Review of Artificial intelligence techniques for marine plant Recognition

Abstract

Marine plants are vital for supporting biodiversity and carbon sequestration, but their identification is challenging due to the complexities of the marine environment. This review examines the application of artificial intelligence (AI) techniques, particularly deep learning methods like Convolutional Neural Networks (CNNs), to enhance the recognition and classification of marine plants. It compares these AI approaches with traditional methods, which often rely on manual observation and expert knowledge—processes that can be labor-intensive and error-prone. The study assesses the effectiveness of AI techniques in enhancing marine plant recognition compared to traditional methods. We conducted a comprehensive review of various AI methodologies utilized in marine plant identification, examining their strengths and limitations, and highlighting the significance of AI in marine biology, particularly in data analysis and pattern recognition. Our findings demonstrate that AI technologies have the potential to significantly improve the identification process, with several recent studies showcasing successful applications. The review also discusses current datasets, highlights challenges such as data scarcity and environmental variability, and outlines future research directions to improve the accuracy and efficiency of AI in marine plant identification. This paper emphasizes the need to integrate AI into marine biology to advance conservation efforts and deepen our understanding of marine biodiversity, ultimately contributing to the sustainability of marine ecosystems.