Flower Classification Using Deep Learning Approaches

Flowers are abundantly present in our surroundings and serve as nourishment for various insects, birds, animals, and humans. Additionally, they possess medicinal properties that are beneficial to both people and animals. To effectively identify new or distinctive flower species and determine their appropriate applications, a comprehensive comprehension of flowers is imperative. The term "deep learning" has gained significant popularity across multiple domains and sectors. Within this study, a classification model was developed, utilizing deep learning algorithms for the recognition and categorization of images. Over time, deep learning architectures have evolved with increased layers and improved models, enhancing their accuracy in addressing categorization challenges. Classification, a prevalent technique, is deployed to structure data and maintain its relevance across diverse fields. Here, the specific subset of machine learning known as deep learning was harnessed to classify flowers based on their inherent characteristics. The utilization of Tensorflow in classifying floral images yielded optimal outcomes. The findings highlight that Convolutional Neural Network (CNN) categorization surpasses traditional methods in accuracy. Given deep learning's capacity to swiftly and efficiently learn from extensive data inputs, it autonomously extracts features from incoming data. Both nature enthusiasts and botanists engaged in research can find immense value in this real-time flower categorization approach, as it has practical implications for their endeavors.