Deep Transfer Learning for Automatic Plant Species Recognition.

Abstract

Image processing has emerged as a promising tool for plant species recognition, allowing individuals to capture images with their mobile phones in the field and identify plant species or a list of closely related plants. Deep learning, particularly Convolutional Neural Networks (CNNs), has become the leading approach in image recognition tasks. This study explores the use of transfer learning, a deep learning technique, for automatic plant species recognition. Transfer learning involves using pre-trained CNN models, originally trained on large datasets like ImageNet, and fine-tuning them for specific tasks with smaller datasets. In this research, six pre-trained CNN models—VGG16, VGG19, DenseNet121, InceptionResNetV2, MobileNet, and MobileNetV2—were evaluated on a dataset comprising 30 plant species. The goal is to determine which transfer learning model performs best for plant species recognition.