Serial combination of two classifiers for automatic recognition of the damages and symptoms on plant leaves

The proposed approach aims at designing an automatic recognition system of the damages and symptoms on plant leaves. It is based on serial combination of two neural networks classifiers. The first classifier uses the color to differentiate between classes. Indeed, at this phase, the damages and/or the symptoms that have a similar or a nearest color are considered to belong to the same class. Then, the second classifier is used to differentiate between classes with similar color according to the shape and texture features. The approach is tested on four classes, including the damages of two kinds of pest insects (Leaf miners and the caterpillar Tuta absoluta), and the symptoms of two fungal diseases (Downy mildew and internal Powdery mildew). The experimental results indicate that the proposed approach would be interesting to use as means of diagnosis and phytosanitary problem recognition from images of damages and symptoms.