Abstract:
Flower species classification stands as a quintessential challenge in the realm of computer
vision, bearing profound implications for botanical research, conservation, and ecological
management. In this study, we embark on a rigorous exploration of deep learning
architectures, meticulously assessing their efficacy in discerning intricate floral
characteristics. Leveraging a bespoke dataset meticulously curated to encapsulate the rich
diversity of floral species, we subject ResNet50, ResNet152, DenseNet121, and
DenseNet201 to exhaustive scrutiny. Through this meticulous inquiry, we unearth the
paramount importance of model selection in elucidating the subtle nuances inherent to
floral taxonomy. Emerging from this crucible of computational inquiry, DenseNet201
emerges as the paragon of accuracy and efficiency, wielding an unparalleled accuracy of
99.61% with a minuscule loss of 0.06. Meanwhile, DenseNet121 stands as a stalwart
contender, boasting a commendable accuracy of 96.56%. Beyond the realm of
computational achievements, our findings resonate with broader implications for
botanical research and conservation efforts. By shedding light on the transformative
potential of transfer learning and DenseNet architectures in the realm of floral diversity
analysis, this study not only advances the frontiers of technological innovation but also
invigorates interdisciplinary collaborations poised to reshape our understanding of floral
ecosystems. Through this convergence of computational prowess and botanical inquiry,
we navigate towards a future where advanced technologies serve as indispensable allies
in the noble pursuit of biodiversity conservation and ecological stewardship.
