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Disease Detection on the Plant Leaves by Deep Learning

机译：通过深度学习对植物叶片进行病害检测

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Plant disease detection by using different machine learning techniques is very popular field of study. Many promising results were already obtained but it is still only few real life applications that can make farmer's life easier. The aim of our research is solving the problem of detection and preventing diseases of agricultural crops. We considered several models to identify the most appropriate deep learning architecture. As a source of the training data, we use the PlantVillage open database. During research, the problem with PlantVillage images collection was detected. The synthetic nature of the collection can seriously affect the accuracy of the neural model while processing real-life images. We collected a special database of the grape leaves consisting of four set of images. Deep Siamese convolutional network was developed to solve the problem of the small image databases. Accuracy over 90% was reached in the detection of the Esca, Black rot and Chlorosis diseases on the grape leaves. Comparative results of various models and plants using are presented.
机译：通过使用不同的机器学习技术进行植物病害检测是非常受欢迎的研究领域。已经获得了许多有希望的结果，但是只有很少的现实应用可以使农民的生活更加轻松。我们研究的目的是解决检测和预防农作物疾病的问题。我们考虑了几种模型来确定最合适的深度学习架构。作为培训数据的来源，我们使用PlantVillage开放式数据库。在研究过程中，发现了PlantVillage图像收集问题。集合的综合性质在处理现实生活中的图像时会严重影响神经模型的准确性。我们收集了一个由四组图像组成的葡萄叶数据库。开发了深度暹罗卷积网络以解决小图像数据库的问题。葡萄叶上的埃斯卡，黑腐病和绿化病的检测精度达到90％以上。提出了各种模型和工厂使用的比较结果。

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