Spectral Analysis of CNN for Tomato Disease Identification

Although Deep Convolutional Neural Networks have been widely applied for object recognition, most of the works have often based their analysis on the results generated by a specific network without considering how the internal part of the network itself has generated those results. The visualization of the activations and features of the neurons generated by the network can help to determine the best network architecture for our proposed idea. By the application of deconvolutional networks and deep visualization, in this work, we propose an analysis to determine which kind of images with different color spectrum provide better information to generate a better accuracy of our CNN model. The focus of this study is mostly based on the identification of diseases and plagues on plants. Experimental results on images with different diseases from our Tomato disease dataset show that each disease contains valuable information in the infected part of the leaf that responds differently to other uninfected parts of the plant.