Harnessing deep learning for plant species classification: A comprehensive review

Plants are fundamental to Earth’s ecosystems and human life, playing an indispensable role in sustaining and improving the global ecological balance. Understanding plant biodiversity, including species distribution and population dynamics, is essential for ecological and environmental protection. Automated plant species classification, driven by advanced machine learning and computer vision technologies, is a key step towards biodiversity conservation. This survey presents a comprehensive review of the past decade’s research on automated plant species classification, focusing on datasets and identification methods. It highlights the progression from single-organ, single label species classification to multi-organ, multi-label approaches for more comprehensive biodiversity monitoring. The study examines deep learning approaches, emphasizing moving from supervised learning to semi-supervised, self-supervised, and few-shot learning paradigms. It also highlights the progression from Convolutional Neural Networks to Vision Transformers and the performance evaluation on six plant species datasets. A notable contribution of this study is the evaluation of state-of-the-art few-shot learning methods on six plant datasets. By synthesizing trends, key findings, and critical analyses, this paper offers valuable insights into past advancements and identifies future research directions and challenges, paving the way for enhanced automated plant species classification and biodiversity assessment.