Abstract
Cassava, a key food security crop in Africa, can provide a large amount of carbohydrates, which is widely grown in Africa. This kind of crop has been disturbed by viral diseases for a long time, which is the main reason for low production. The symptoms of cassava diseases can be reflected in their leaves but artificial visual diagnosis of diseases is inefficient. In order to get rid of this inefficient method, it is important to establish a model to automatically diagnose cassava by using the captured crop images to help farmers save their crops. Convolutional neural network has a good performance in image classification. Based on the research of Kaiming He et al. on residual convolution network, this paper trains the image dataset of cassava leaves from Kaggle by residual networks and then compares the classification effects by using different layers. Moreover, data augmentation and Focalloss function are used to optimize the model, which achieve better performance.
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