A hybrid combination of multiple SVM classifiers for automatic recognition of the damages and symptoms on plant leaves

Abstract

A machine vision system is reported in this study for automatic recognition of the damages and symptoms on plant leaves from images. The system is based on a hybrid combination of three SVM classifiers including an individual classifier, which is used in parallel with a serial combination of two classifiers. The individual classifier adopts two types of features (texture and shape) to discriminate between the damages and symptoms. In serial architecture, the first classifier adopts the color features to classify the images; it considers the damages and/or symptoms that have a similar or nearest color belonging to the same class. Then, the second classifier is used to differentiate between the classes with similar color depending on the shape and texture features. A combination function is provided for comparing the decision of the individual classifier and of the serial architecture in order to achieve the final decision that represents the class of the form to be recognized. The tests of this study are carried out on six classes including three types of pest insects damages and three forms of fungal diseases symptoms. The results, with an overall recognition rate of 93.9%, show the advantages of the proposed method compared to the other existing methods.