Published Paper Details:

Plant tissue culture represents a pivotal tool in plant biotechnology, facilitating in-vitro regeneration, genetic transformation, and large-scale propagation of plant species under aseptic and controlled environmental conditions. Traditional tissue culture practices, however, are constrained by empirical trial-and-error methods, considerable labor demands, and variability in outcomes. The integration of Artificial Intelligence (AI), encompassing machine learning (ML), deep learning (DL), and optimization algorithms, has emerged as a transformative approach to address these limitations and enhance experimental precision, reproducibility, and efficiency. This article critically examines the diverse applications of AI in plant tissue culture. AI-driven image analysis employing convolutional neural networks (CNNs) enables automated monitoring of explant development, callus induction, and contamination detection with high accuracy. Machine learning models such as artificial neural networks (ANNs), genetic algorithms (GAs), and support vector machines (SVMs) have been successfully implemented to predict and optimize key culture variables, including nutrient composition, phytohormone concentrations, and environmental parameters. Predictive modeling further allows for the estimation of regeneration success rates and identification of critical determinants influencing morphogenesis and somatic embryogenesis. Additionally, the integration of AI with automation and robotic systems has advanced large-scale micropropagation, enhancing throughput and standardization. The convergence of AI with the Internet of Things (IoT) and data analytics presents a pathway toward self-regulating, intelligent biolaboratories capable of real-time optimization. Despite challenges related to data quality, cost, and interdisciplinary implementation, AI offers significant promise in redefining plant tissue culture through enhanced decision-making, reduced experimental variability, and sustainable scalability. Collectively, AI-driven innovations are poised to revolutionize plant biotechnology, ensuring more precise, efficient, and resilient systems for global agricultural advancement.