Abstract
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.
Highlights
Citrus fruits are one of the most popular fruits in the world, and they are an important part of the agricultural economy
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) with strong classification capabilities from the original disease image features to select feature vectors
This effect will occur in the image cutting feature extraction stage, so this paper uses the citrus Huanglongbing recognition algorithm based on CKMOPSO, which uses CKMOPSO to automatically select feature vectors with strong classification capabilities from the original disease image features for use in Huanglongbing image recognition, experimental results show that this is an effective recognition method
Summary
Citrus fruits are one of the most popular fruits in the world, and they are an important part of the agricultural economy. In view of the similarity of characteristics between the characteristics of the disease images and the large dimension, especially the characteristics of the disease images may be related, resulting in the generation of feature redundancy, which has a serious impact on the efficiency and accuracy of citrus Huanglongbing e image recognition They have the defects of high cost of detection algorithms and low detection accuracy. This effect will occur in the image cutting feature extraction stage, so this paper uses the citrus Huanglongbing recognition algorithm based on CKMOPSO, which uses CKMOPSO to automatically select feature vectors with strong classification capabilities from the original disease image features for use in Huanglongbing image recognition, experimental results show that this is an effective recognition method
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