CASIA OpenIR
Detection of Apple Lesions in Orchards Based on Deep Learning Methods of CycleGAN and YOLOV3-Dense
Tian, Yunong1,2; Yang, Guodong1,2; Wang, Zhe1,2; Li, En1,2; Liang, Zize1,2
Source PublicationJOURNAL OF SENSORS
ISSN1687-725X
2019
Pages13
Corresponding AuthorLi, En(en.li@ia.ac.cn)
AbstractPlant disease is one of the primary causes of crop yield reduction. With the development of computer vision and deep learning technology, autonomous detection of plant surface lesion images collected by optical sensors has become an important research direction for timely crop disease diagnosis. In this paper, an anthracnose lesion detection method based on deep learning is proposed. Firstly, for the problem of insufficient image data caused by the random occurrence of apple diseases, in addition to traditional image augmentation techniques, Cycle-Consistent Adversarial Network (CycleGAN) deep learning model is used in this paper to accomplish data augmentation. These methods effectively enrich the diversity of training data and provide a solid foundation for training the detection model. In this paper, on the basis of image data augmentation, densely connected neural network (DenseNet) is utilized to optimize feature layers of the YOLO-V3 model which have lower resolution. DenseNet greatly improves the utilization of features in the neural network and enhances the detection result of the YOLO-V3 model. It is verified in experiments that the improved model exceeds Faster R-CNN with VGG16 NET, the original YOLO-V3 model, and other three state-of-the-art networks in detection performance, and it can realize real-time detection. The proposed method can be well applied to the detection of anthracnose lesions on apple surfaces in orchards.
DOI10.1155/2019/7630926
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2017YFD0701401]
Funding OrganizationNational Key Research and Development Program of China
WOS Research AreaEngineering ; Instruments & Instrumentation
WOS SubjectEngineering, Electrical & Electronic ; Instruments & Instrumentation
WOS IDWOS:000465310100001
PublisherHINDAWI LTD
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/24907
Collection中国科学院自动化研究所
Corresponding AuthorLi, En
Affiliation1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, 19A Yuquan Rd, Beijing 100049, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Tian, Yunong,Yang, Guodong,Wang, Zhe,et al. Detection of Apple Lesions in Orchards Based on Deep Learning Methods of CycleGAN and YOLOV3-Dense[J]. JOURNAL OF SENSORS,2019:13.
APA Tian, Yunong,Yang, Guodong,Wang, Zhe,Li, En,&Liang, Zize.(2019).Detection of Apple Lesions in Orchards Based on Deep Learning Methods of CycleGAN and YOLOV3-Dense.JOURNAL OF SENSORS,13.
MLA Tian, Yunong,et al."Detection of Apple Lesions in Orchards Based on Deep Learning Methods of CycleGAN and YOLOV3-Dense".JOURNAL OF SENSORS (2019):13.
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