Tobacco Leaves Disease Identification and Spot Segmentation Based on the Improved ORB Algorithm

In order to improve the problems of poor accuracy and low efficiency in tobacco leaves disease recognition and diagnosis and avoid the misjudgment in tobacco disease recognition, a disease recognition and spot segmentation method based on the improved ORB algorithm was proposed. The improved FAST14-...

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Bibliographic Details
Published in:Scientific programming 2022-09, Vol.2022, p.1-12
Main Authors: Xu, Min, Li, Lihua, Cheng, Liangkun, Zhao, Haobin, Wu, Jiang, Wang, Xiaoqiang, Li, Hongchen, Liu, Jianjun
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Classification
Corn
Corner detection
Crop diseases
Economic development
Eigenvalues
Eye diseases
Feature extraction
Image retrieval
Image segmentation
Neighborhoods
Neural networks
Plant diseases
Recognition
Support vector machines
Tobacco
Tobacco industry
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Summary:In order to improve the problems of poor accuracy and low efficiency in tobacco leaves disease recognition and diagnosis and avoid the misjudgment in tobacco disease recognition, a disease recognition and spot segmentation method based on the improved ORB algorithm was proposed. The improved FAST14-24 algorithm was used to preliminarily extract corners. It overcame the deficiency of the sensitivity of the traditional ORB corner detection algorithm to image edges. During the experiment, 28 parameters were obtained through the extraction of color features, morphological features, texture, and other features of tobacco disease spots. Through the experimental comparisons, it was found that the fitness of the improved ORB algorithm was 96.68 and the cross-checking rate was 93.21%. The validation and recognition rate for samples was 96%. The identification rate of tobacco brown spot disease and frog eye disease was 92%, and the identification rate of 6 categories in different periods was over 96%. The experimental results verified the effects of the disease identification fully.
ISSN:1058-9244
1875-919X
DOI:10.1155/2022/4285045