Individual Tree Identification and Classification Using Image Data Processing Method

Abstract

Identifying trees based on their type, such as fruit-bearing, medicinal, or forest trees, plays a crucial role in environmental monitoring, agriculture, and forestry management. This project aims to develop a robust deep learning-based system to individually identify these tree types from images of their leaves and bark. Three state-of-the-art convolutional neural network (CNN) models were employed for this purpose: ResNet50, ResNet101, and InceptionV3. The dataset used for training and testing consisted of diverse images collected from various sources, covering different tree species under varying conditions. The images were preprocessed using techniques like resizing, normalization, and data augmentation to ensure the models could learn effectively and generalize well to new samples. Each model was fine- tuned using transfer learning, leveraging their pre-trained weights on ImageNet. Performance metrics such as accuracy and loss were evaluated during the training and testing phases to compare the models. Among the three models, ResNet101 demonstrated superior performance, achieving a test accuracy of 85%. ResNet50 and InceptionV3, while still effective, exhibited slightly lower accuracy. This result highlights the effectiveness of deeper architectures like ResNet101 in capturing intricate features and patterns in leaf and bark images for tree classification. The findings of this study provide a foundation for deploying automated tree identification systems in real-world applications, such as forest management and ecological research. Future work can focus on expanding the dataset and incorporating additional models to further improve accuracy and scalability.