Authors: Tatsuya Yamazaki, Kensuke Nakazawa
Identifier: CSBE21792
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Published in: CSBE-SCGAB Technical Conferences » 5th CIGR and AGM Quebec City 2021 » World Congress on Computers in Agriculture and Natural Resources

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Description: Artificial pollination is a heavy-duty task in pear cultivation. This research aims to develop a method to detect pear flowers to be pollinated from camera images, which is planned to mount as a robot vision. Two types of images exist: a distant view image and a close-up view image. Faster R-CNN (Regions with Convolutional Neural Networks) is applied to the distant view images to detect flower clusters. In addition, branch information, that is extracted by a multi-scale filter, in the distant view image is used to improve the accuracy of detection. Then Faster R-CNN is also used for the close-up view images to extract flowers to be pollinated. The Faster R-CNN models are trained by using the pear flower image data collected in real pear fields. The proposed models overperformed the model without the branch information.

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Date: 2021-06-11
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Conference name: 5th CIGR International Conference and CSBE-SCGAB AGM 2021, Quebec City,QC, 11-14 May 2021.
Session name: World Congress on Computers in Agriculture and Natural Resources (WCCA 2)

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Publication type: Presentation
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Coverage: Japan
Language 1: en
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Rights: Canadian Society for Bioengineering
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