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A Neural Network Structure with Attention Mechanism and Additional Feature Fusion Layer for Tomato Flowering Phase Detection in Pollination Robots

文献类型： 外文期刊

作者： Xu, Tongyu 1 ; Qi, Xiangyu 1 ; Lin, Sen 2 ; Zhang, Yunhe 2 ; Ge, Yuhao 2 ; Li, Zuolin 2 ; Dong, Jing 3 ; Yang, Xin 1 ;

作者机构： 1.Shenyang Agr Univ, Coll Informat & Elect Engn, Shenyang 110866, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Res Ctr Intelligent Equipment, Beijing 100097, Peoples R China

3.Nongxin Sci & Technol Beijing Co Ltd, Beijing 100097, Peoples R China

关键词： robot technology; pollination robot; tomato flowering phase detection; attention mechanism; deep learning method

期刊名称：MACHINES （ 影响因子：2.899； 五年影响因子：3.09 ）

ISSN：

年卷期： 2022 年 10 卷 11 期

页码：

收录情况： SCI

摘要： In recent years, convolutional neural networks have made many advances in the field of computer vision. In smart greenhouses, using robots based on computer vision technology to pollinate flowers is one of the main methods of pollination. However, due to the complex lighting environment and the influence of leaf shadow in the greenhouse, it is difficult for the existing object detection algorithms to have high recall rate and accuracy. Based on this problem, from the perspective of application, we proposed a Yolov5s-based tomato flowering stage detection method named FlowerYolov5, which can well identify the bud phase, blooming phase and first fruit phase of tomato flowers. Firstly, in order to reduce the loss of tomato flower feature information in convolution and to strengthen the feature extraction of the target, FlowerYolov5 adds a new feature fusion layer. Then, in order to highlight the information of the object, the Convolutional Block Attention module (CBAM) is added to the backbone layer of FlowerYolov5. In the constructed tomato flower dataset, compared with YOLOv5s, the mAP of FlowerYolov5 increased by 7.8% (94.2%), and the F-1 score of FlowerYolov5 increased by 6.6% (89.9%). It was found that the overall parameter of FlowerYolov5 was 23.9 Mbyte, thus achieving a good balance between model parameter size and recognition accuracy. The experimental results show that the FlowerYolov5 has good robustness and more accurate precision. At the same time, the recall rate has also been greatly improved. The prediction results of the proposed algorithm can provide more accurate flower positioning for the pollination robot and improve its economic benefits.

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