Study on Pear Flowers Detection Performance of YOLO-PEFL Model Trained With Synthetic Target Images

• Published in: Frontiers in plant science 2022-06, Vol.13, p.911473-911473
• Main Authors: Wang, Chenglin, Wang, Yawei, Liu, Suchwen, Lin, Guichao, He, Peng, Zhang, Zhaoguo, Zhou, Yi
• Format: Article
• Language: English
• Subjects: deep learning; pear flowers identification; Plant Science; target detection; yield estimation; YOLOv4
• ISSN: 1664-462X; 1664-462X
• DOI: 10.3389/fpls.2022.911473

Accurate detection of pear flowers is an important measure for pear orchard yield estimation, which plays a vital role in improving pear yield and predicting pear price trends. This study proposed an improved YOLOv4 model called YOLO-PEFL model for accurate pear flower detection in the natural environment. Pear flower targets were artificially synthesized with pear flower’s surface features. The synthetic pear flower targets and the backgrounds of the original pear flower images were used as the inputs of the YOLO-PEFL model. ShuffleNetv2 embedded by the SENet (Squeeze-and-Excitation Networks) module replacing the original backbone network of the YOLOv4 model formed the backbone of the YOLO-PEFL model. The parameters of the YOLO-PEFL model were fine-tuned to change the size of the initial anchor frame. The experimental results showed that the average precision of the YOLO-PEFL model was 96.71%, the model size was reduced by about 80%, and the average detection speed was 0.027s. Compared with the YOLOv4 model and the YOLOv4-tiny model, the YOLO-PEFL model had better performance in model size, detection accuracy, and detection speed, which effectively reduced the model deployment cost and improved the model efficiency. It implied the proposed YOLO-PEFL model could accurately detect pear flowers with high efficiency in the natural environment.