid shoot production, and subsequent root formation in plants are influenced by the utilization of appropriate growing conditions customized to each specific explant type. By carefully manipulating environmental factors, such as temperature, light, and nutrient availability, it is possible to stimulate the growth and development of new shoots in a time-efficient manner. This strategic combination of optimal growing conditions and hormone supplementation holds great promise in the domain of efficient propagation of plants through tissue culture techniques. The recent progress in artificial techniques such as artificial neural networks (ANN) and machine learning (ML) algorithms has presented promising opportunities for the development of sustainable and precise plant tissue culture processes. These techniques are widely recognized as robust techniques for assessing outcomes and enhancing the accuracy of predicting outputs in the domain of plant tissue culture. AI techniques and optimization algorithms have been applied to predict and optimize callogenesis, embryogenesis, several shoots, shoot length, hairy root culture, in vitro rooting, and plant acclimatization by helping predict sterilizing conditions, optimal culture conditions, and formulation of a suitable medium. Patents, modeling, and formulation of each stage of plant tissue culture using tools like artificial neural networks (ANNs), neuro-fuzzy logic, support vector machines (SVMs), decision trees (DT), random forests (FR), and genetic algorithms (GA) are presented. Conclusion: In this article, the current state of Artificial Intelligence (AI) algorithms, including their applications in all elements of plant tissue culture, as well as the patents that have been gained for these algorithms, are dissected in great detail.