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首页> 外文会议>International Conference on Computer and Information Sciences >Detection And Classification Of Apple Diseases using Convolutional Neural Networks
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Detection And Classification Of Apple Diseases using Convolutional Neural Networks

机译：使用卷积神经网络对苹果病进行检测和分类

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In agricultural products, fruit diseases could lead to economic loss. In this paper, we focus on an important fruit-apples. Disease classification could be done by a human expert, which is the old way, costs a lot of money, and is also time-consuming. Computer vision (CV) and deep learning techniques show promising results with good accuracy and less time. In this paper, we have considered apple diseases like apple scab, apple blotch, and apple rot; these are fungal diseases. The dataset of the apples were collected from the local market; from that sample, we picked the apples which were already infected. Different models based on convolutional neural network are used for the classification of healthy apples and identifies the diseases apple. All the models showed good classification accuracy on more than 90% on testing images. The best accuracy was achieved by model-5; it gave 99.17%.
机译：在农产品中，水果疾病可能导致经济损失。在本文中，我们重点研究了重要的水果。疾病分类可以由人类专家完成，这是旧的方法，花费很多钱，而且很费时间。计算机视觉（CV）和深度学习技术以良好的准确性和更少的时间显示出令人鼓舞的结果。在本文中，我们考虑了苹果病，如苹果黑星病，苹果斑和苹果腐烂。这些是真菌病。苹果的数据集是从当地市场收集的;从该样本中，我们选择了已经被感染的苹果。基于卷积神经网络的不同模型用于对健康苹果进行分类并识别疾病苹果。所有模型在测试图像上均显示出超过90％的良好分类精度。 Model-5实现了最佳的精度;它给出了99.17％。

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