Robust Cherry Tomatoes Detection Algorithm in Greenhouse Scene Based on SSD

Ting Yuan, Lin Lv, Fan Zhang, Jun Fu, Jin Gao, Junxiong Zhang, Wei Li, Chunlong Zhang and Wenqiang Zhang
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Ting Yuan: College of Engineering, China Agricultural University, Qinghua a Rd.(E)No.17, Haidian District, Beijing 100083, China
Lin Lv: College of Engineering, China Agricultural University, Qinghua a Rd.(E)No.17, Haidian District, Beijing 100083, China
Fan Zhang: College of Engineering, China Agricultural University, Qinghua a Rd.(E)No.17, Haidian District, Beijing 100083, China
Jun Fu: College of Engineering, China Agricultural University, Qinghua a Rd.(E)No.17, Haidian District, Beijing 100083, China
Jin Gao: College of Engineering, China Agricultural University, Qinghua a Rd.(E)No.17, Haidian District, Beijing 100083, China
Junxiong Zhang: College of Engineering, China Agricultural University, Qinghua a Rd.(E)No.17, Haidian District, Beijing 100083, China
Wei Li: College of Engineering, China Agricultural University, Qinghua a Rd.(E)No.17, Haidian District, Beijing 100083, China
Chunlong Zhang: College of Engineering, China Agricultural University, Qinghua a Rd.(E)No.17, Haidian District, Beijing 100083, China
Wenqiang Zhang: College of Engineering, China Agricultural University, Qinghua a Rd.(E)No.17, Haidian District, Beijing 100083, China

Agriculture, 2020, vol. 10, issue 5, 1-14

Abstract: The detection of cherry tomatoes in greenhouse scene is of great significance for robotic harvesting. This paper states a method based on deep learning for cherry tomatoes detection to reduce the influence of illumination, growth difference, and occlusion. In view of such greenhouse operating environment and accuracy of deep learning, Single Shot multi-box Detector (SSD) was selected because of its excellent anti-interference ability and self-taught from datasets. The first step is to build datasets containing various conditions in greenhouse. According to the characteristics of cherry tomatoes, the image samples with illumination change, images rotation and noise enhancement were used to expand the datasets. Then training datasets were used to train and construct network model. To study the effect of base network and the input size of networks, one contrast experiment was designed on different base networks of VGG16, MobileNet, Inception V2 networks, and the other contrast experiment was conducted on changing the network input image size of 300 pixels by 300 pixels, 512 pixels by 512 pixels. Through the analysis of the experimental results, it is found that the Inception V2 network is the best base network with the average precision of 98.85% in greenhouse environment. Compared with other detection methods, this method shows substantial improvement in cherry tomatoes detection.

Keywords: cherry tomatoes; deep learning; SSD; robotic harvesting (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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