论文标题
使用深度学习模型对越南物种的自动植物形象识别
Automatic Plant Image Identification of Vietnamese species using Deep Learning Models
论文作者
论文摘要
区分自然生态系统中数千种植物物种是很复杂的，并且已经研究了许多努力来解决该问题。在越南，识别12,000种物种的任务需要植物管理方面的专业专家，具有详尽的培训技巧和深入的知识。因此，随着机器学习的发展，已经提出了自动植物识别系统，以使包括植物学家，药品实验室，分类学家，林业服务和组织在内的各种利益相关者受益。该概念引起了全球研究人员和工程师在机器学习和计算机视觉领域的研究和应用的兴趣。在本文中，越南植物图像数据集是从越南生物的在线百科全书中收集的，以及生命百科全书，共同产生越南109种植物物种的28,046个环境图像。提出了对四个深卷积特征提取模型的比较评估，即MobilenetV2，VGG16，ResnETV2和Inception Resnet V2。这些模型已在支持矢量机（SVM）分类器上测试，以实现植物图像识别的目的。拟议的模型达到了有希望的识别率，而MobilenetV2达到了最高，达到83.9％。该结果表明，机器学习模型可能是自然环境中植物物种识别的潜力，未来的工作需要检查较大数据集上的更高精度系统以满足当前的应用需求。
It is complicated to distinguish among thousands of plant species in the natural ecosystem, and many efforts have been investigated to address the issue. In Vietnam, the task of identifying one from 12,000 species requires specialized experts in flora management, with thorough training skills and in-depth knowledge. Therefore, with the advance of machine learning, automatic plant identification systems have been proposed to benefit various stakeholders, including botanists, pharmaceutical laboratories, taxonomists, forestry services, and organizations. The concept has fueled an interest in research and application from global researchers and engineers in both fields of machine learning and computer vision. In this paper, the Vietnamese plant image dataset was collected from an online encyclopedia of Vietnamese organisms, together with the Encyclopedia of Life, to generate a total of 28,046 environmental images of 109 plant species in Vietnam. A comparative evaluation of four deep convolutional feature extraction models, which are MobileNetV2, VGG16, ResnetV2, and Inception Resnet V2, is presented. Those models have been tested on the Support Vector Machine (SVM) classifier to experiment with the purpose of plant image identification. The proposed models achieve promising recognition rates, and MobilenetV2 attained the highest with 83.9%. This result demonstrates that machine learning models are potential for plant species identification in the natural environment, and future works need to examine proposing higher accuracy systems on a larger dataset to meet the current application demand.
