论文标题
使用卷积神经网络通过树皮纹理分类对树种自动识别
Automated Identification of Tree Species by Bark Texture Classification Using Convolutional Neural Networks
论文作者
论文摘要
树种的鉴定在林业保护，疾病诊断和植物生产等相关任务中起着关键作用。关于树的一部分，无论是叶子，水果，鲜花还是树皮，都有一场辩论。研究证明，树皮至关重要，因为尽管季节性变化，但它仍将存在，并通过结构的变化为树提供了特征性的身份。在本文中，通过使用BARKVN-50数据集的树皮纹理来利用计算机视觉方法对50种树种进行分类来提出一种基于深度学习的方法。到目前为止，这是树皮分类的最大树木数量。卷积神经网络（CNN），RESNET101已使用基于转移学习的微调技术实施，以最大程度地提高模型性能。该模型在评估过程中产生的总体精度> 94％。性能验证是使用K折交叉验证并通过对从Internet收集的未见数据进行测试进行的，这证明了该模型对现实世界使用的概括能力。
Identification of tree species plays a key role in forestry related tasks like forest conservation, disease diagnosis and plant production. There had been a debate regarding the part of the tree to be used for differentiation, whether it should be leaves, fruits, flowers or bark. Studies have proven that bark is of utmost importance as it will be present despite seasonal variations and provides a characteristic identity to a tree by variations in the structure. In this paper, a deep learning based approach is presented by leveraging the method of computer vision to classify 50 tree species, on the basis of bark texture using the BarkVN-50 dataset. This is the maximum number of trees being considered for bark classification till now. A convolutional neural network(CNN), ResNet101 has been implemented using transfer-learning based technique of fine tuning to maximise the model performance. The model produced an overall accuracy of >94% during the evaluation. The performance validation has been done using K-Fold Cross Validation and by testing on unseen data collected from the Internet, this proved the model's generalization capability for real-world uses.
