Determining Categories of Leaf Images Using Transfer Learning of VGGNet
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Keywords:
Leaf Image Categorization, VGGNet16, Convolutional Neural Networks, Transfer Learning, Augmented ImagesAbstract
Recognizing plant species from their leaves holds great value across many domains, from
agriculture and forestry to pharmacology and the assessment of regional biodiversity. This research
focused on creating automated systems capable of identifying 12 distinct plant species solely from leaf
images. To accomplish this, we utilized the powerful VGGNet16 convolutional neural network. After
dividing our leaf images into a train set and a test set, we enriched the training data through an on-the-fly
augmentation process. By applying transformations like scaling, translation, and rotation to the images
during the network's training epochs, we improved its ability to generalize to future data, thereby
enhancing its overall performance. We applied with two approaches as training a standard VGGNet16
network and training a pre-trained VGGNet16 that had already learned features from a massive image
database. The effectiveness of these systems, configured with different parameters, was benchmarked on
the test dataset using classification metrics as F1-Score , Accuracy, and the ROC curve. Performance of
the pre-trained VGGNet16 network was observed to be markedly superior, achieving an F1-score of
98.8% compared to the 93.4% highest score from the standard VGGNet16.
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