Asian Journal of Research in Botany

The integration of artificial intelligence (AI) technologies into plant science has emerged as a transformative force, revolutionizing various aspects of plant research, agriculture, and environmental sustainability. This paper explores the accelerating adoption and diverse applications of AI in plant science. AI techniques, including machine learning, deep learning, and computer vision, are being leveraged to enhance plant phenotyping, crop monitoring, disease detection, and yield prediction with unprecedented accuracy and efficiency. Furthermore, AI-driven approaches are facilitating the optimization of crop breeding strategies, crop management practices, and resource allocation, thereby contributing to improved agricultural productivity and resilience in the face of climate change and global food security challenges. Moreover, AI-based models are aiding in the discovery of novel plant traits, genetic markers, and biochemical pathways, accelerating the development of stress-tolerant and high-yielding crop varieties. However, challenges such as data availability, model interpretability, and ethical considerations underscore the need for continued interdisciplinary collaboration and ethical guidelines to harness the full potential of AI in plant science responsibly. Looking ahead, the convergence of AI with other emerging technologies like robotics, remote sensing, and genomic editing promises even greater strides in understanding and manipulating plant biology for sustainable agriculture and environmental stewardship.