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Disease Detection on the Leaves of the Tomato Plants by Using Deep Learning

机译：深入学习，番茄植物叶片的疾病检测

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The aim of this work is to detect diseases that occur on plants in tomato fields or in their greenhouses. For this purpose, deep learning was used to detect the various diseases on the leaves of tomato plants. In the study, it was aimed that the deep learning algorithm should be run in real time on the robot So the robot will be able to detect the diseases of the plants while wandering manually or autonomously on the field or in the greenhouse. Likewise, diseases can also be detected from close-up photographs taken from plants by sensors built in fabricated greenhouses. The examined diseases in this study cause physical changes in the leaves of the tomato plant These changes on the leaves can be seen with RGB cameras. In the previous studies, standard feature extraction methods on plant leaf images to detect diseases have been used. In this study, deep learning methods were used to detect diseases. Deep learning architecture selection was the key issue for the implementation. So that, two different deep learning network architectures were tested first AlexNet and then SqueezeNet. For both of these deep learning networks training and validation were done on the Nvidia Jetson TX1. Tomato leaf images from the PlantVillage dataset has been used for the training. Ten different classes including healthy images are used. Trained networks are also tested on the images from the internet.
机译：这项工作的目的是检测番茄田或温室植物上发生的疾病。为此目的，深入学习用于检测番茄植物叶片上的各种疾病。在该研究中，旨在在机器人上实时运行深度学习算法，因此机器人将能够检测植物的疾病，同时手动或自主地在现场或温室中徘徊。同样，还可以从由制造的温室的传感器从植物中取出的特写照片中检测到疾病。本研究中的检测疾病会导致番茄植物叶片的物理变化这些变化可以用RGB相机看到叶子上的这些变化。在先前的研究中，已经使用了植物叶片图像的标准特征提取方法检测疾病。在这项研究中，深入学习方法用于检测疾病。深度学习架构选择是实施的关键问题。因此，测试了两个不同的深度学习网络架构首先是alexnet然后挤压Zenet。对于这两种深度学习网络，在NVIDIA Jetson TX1上完成了培训和验证。来自Plantvillage DataSet的番茄叶片已被用于培训。使用十种不同的类，包括健康图像。培训的网络也在互联网上的图像上进行测试。

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