Deep Learning Modeling for Local Wildflower Recognition
Keywords:
Wildflower, Transfer Learning, Convolutional Neural Network, VGG-19, Performance Measures, AccuracyAbstract
Bangladesh is a small country surrounded by greenery. This nation is coated in a variety of lush vegetation, including grasses, flowers, and trees. As time passes, the atmosphere and soil also alter. Because of this, many people are unaware of the true names of many flowers, and some are currently in danger of going extinct. It will be challenging for the new generation to learn about these flowers. Bangladesh is home to a wide variety of regional flora. The Datura Metel, Hill Glory Bower, and Periwinkle plants will be the subjects of our effort. We used four deep neural network models VGG-16, VGG-19, MobileNetV2, and Resnet50 to carry out our research. We gathered a data set of images of three local wildflowers, i.e. Datura Metel, Hill Glory Bower, and Periwinkle flower images, processed them beforehand, and taught the four deep learning models on them separately. We used the holdout method to evaluate each of the four deep-learning models. For each of these models, we tuned different hyperparameters and came up with the best-configured model. Following successful testing, we discovered that the VGG-19 model had performed the best classification performance exhibiting an accuracy of 99.2%.
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