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Apricot Disease Identification based on Attributes Obtained from Deep Learning Algorithms

机译:基于深度学习算法获得的属性的杏病识别

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In recent years, deep learning widely used in image processing field, has introduced many new applications related to the agricultural field. In this study, for apricot disease detection were used deep learning models such as AlexNet, Vgg16, and Vgg19 based on pre-trained deep Convolutional Neural Networks (CNN). The deep attributes obtained from these models are classified by K-Nearest Neighbour (KNN) method. To calculate the performance of the proposed methods was applied 10- fold cross-validation test. The dataset consists of 960 images including healthy and diseased apricot images. According to the obtained results, the highest accuracy was obtained as 94.8% by using Vgg16 model.
机译:近年来,深度学习广泛应用于图像处理领域,引入了许多与农业领域相关的新应用。在这项研究中,基于预先训练的深度卷积神经网络(CNN),使用了深度学习模型(例如AlexNet,Vgg16和Vgg19)用于杏病的检测。从这些模型获得的深层属性通过K最近邻(KNN)方法进行分类。为了计算所提出方法的性能,应用了10倍交叉验证测试。该数据集由960幅图像组成,包括健康和患病的杏图像。根据所得结果,使用Vgg16模型可获得最高的准确度,为94.8%。

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