Tea buds recognition under complex scenes based on optimized YOLOV3 model

doi:10.3969/j.issn.1004-1524.2021.09.18

Acta Agriculturae Zhejiangensis ›› 2021, Vol. 33 ›› Issue (9): 1740-1747.DOI: 10.3969/j.issn.1004-1524.2021.09.18

• Biosystems Engineering • Previous Articles Next Articles

Tea buds recognition under complex scenes based on optimized YOLOV3 model

ZHANG Qingqing¹(), LIU Lianzhong¹^,^*(), NING Jingming²^,³, WU Guodong¹, JIANG Zhaohui¹, LI Mengjie¹, LI Dongliang¹

1. College of Information and Computer Science, Anhui Agricultural University, Hefei 230036, China
2. College of Tea and Food Science and Technology, Anhui Agricultural University, Hefei 230036, China
3. State Key Laboratory of Tea Plant Biology and Resource Utilization, Hefei 230036, China

Received:2020-07-18 Online:2021-09-25 Published:2021-10-09
Contact: LIU Lianzhong

Abstract

Abstract:

Tea buds recognition is one of the key technologies for intelligent tea buds picking, and the size of buds, environmental lighting, imaging angle and other factors will bring difficulties for precise buds identification. In order to solve the problem of low accuracy of traditional tea buds recognition in complex scenes, a tea buds recognition method based on YOLOV3 deep convolution model was proposed. By adding a SPP module into the YOLOV3 model, an optimized YOLOV3 model was designed to further improve the recognition ability of tea buds. The results showed that YOLOV3 and optimized YOLOV3 models could both realize tea buds recognition in complex scenes, and the average accuracy mean (mAP) of the optimized YOLOV3 model reached 91%, which was 3.5 percentage points higher than YOLOV3 model, indicating the optimized YOLOV3 could be well applied to the tea buds identification in natural environments.

Key words: deep learning, convolutional neural network, tea buds recognition

CLC Number:

S571.1
S24

ZHANG Qingqing, LIU Lianzhong, NING Jingming, WU Guodong, JIANG Zhaohui, LI Mengjie, LI Dongliang. Tea buds recognition under complex scenes based on optimized YOLOV3 model[J]. Acta Agriculturae Zhejiangensis, 2021, 33(9): 1740-1747.

Figures/Tables 9

Fig.1 Collection, cropping and marking of tea tree images a,Original tea tree image;b,Cropped tea tree image;c,Tea bud marking.

Fig.2 Overall flow chart of experiment

Fig.3 Image pre-processing a.Before pretreatment;b.After pretreatment.

Fig.4 The network structure of optimized YOLOV3 model

Fig.5 The mAP values of the two models change curve with the training times

Table 1 Performance comparison of the two models

模型种类 Model type	平均精度均值mAP Mean average precision/%	召回率 The recall rate/%	平均检测时间 Average detection time/s
YOLOV3模型YOLOV3 model	87.5	71	0.375 5
YOLOV3优化模型Optimized YOLOV3 model	91.0	75	0.387 2

Fig.6 The recognition results of the two models with different number of buds

Fig.7 The recognition results of the two models under different lighting conditions

Fig.8 Recognition results of the two models from different shooting angles

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Tea buds recognition under complex scenes based on optimized YOLOV3 model

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