Abstract

Plants have long been recognized as natural sources of therapeutic compounds, with their various parts, including flowers, being used in traditional treatments for centuries. Flowers, in particular, have captivated humans with their beauty. However, the classification and identification of specific flowers for therapeutic use can be challenging using conventional methods. Researchers have turned to modern tools like cameras and computers to aid in this process. Despite their limitations, the need for more efficient and accurate methods has led to the exploration of artificial intelligence (AI). This study seeks to evaluate various AI-based methods utilized by researchers in the field of flower analysis, highlighting their strengths and weaknesses to inform future research. The advanced analytical tools available today are instrumental in creating a chemical fingerprint of flowers. Chromatographic and spectroscopic techniques, used to determine precise chemical composition, offer valuable scientific insights into traditional medicine. Plant part identification often commences with feature extraction. Any plant part is digitally captured multiple times and subjected to different feature extraction methods. Common basic features include color, texture, and shape, while deep learning features like CNNs are also employed. We analyze and review diverse approaches reported in recent literature, examining their advantages and potential applications.

Keywords

Flower Identification, Machine Learning, Convolutional Neural Network, Artificial Intelligence, Chemical Finger Print Analysis,

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References

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