CASIA OpenIR
Apple detection during different growth stages in orchards using the improved YOLO-V3 model
Tian, Yunong; Yang, Guodong; Wang, Zhe; Wang, Hao; Li, En1; Liang, Zize
Source PublicationCOMPUTERS AND ELECTRONICS IN AGRICULTURE
ISSN0168-1699
2019-02-01
Volume157Pages:417-426
Corresponding AuthorLi, En(en.li@ia.ac.cn)
AbstractReal-time detection of apples in orchards is one of the most important methods for judging growth stages of apples and estimating yield. The size, colour, cluster density, and other growth characteristics of apples change as they grow. Traditional detection methods can only detect apples during a particular growth stage, but these methods cannot be adapted to different growth stages using the same model. We propose an improved YOLO-V3 model for detecting apples during different growth stages in orchards with fluctuating illumination, complex backgrounds, overlapping apples, and branches and leaves. Images of young apples, expanding apples, and ripe apples are initially collected. These images are subsequently augmented using rotation transformation, colour balance transformation, brightness transformation, and blur processing. The augmented images are used to create training sets. The DenseNet method is used to process feature layers with low resolution in the YOLO-V3 network. This effectively enhances feature propagation, promotes feature reuse, and improves network performance. After training the model, the performance of the trained model is tested on a test dataset. The test results show that the proposed YOLOV3-dense model is superior to the original YOLO-V3 model and the Faster R-CNN with VGG16 net model, which is the state-of-art fruit detection model. The average detection time of the model is 0.304 s per frame at 3000 x 3000 resolution, which can provide real-time detection of apples in orchards. Moreover, the YOLOV3-dense model can effectively provide apple detection under overlapping apples and occlusion conditions, and can be applied in the actual environment of orchards.
KeywordApple images acquisition Image augmentation Deep learning YOLOV3-dense Real-time detection
DOI10.1016/j.compag.2019.01.012
WOS KeywordVISION ; FRUITS
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 AreaAgriculture ; Computer Science
WOS SubjectAgriculture, Multidisciplinary ; Computer Science, Interdisciplinary Applications
WOS IDWOS:000459358400041
PublisherELSEVIER SCI LTD
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/25015
Collection中国科学院自动化研究所
Corresponding AuthorLi, En
Affiliation1.Chinese Acad Sci, Inst Automat, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, 19 A Yuquan Rd, Beijing 100049, Peoples R China
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Tian, Yunong,Yang, Guodong,Wang, Zhe,et al. Apple detection during different growth stages in orchards using the improved YOLO-V3 model[J]. COMPUTERS AND ELECTRONICS IN AGRICULTURE,2019,157:417-426.
APA Tian, Yunong,Yang, Guodong,Wang, Zhe,Wang, Hao,Li, En,&Liang, Zize.(2019).Apple detection during different growth stages in orchards using the improved YOLO-V3 model.COMPUTERS AND ELECTRONICS IN AGRICULTURE,157,417-426.
MLA Tian, Yunong,et al."Apple detection during different growth stages in orchards using the improved YOLO-V3 model".COMPUTERS AND ELECTRONICS IN AGRICULTURE 157(2019):417-426.
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