Citrus Huanglongbing Recognition Algorithm Based on CKMOPSO

Hui Wang, Tie Cai, Wei Cao

OAI: oai:igi-global.com:282181 • DOI: 10.4018/IJCINI.20211001.oa10

In view of the similarity of characteristics between the features of the disease images and the large dimension, and the features correlation of the disease images, this will lead to the generation of feature redundancy, and will introduce a serious impact on the recognition efficiency and accuracy of citrus Huanglongbing. In addition, they have the defects of high cost of detection algorithms and low detection accuracy. This will occur in the image cutting feature extraction stage, so this paper uses the citrus Huanglongbing recognition algorithm based on kriging model simplex crossover local based search Multi-objective particle swarm optimization algorithm(CKMOPSO) selects feature vectors with strong classification capabilities from the original disease image features, experimental results show that this is an effective recognition method.