Research Article Open Access

Research on Cotton Top Bud Target Detection Algorithm Based on Improved RetinaNet

Jikui Zhu1, Shijie Lin1, Fengkui Zhang1,2,3, Ting Zhang1,4,3, Shijie Zhao1,4,3 and Ping Li1
  • 1 College of Mechanical and Electrical Engineering, Tarim University, Alar, Xinjiang, China
  • 2 Department of Xinjiang Uygur Autonomous Region, Modern Agricultural Engineering Key Laboratory at Universities of Education, Tarim University, Alar, Xinjiang, China
  • 3 Key Laboratory of Tarim Oasis Agriculture (Tarim University), Ministry of Education, Tarim University, Alar, Xinjiang, China
  • 4 Department of Xinjiang Uygur Autonomous Region, Modern Agricultural Engineering Key Laboratory at Universities of Education, Tarim University, Alar, Xinjiang, China

Abstract

To solve the problems of low accuracy and high miss rate in the recognition of cotton apical buds during mechanical topping, an enhanced method based on the RetinaNet network is proposed for the accurate identification of cotton apical buds under natural light. The traditional RetinaNet algorithm is validated to improve the recall rate and average accuracy of cotton apical bud recognition ([email protected]) at 83.61% and 77.64% respectively. Due to the shallow nature of the network, there is still overfitting and the RetinaNet algorithm is improved. This algorithm incorporates R-CBAM and ShuffleViT Block network modules and uses Atrous Spatial Pyramid Pooling (ASPP) to connect the cross-domain feature layer to the feature fusion layer. The results indicate thatcompared with the traditional RetinaNet algorithm, theimprovedRetinaNet algorithm has an average accuracy ([email protected]) of 96.25% and a recall rate of 91.10% for cotton apical bud recognition. This indicates that the improved RetinaNet algorithm has optimal recognition performance and high recognition accuracy for cotton apical buds, laying a solid foundation for precise topping operations in cotton cultivation.

American Journal of Biochemistry and Biotechnology
Volume 20 No. 1, 2024, 94-104

DOI: https://doi.org/10.3844/ajbbsp.2024.94.104

Submitted On: 27 November 2023 Published On: 17 April 2024

How to Cite: Zhu, J., Lin, S., Zhang, F., Zhang, T., Zhao, S. & Li, P. (2024). Research on Cotton Top Bud Target Detection Algorithm Based on Improved RetinaNet. American Journal of Biochemistry and Biotechnology, 20(1), 94-104. https://doi.org/10.3844/ajbbsp.2024.94.104

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Keywords

  • Cotton Apical Bud
  • RetinaNet
  • Target Detection