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An image processing method for green apple lesion detection in natural environment based on GA-BPNN and SVM
Tian YN(田雨农)
发表期刊2018 IEEE International Conference on Mechatronics and Automation
2018-08
期号2018页码:1210-1215
摘要

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.

关键词GA-BPNN ROI SVM
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/46589
专题复杂系统认知与决策实验室_先进机器人
推荐引用方式
GB/T 7714
Tian YN. An image processing method for green apple lesion detection in natural environment based on GA-BPNN and SVM[J]. 2018 IEEE International Conference on Mechatronics and Automation,2018(2018):1210-1215.
APA Tian YN.(2018).An image processing method for green apple lesion detection in natural environment based on GA-BPNN and SVM.2018 IEEE International Conference on Mechatronics and Automation(2018),1210-1215.
MLA Tian YN."An image processing method for green apple lesion detection in natural environment based on GA-BPNN and SVM".2018 IEEE International Conference on Mechatronics and Automation .2018(2018):1210-1215.
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