A Deep CNN Approach for Plant Disease Detection
Fatma Marzougui, Mohamed Elleuch, Monji Kherallah
Abstract
The diagnosis of the plants is carried out with a visual inspection by experts and a biological examination is the second choice if necessary. They are usually expensive and time consuming. This inspired several computer methodologies to detect plant blights based on their leaf images. We apply a computer methodology on Deep Learning systems based on artificial neural networks, this branch also allows for the early detection of plant diseases, by applying convolutional neural networks (CNNs) familiar with some of the famous architectures, notably the “ResNet” architecture, using an augmented dataset containing images of healthy and diseased leaves (each leaf is manually cut and placed on a uniform background) with acceptable accuracy rates in the research environment. This Deep Learning technique has shown very good performance for various object detection problems. The model fulfills its role by classifying images into two categories (disease-free) and (diseased). According to the results obtained, the developed system achieves better detection performances than those proposed in the state of the art. Finally, to compare their performances, we use the implementation under Anaconda 2019.10.