Classification and Irrigation of Different Kinds of Plants with Mobile Application

E. Yalcin, Derya Yiltas-Kaplan
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Abstract

In recent years, the use of deep learning methods has become increasingly common. Deep learning methods are used in many areas such as image classification, voice recognition, text detection and recognition. Convolutional Neural Networks (CNNs) are also one of the most preferred methods in deep learning. Especially, its high performance in image classification processes makes a significant contribution to the preference of this method. There are many algorithms using the CNN architecture. In this study, model training was completed with the MobileNet model developed with CNN architecture. These trained models were integrated into the mobile application, and the plants were classified through the mobile application. In addition, the Arduino system that will work with the application has been developed for automatic irrigation of plants.
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不同种类植物的分类和灌溉与移动应用
近年来,深度学习方法的使用变得越来越普遍。深度学习方法被用于图像分类、语音识别、文本检测和识别等许多领域。卷积神经网络(cnn)也是深度学习中最受欢迎的方法之一。特别是其在图像分类过程中的高性能,为该方法的首选性做出了重要贡献。有许多算法使用CNN架构。在本研究中,使用基于CNN架构开发的MobileNet模型完成模型训练。将这些训练好的模型集成到移动应用程序中,并通过移动应用程序对植物进行分类。此外,Arduino系统将与该应用程序一起工作,用于植物的自动灌溉。
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