Plant diseases detection with low resolution data using nested skip connections

At the moment, there are increasing trends of using deep learning for plant diseases detection. However, their implementations may be difficult in developing countries due to several reasons. First, existing deep learning models are usually trained with images with adequate resolutions. In developing countries however, with limited internet connection, models that would perform well even when data with low resolution are used are needed. Secondly, the generated models are large. Hence, most deep learning based applications are available on-line. Unfortunately, the trend for new deep learning architectures are either have larger models or require a heavy memory usage. So, models with smaller size would be preferred. In this paper, we evaluate various existing deep learning models for plant diseases detection when low resolution data are used. They are: VGGNet, AlexNet, Resnet, Xception, and MobileNet. Our focus is deep convolutional neural network (DCNN) which is commonly applied for image data. We also propose a new DCNN architecture with two branches of concatenated residual networks. It is well known that the deeper the networks the better performance of DCNN. However, DCNN with very deep networks and large number of training parameters is prone to vanishing gradient problems. One solutions for that is to apply residual networks as branches to DCNN. While it is found that increasing the branch of the networks benefit the performance, larger memory are required to train the networks. So, we apply two concatenated residual networks only. We called it Compact Networks (ComNet). We compare our method other with six popular CNN architectures. We evaluate the performance on the PlantVillage dataset and our own dataset. We collected images of tea leaves which consist of 6 classes: 5 classes of diseases that are commonly found in Indonesia and a healthy class. Our experiments show that our method is generally better than referenced DCNN networks.