REASEARCH ON PEAR INFLORESCENCE RECOGNITION BASED ON FUSION ATTENTION MECHANISM WITH YOLOV5
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Published:2023-04-30
Issue:
Volume:
Page:11-20
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ISSN:2068-2239
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Container-title:INMATEH Agricultural Engineering
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language:en
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Short-container-title:INMATEH
Author:
XIA Ye1, LEI Xiaohui2, HERBST Andreas3, LYU Xiaolan2
Affiliation:
1. School of Agricultural Engineering, Jiangsu University, Zhenjiang/China, Institute of Agricultural Facilities and Equipment, Jiangsu Academy of Agricultural Sciences / Key Laboratory of Modern Horticultural Equipment, Ministry of Agriculture and Rural Affairs, Nanjing/China 2. Institute of Agricultural Facilities and Equipment, Jiangsu Academy of Agricultural Sciences / Key Laboratory of Modern Horticultural Equipment, Ministry of Agriculture and Rural Affairs, Nanjing/China 3. Institute for Chemical Application Technology of JKI, Braunschweig Messeweg/Germany
Abstract
Thinning is an important agronomic process in pear production, thus the detection of pear inflorescence is an important technology for intelligentization of blossom thinning. In this paper, images of buds and flowers were collected under different natural conditions for model training, and the images were augmented by data augmentation methods. Model training was performed based on the YOLOv5s network with coordinate attention mechanism added to the backbone network and compared with the native YOLOv5s, YOLOv3, SSD 300, and Faster-RCNN algorithms. The mAP, F1 score and recall of the algorithm reached 93.32%, 91.10%, and 91.99%. The model size only took up 14.1 MB, and the average detection time was 27 ms, which are suitable for application in actual intelligent blossom thinning equipment.
Publisher
INMA Bucharest-Romania
Subject
Industrial and Manufacturing Engineering,Mechanical Engineering,Food Science
Reference16 articles.
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