Left 3-Engel elements in groups
Left 3-Engel elements in groups
复制标题
左 3-恩格尔元素分组
DOI：
10.1016/j.jpaa.2003.10.007
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发表时间：
2002
影响因子：
0.8
通讯作者：
A. Abdollahi
中科院分区：
文献类型：
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作者：
A. Abdollahi
Ardabil以提供优质、廉价的药草的理想环境而闻名。各种植物部位被用作生产天然药物的必要成分。根据IUCN(国际自然保护联盟)的记录，许多药用植物濒临灭绝，因此使用图像处理和计算机视觉算法来区分药用植物的证据至关重要。因此，有益的治疗植物的数字化对于保护生物多样性至关重要。本文研究了基于卷积神经网络(CNN)的印度树种识别技术。最近，几个深度学习框架被用于识别、识别和描述各种植物。这项研究主要集中在确定在农村地区可以找到的药用植物。转移学习技术选择了一种著名的预先训练的CNN架构，称为移动网络v2。药用植物数据集是使用30种不同类别的药用植物建立的，总共有3000张照片，这些模型用它们预先训练的权重进行了评估。在一个坚持测试集上，训练后的模型具有98.05%的准确率，证明了该方法的实用性。
Ardabil is well-known for offering the ideal environment for a good, cheap medicinal herb. Various plant parts are used as essential components in producing natural medicines. According to IUCN (International Union for Conservation of Nature) records, many medicinal plants are on the verge of extinction, so employing image processing and computer vision algorithms to distinguish proof of medicinal plants is critical. As a result, the digitalization of beneficial therapeutic plants is critical for biodiversity preservation. The use of Convolutional Neural Network (CNN)-based techniques to distinguish Indian leaf species is investigated in this research. Several Deep Learning frameworks have recently been used to discern, identify, and characterize various plants. This study is mostly focused on identifying medicinal plants that can be found in rural areas. The Transfer Learning technique selected a well-known pre-trained CNN architecture called mobile net v2. The medical plant dataset was built using 30 different classes of medicinal plants, totaling 3000 photos, and these models were assessed with their pre-trained weights. On a held-out test set, the trained model had an accuracy of 98.05 percent, demonstrating the practicality of this approach.