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Navigation line extraction algorithm for corn spraying robot based on Light-YOLOv8s network
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  • Zhihua Diao,
  • Peiliang Guo,
  • Chunjiang Zhao,
  • Jiangbo Li,
  • Ruirui Zhang,
  • Ranbing Yang,
  • Shushuai Ma,
  • Zhendong He,
  • Suna Zhao,
  • Baohua Zhang
Zhihua Diao
Zhengzhou University of Light Industry

Corresponding Author:[email protected]

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Peiliang Guo
Zhengzhou University of Light Industry
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Chunjiang Zhao
Beijing Academy of Agriculture and Forestry Sciences
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Jiangbo Li
Beijing Academy of Agriculture and Forestry Sciences
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Ruirui Zhang
Beijing Academy of Agriculture and Forestry Sciences
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Ranbing Yang
Hainan University
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Shushuai Ma
Zhengzhou University of Light Industry
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Zhendong He
Zhengzhou University of Light Industry
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Suna Zhao
Zhengzhou University of Light Industry
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Baohua Zhang
Nanjing Agricultural University
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Abstract

The continuous and close combination of artificial intelligence technology and agriculture promotes the rapid development of smart agriculture, among which the agricultural robot navigation line recognition algorithm based on deep learning has achieved great success in detection accuracy and detection speed. However, there are still many problems, such as the large size of the algorithm is difficult to deploy in hardware equipment, and the accuracy and speed of crop row detection in real farmland environment are low. In order to solve the above problems, this paper proposed a navigation line extraction algorithm for corn spraying robot based on Light-YOLOv8s network. Firstly, the Convolution (Conv) module and C2f module of YOLOv8s network are replaced with Depthwise Convolution (DWConv) module and PP-LCNet module respectively to reduce the parameters (Params) and giga floating-point operations per second (GFLOPs) of the network, so as to achieve the purpose of network lightweight. Secondly, in order to reduce the precision loss caused by network lightweight, the spatial pyramid pooling fast (SPPF) module in the backbone network is changed to atrous spatial pyramid pooling faster (ASPPF) module to improve the accuracy of network feature extraction. Meanwhile, normalization-based attention module (NAM) is introduced into the network to improve the network’s attention to corn plants. Then the corn plant was located by using the midpoint of the corn plant detection box. Finally, the least square method is used to extract the maize crop row line, and the middle line of the maize crop row line is the navigation line of the maize spraying robot. According to the experimental results, the Params of Light-YOLOv8s network decreased by 29.24%, 86.64% and 55.38%, respectively, compared with YOLOv5s network, YOLOv7 network and YOLOv8s network. GFLOPs dropped 26.79%, 88.77%, and 58.74%, respectively, while accuracy lost only 1%, 0.6%, and 2.2%. It shows that the Light-YOLOv8s network proposed in this paper greatly reduces the size of the model, solves the problems such as the difficulty of deployment caused by the large size of the existing algorithm, and also greatly reduces the accuracy loss of the model, and solves the problems such as the reduced accuracy of the algorithm caused by the lightweight network. When the corn spraying robot works in the real farmland environment, the navigation line extraction algorithm proposed in this paper not only ensures the real-time navigation of the corn spraying robot, but also ensures the accuracy of the navigation, and makes a contribution to the development of agricultural robot visual navigation technology.