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An image processing method for green apple lesion detection in natural environment based on GA-BPNN and SVM
- Source :
- 2018 IEEE International Conference on Mechatronics and Automation (ICMA).
- Publication Year :
- 2018
- Publisher :
- IEEE, 2018.
-
Abstract
- The technologies of information processing, especially the image processing, are important technical means to realize intelligent management and control of modern orchard. In order to realize the intelligentization of orchard production, the orchard image database, especially the lesion image database needs to be set up. Because of the uneven illumination and the complex background under the natural light condition, traditional image segmentation methods cannot solve the adaptive threshold issue during the lesion image processing of green apples. This paper proposes an image processing method based on a BP neural network updated by genetic algorithm (GA-BPNN) and support vector machine (SVM) to realize lesion image processing of green apples in orchard. With this method, apple images can be processed in batch and the lesion image database can be consummated automatically. Furthermore, the recognition of the diseased apple is realized. The experimental results show that the proposed method can obtain green apple segmentation and lesion detection with good efficiency and robustness in complex natural orchard environment.
- Subjects :
- 0106 biological sciences
Artificial neural network
Computer science
business.industry
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Information processing
Pattern recognition
Image processing
04 agricultural and veterinary sciences
Image segmentation
01 natural sciences
Support vector machine
Robustness (computer science)
Genetic algorithm
040103 agronomy & agriculture
0401 agriculture, forestry, and fisheries
Segmentation
Artificial intelligence
business
010606 plant biology & botany
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2018 IEEE International Conference on Mechatronics and Automation (ICMA)
- Accession number :
- edsair.doi...........8d0f09fa8e7e002fbf6c9a7a9aa3ab71