Yapay Zekanın Botanik Bahçelerindeki Rolü: Bitki Tanımlamanın Geliştirilmesi

This review explores the potential of artificial intelligence (AI) in botanical gardens, with a particular focus on plant species identification. The primary objective is to evaluate the effectiveness of AI models, such as Convolutional Neural Networks (CNNs), in addressing challenges related to environmental variability, data limitations, and species diversity. The methodology involves a systematic analysis of existing literature, assessing studies that apply AI to plant identification within botanical gardens. Findings reveal that AI achieves high accuracy rates for common plant species, often exceeding 90%, but faces significant challenges with rare or endangered species due to insufficient and inconsistent training data. Environmental factors, including lighting conditions, seasonal changes, and mixed-species environments, further impact AI performance, emphasizing the need for advanced preprocessing techniques and multi-modal data integration. The review also examines the ethical implications of AI applications in botanical gardens, particularly regarding data privacy and the conservation of biodiversity. It underscores the importance of mitigating risks associated with sensitive data misuse while ensuring AI tools complement traditional conservation methods. The conclusion highlights the transformative potential of AI to enhance plant identification and biodiversity management in botanical gardens. However, it calls for ongoing advancements in AI technologies, collaborative governance models, and robust ethical frameworks to ensure effective and sustainable implementation.