Published Paper Details:

Artificial Intelligence (AI) and Machine Learning (ML) are transforming plant taxonomy and biodiversity monitoring by enabling accurate, rapid, and automated plant species identification from images of leaves and flowers. Accurate, fast, and scalable identification of plant species from images of leaves and flowers is central to biodiversity monitoring, agriculture, conservation and citizen science. This paper reviews recent advancements in AI-driven plant identification, focusing on computer vision techniques and deep learning models such as Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs). These models analyze morphological and color features from large datasets like PlantVillage, Leafsnap, and PlantNet to distinguish species with remarkable precision. Integration of AI tools into mobile applications and cloud-based systems has enhanced field-level biodiversity assessment and agricultural diagnostics. The paper also discusses challenges including dataset bias, environmental variability, and the need for explainable and domain-adaptive models. Through improved data diversity, model transparency, and ethical AI deployment, AI-powered plant identification systems are poised to support sustainable biodiversity management, ecological research, and education. This review emphasizes the potential of AI as a cornerstone for sustainable innovations in plant sciences and precision agriculture. Advances in computer vision and machine learning especially convolutional neural networks (CNNs) and transformer-based models have made automated plant identification viable at large scale. This review synthesizes the literature on image-based plant species identification, describes common datasets and pipelines, compares representative model performances, discusses practical deployment challenges (domain shift, field conditions, interpretability), and outlines research directions for robust, sustainable, and equitable AI tools for plant identification.