THE TRANSFORMATIVE ROLE OF ARTIFICIAL INTELLIGENCE IN MODERN BOTANICAL RESEARCH: A COMPREHENSIVE REVIEW

Abstract

Botanical research, fundamental to understanding biodiversity, ecosystem functioning, and addressing global challenges like food security and climate change, is undergoing a profound transformation driven by Artificial Intelligence (AI). The field is increasingly data-rich, fuelled by high-throughput phenotyping, genomics, remote sensing, and digitized herbarium collections. This data deluge has necessitated the adoption of advanced computational tools for efficient and accurate analysis. This review paper synthesizes the current state of AI applications in botany, focusing on machine learning (ML) and deep learning (DL) techniques. Key areas covered include plant species identification and classification, phenotyping and trait analysis, stress and disease detection, ecological and conservation applications, and genomic studies. We highlight how convolutional neural networks (CNNs) have revolutionized image-based analysis, while other ML models excel in handling complex, multi-dimensional data. Furthermore, the review addresses significant challenges such as data quality, model interpretability, and computational barriers. Finally, we explore emerging trends and future directions, including the integration of multimodal data, the promise of generative AI, and the imperative for ethical and collaborative frameworks. The conclusion underscores that AI is not merely a tool but a paradigm shift, enabling unprecedented scales of analysis and discovery in botanical science.