Application of Artificial Intelligence and Predictive Analysis in Plant Physiology Teaching and Research

Abstract

Artificial Intelligence (AI) and predictive analysis are transforming plant physiology education and research. These technologies allow for the efficient analysis of large datasets, improving our understanding of complex plant processes and their responses to environmental factors. In education, AI-driven tools create personalized learning experiences, helping students grasp challenging concepts in plant physiology through interactive and adaptive learning environments. Predictive models provide researchers with insights into plant behavior under various conditions, enabling better crop management and sustainability practices. This research explores the integration of AI and predictive analysis in plant physiology, focusing on their impact on both teaching and research. Key findings suggest that these technologies not only improve academic outcomes but also foster interdisciplinary collaboration. However, challenges such as data quality, ethical concerns, and the complexity of biological systems must be addressed. Future prospects for AI in plant physiology include enhanced experiment design, data management, and the development of more comprehensive educational frameworks. By leveraging AI and predictive analytics, the field of plant physiology can embrace new methods of exploration, pushing the boundaries of scientific discovery and education.