Handling similar looking disease symptoms in plants using dilation and feature reuse

Abstract

For the last decade, the use of deep learning techniques in plant leaf disease recognition has seen a lot of success. Pretrained models and the networks trained from scratch have obtained near-ideal accuracy on various public and self-collected datasets. However, symptoms of many diseases found on various plants look similar, which still poses an open challenge. This work takes on the task of dealing with classes with similar symptoms by proposing a trained-from-scratch shallow and thin convolutional neural network employing dilated convolutions and feature reuse. The proposed architecture is only four layers deep with a maximum width of 48 features. The utility of the proposed work is twofold: (1) it is helpful for the automatic detection of plant leaf diseases and (2) it can be used as a virtual assistant for a field pathologist to distinguish among classes with similar symptoms. Since dealing with classes with similar-looking symptoms is not well studied, there is no benchmark database for this purpose. We prepared a dataset of 11 similar-looking classes and 5, 108 images for experimentation and have also made it publicly available. The results demonstrate that our proposed model outperforms other recent and state-of-the-art models in terms of the number of parameters, training & inference time, and classification accuracy.