Welcome to Smart Agriculture 中文

Smart Agriculture ›› 2022, Vol. 4 ›› Issue (3): 108-119.

• Special Issue--Key Technologies and Equipment for Smart Orchard •

### Detection of Pear Inflorescence Based on Improved Ghost-YOLOv5s-BiFPN Algorithm

XIA Ye1,2(), LEI Xiaohui1, QI Yannan1, XU Tao1, YUAN Quanchun1, PAN Jian1, JIANG Saike1, LYU Xiaolan1()

1. 1.Institute of Agricultural Facilities and Equipment, Jiangsu Academy of Agricultural Sciences / Key Laboratory of Modern Horticultural Equipment, Ministry of Agriculture and Rural Affairs, Nanjing 210014, China
2.Institute of Agricultural Engineering Jiangsu University, Zhenjiang 210200, China
• Received:2022-07-14 Online:2022-09-30 Published:2022-11-23
• corresponding author: LYU Xiaolan E-mail:misakuu@126.com;lxlanny@126.com

Abstract:

Mechanized and intelligent flower thinning is a high-speed flower thinning method nowadays. The classification and detection of flowers and flower buds are the basic requirements to ensure the normal operation of the flower thinning machine. Aiming at the problems of pear inflorescence detection and classification in the current intelligent production of pear orchards, a Y-shaped shed pear orchard inflorescence recognition algorithm Ghost-YOLOv5s-BiFPN based on improved YOLOv5s was proposed in this research. The detection model was obtained by labeling and expanding the pear tree bud and flower images collected in the field and sending them to the algorithm for training. The Ghost-YOLOv5s-BiFPN algorithm used the weighted bidirectional feature pyramid network to replace the original path aggregation network structure, and effectively fuse the features of different sizes. At the same time, ghost module was used to replace the traditional convolution, so as to reduce the amount of model parameters and improve the operation efficiency of the equipment without reducing the accuracy. The field experiment results showed that the detection accuracy of the Ghost-YOLOv5s-BiFPN algorithm for the bud and flower in the pear inflorescence were 93.21% and 89.43%, respectively, with an average accuracy of 91.32%, and the detection time of a single image was 29 ms. Compared with the original YOLOv5s algorithm, the detection accuracy was improved by 4.18%, and the detection time and model parameters were reduced by 9 ms and 46.63% respectively. Compared with the original YOLOV5s network, the mAP and recall rate were improved by 4.2% and 2.7%, respectively; the number of parameters, model size and floating point operations were reduced by 46.6%, 44.4% and 47.5% respectively, and the average detection time was shortened by 9 ms. With Ghost convolution and BIFPN adding model, the detection accuracy has been improved to a certain extent, and the model has been greatly lightweight, effectively improving the detect efficiency. From the thermodynamic diagram results, it can be seen that BIFPN structure effectively enhances the representation ability of features, making the model more effective in focusing on the corresponding features of the target. The results showed that the algorithm can meet the requirements of accurate identification and classification of pear buds and flowers, and provide technical support for the follow-up pear garden to achieve intelligent flower thinning.

CLC Number: