Successfully trained a deep learning model which can precisely predict the species of flowers based on their images.

The planet is home to numerous flower species. Some species, like roses, have a variety of hues. The names and details of every flower are tough to recall. Additionally, people could mistakenly identify similar floral species. For instance, although having similar names and flower forms, white champaka and champak have different colours and petal lengths. Currently, the only way to identify any specific flower or flower species is to search for information based on one's own knowledge and professional experience. The availability of such expertise may provide a challenge in this investigation. These days, only keyword searches and word processors can be used to find such material online. The problem is that even then, the searcher would still need to come up with keywords that were both relevant and sufficient.

Therefore, I've created a deep learning model for the purpose of classifying flowers into recognizable categories which can make it easier for us to identify them in an automated manner.

Link: https://www.kaggle.com/datasets/sauravagarwal/flower-classification

The dataset consists of raw jpg images of five types of flowers. It is partitioned into training, validation and test directories. Original source are not partitioned. Original source is https://storage.googleapis.com/download.tensorflow.org/example_images/flower_photos.tgz Flower types - daisy, dandelion, roses, sunflowers, tulips.

• Numpy
• Pandas
• Seaborn
• Matplotlib
• Scikit-learn
• OpenCV
• Keras
• Tensorflow

Tung, KC (2020), “Flower Images jpg”, Mendeley Data, V1, doi: 10.17632/738sdjm6h9.1