Automatic Recognition of the Damages and Symptoms on Plant Leaves Using Parallel Combination of Two Classifiers

This study presents a multiple classifier system for automatic recognition of the damages and symptoms on plant leaves from images. The proposed approach is based on parallel combination of two kinds of classifiers, one is a neural network classifier that uses texture, color and shape features to distinguish between the damages and symptoms, then the other is a support vector machine (SVM) classifier that uses texture and shape features. In order to design our system, we have based on some existing approaches in the field that adopt a single classifier. The tests of this study were carried out on six classes including the damages of three pest insects (Leaf miners, Thrips and Tuta absoluta) and symptoms of three fungal diseases (Early blight, Late blight and Powdery mildew). The experimental results show the efficiency of our approach compared to the pervious approaches based on single classifier. The proposed approach is more effective and has the highest rate of recognition.