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首页> 外文期刊>International Journal of Computer Trends and Technology >Classification Quality of Tobacco Leaves as Cigarette Raw Material Based on Artificial Neural Networks
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Classification Quality of Tobacco Leaves as Cigarette Raw Material Based on Artificial Neural Networks

机译：基于人工神经网络的烟叶为卷烟原料分类质量

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Determination of the current tobacco grade classification performed by the tobacco commonly called grader with a variety of human frailties. Therefore it is necessary to develop classification automation tools. But earlier experiments need to be done first, in this case using Backpropagation Neural Network classification approach.From this research was obtained increased accuracy for the classification grade tobacco leaf with Backpropagation Neural Network method obtained an accuracy of 77.50%. This indicates that the feature extraction parameters such as shape, color, and texture applied to a Neural Network Backpropagation method can produce a level of accuracy that is quite accurate. Tests were also carried out to produce a level of precision and recall satisfactory as well. Based on the data testing eksperimet of 40 tested for classification grade tobacco leaf there are 8 different datasets that result accuracy between Backpropagation Neural Network with a grader.
机译：烟草通常被称为分级机，具​​有多种人类脆弱性，可以确定当前的烟草等级。因此，有必要开发分类自动化工具。但是在这种情况下，需要使用反向传播神经网络分类方法进行更早的实验。通过反向传播神经网络方法，从该研究中获得的分类级烟叶精度提高了77.50％。这表明应用于神经网络反向传播方法的特征提取参数（例如形状，颜色和纹理）可以产生相当准确的精度。还进行了测试，以产生一定水平的精度，并且召回率也令人满意。根据对40种分类级烟叶进行的数据测试，有8个不同的数据集，它们在反向传播神经网络和分级机之间产生了准确性。

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