Bangladeshi Plant Leaf Classification and Recognition Using YOLO Neural Network

Md. Khairul Islam, Sultana Umme Habiba, Sk. Md. Masudul Ahsan
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引用次数: 4

Abstract

Now a days, recognition of plant species and leaves holds a great importance in the field of medical science, environmental issues like maintaining ecological balance, preserving distinct plants etc. Using Deep Convolutional Neural Network (CNN) as a classifier, has shown tremendous success on the field of classification and detection task. In this paper, we have proposed to use the YOLOv2 model as a classifier through which we have trained the model using our leaf dataset. In this transfer learning approach our target leaf data set was used to classify leaves as our target task. Both recognition and localization of the leaves are done through this work. Multiple leaves detection and classification is also an achievement of this work. Approximately 96% classification accuracy was achieved to classify the leaves which had also shown a satisfactory localization accuracy also. Both recognition and localization of leaves will bring a success to the researchers in the field of botany, medicinal plant analysis also.
基于YOLO神经网络的孟加拉植物叶片分类与识别
如今,认识植物的种类和叶子在医学、生态平衡、保护独特植物等环境问题上具有重要意义。使用深度卷积神经网络(CNN)作为分类器,在分类和检测任务领域取得了巨大的成功。在本文中,我们建议使用YOLOv2模型作为分类器,通过该分类器,我们使用叶子数据集训练了模型。在这种迁移学习方法中,我们的目标叶子数据集被用来对叶子进行分类作为我们的目标任务。树叶的识别和定位都是通过这项工作来完成的。多叶检测与分类也是本工作的成果之一。该方法的分类精度约为96%,定位精度也较好。叶片的识别和定位将为植物学和药用植物分析领域的研究人员带来成功。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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