| Abstrakt: |
One of the main risks to food security is the plant diseases, but because of the absence of needed infrastructure and actual noise, scientists are faced with a difficult in detection plant diseases in the real image without de-noisy process. The proposed solution in this paper is based on Ontology to support semantic segmentation. Where, the semantic segmentation divides images into non-overlapped regions, with specified semantic labels allocated. The QPSO (quantum particle swarm optimization) algorithm has been used in segmentation of an original noisy image. The proposed method outperforms state-of-the-art algorithms in terms of global accuracy. The proposed method achieves 88% which is superior to other approaches, likely because of the consistency of the semantic implementation, weighting of features and the implementation of data and knowledge required. Our results show that a classification based on the proposed method is better than the state-of-the-art algorithms. The proposed method is evaluated, with 49,563 images from healthy and diseased plant leaves, 12 plant species were identified and 22 diseases. The classification accuracy of the proposed method is 86.2%, showing that the strategy is appropriate. We enhanced PDO (Plant Disease Ontology) to be EPDO (Enhance Plant Disease Ontology). The segmented noisy image elements are paired with EPDO with derived features that come from QPSO. The proposed method also saves time of de-noisy process and effort for removing the noise at a noise level from the input image σ =70. [ABSTRACT FROM AUTHOR] |
