Hybrid Between Ontology and Quantum Particle Swarm Optimization for Segmenting Noisy Plant Disease Image

Abstract

One of the main risks to food security is plant diseases, but because of the absence of needed infrastructure and actual noise, scientists are faced with a difficult issue. Semantic segmentation of images divides images into non-overlapped regions, with specified semantic labels allocated. In this paper, The QPSO (quantum particle swarm optimization) algorithm has been used in segmentation of an original noisy image and Ontology has been used in classification the segmented image. Input noisy image segmentation is limited to a classification phase in which the object is transferred to Ontology. With 49,563 images from healthy and diseased plant leaves, 12 plant species were identified and 22 diseases, the proposed method is evaluated. The method proposed produces an accuracy of 86.22 percent for a stopped test set, showing that the strategy is appropriate. EPDO (Enhance Plant Disease Ontology) is built with the web ontology language (OWL). The segmented noisy image elements are paired with EPDO with derived features that come from QPSO. Our results show that a classification based on the suggested method is better than the state-of-the-art algorithms. The proposed method also saves time and effort for removing the noise at noise level from the input image σ=70