@article {10.3844/ajbbsp.2024.94.104,
article_type = {journal},
title = {Research on Cotton Top Bud Target Detection Algorithm Based on Improved RetinaNet},
author = {Zhu, Jikui and Lin, Shijie and Zhang, Fengkui and Zhang, Ting and Zhao, Shijie and Li, Ping},
volume = {20},
number = {1},
year = {2024},
month = {Apr},
pages = {94-104},
doi = {10.3844/ajbbsp.2024.94.104},
url = {https://thescipub.com/abstract/ajbbsp.2024.94.104},
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 (mAP@0.5) 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 (mAP@0.5) 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.},
journal = {American Journal of Biochemistry and Biotechnology},
publisher = {Science Publications}
}