Plant Species Classification Using Transfer Learning by Pretrained Classifier VGG-19

TL;DR

This paper employs transfer learning with the VGG-19 model to classify Swedish plant species from leaf images, achieving high accuracy and demonstrating the effectiveness of deep learning in botanical identification.

Contribution

It introduces a transfer learning approach using VGG-19 for plant species classification, improving accuracy over previous methods.

Findings

Achieved 99.70% classification accuracy.

Effective use of image preprocessing and augmentation.

Superior performance compared to prior research.

Abstract

Deep learning is currently the most important branch of machine learning, with applications in speech recognition, computer vision, image classification, and medical imaging analysis. Plant recognition is one of the areas where image classification can be used to identify plant species through their leaves. Botanists devote a significant amount of time to recognizing plant species by personally inspecting. This paper describes a method for dissecting color images of Swedish leaves and identifying plant species. To achieve higher accuracy, the task is completed using transfer learning with the help of pre-trained classifier VGG-19. The four primary processes of classification are image preprocessing, image augmentation, feature extraction, and recognition, which are performed as part of the overall model evaluation. The VGG-19 classifier grasps the characteristics of leaves by employing pre-defined hidden layers such as convolutional layers, max pooling layers, and fully connected layers, and finally uses the soft-max layer to generate a feature representation for all plant classes. The model obtains knowledge connected to aspects of the Swedish leaf dataset, which contains fifteen tree classes, and aids in predicting the proper class of an unknown plant with an accuracy of 99.70% which is higher than previous research works reported.