基于卷积神经网络的开心果分类

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引用次数: 0

摘要

在农业中应用创新技术有可能提高产量生产力并影响农民的福祉。开心果被广泛认为是最珍贵的农产品之一。kirmizi和sirt是两种不同的开心果品种。对不同种类的开心果进行分类是保证产品质量和价值的关键。本文提出了一种基于卷积神经网络(CNN)模型Inception V3和ResNet50的开心果kirmizi和siirt的分类方法。本研究使用的数据集是2148个开心果图像样本。将样本图像分为80%的训练数据、10%的测试数据和10%的验证数据。首先,我们通过包裹和裁剪图像进行预处理和规范化。接下来,Inception-V3和ResNet50架构在样本数据集上进行了训练和测试。实验结果表明,两种模型的准确率分别为96%和86%。由此可以得出结论,使用Inception-V3架构的CNN模型的性能优于ResNet50架构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of Pistachio Nut Using Convolutional Neural Network
The application of innovative technologies in the agricultural industry has the potential to boost yield productivity and affect the well-being of farmers. Pistachio nuts are widely considered among the most precious things agriculture produces. The kirmizi and sirt are the two distinct varieties of pistachio nuts that are available. It is essential to categorize the different types of pistachio nuts to keep the product's quality and worth at a high level. This paper proposes a classified pistachio variety of kirmizi and siirt based on Convolutional Neural Network (CNN) models Inception V3 and ResNet50. The dataset used in this research is 2148 samples of pistachio images. The sample images are divided into 80% training data, 10% testing data, and 10% validation data. First, we pre-process and normalize by wrapping and cropping the images. The next, Inception-V3 and ResNet50 architectures, were trained and tested on the sample datasets. The experimental results show that the accuracy of both models is 96% and 86%, respectively. This can be concluded that the performance of the CNN model using Inception-V3 architecture outperforms ResNet50 architecture.      
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