Fast and precise detection of litchi fruits for yield estimation based on the improved YOLOv5 model

Lele Wang; Yingjie Zhao; Zhangjun Xiong; Shizhou Wang; Yuanhong Li; Yubin Lan

doi:10.3389/fpls.2022.965425

Fast and precise detection of litchi fruits for yield estimation based on the improved YOLOv5 model

Front Plant Sci. 2022 Aug 9:13:965425. doi: 10.3389/fpls.2022.965425. eCollection 2022.

Authors

Lele Wang^{1

2}, Yingjie Zhao^{1

2}, Zhangjun Xiong^{1

2}, Shizhou Wang^{2

3}, Yuanhong Li^{1

2}, Yubin Lan^{1

4

2

3

5}

Affiliations

¹ College of Electronic Engineering, College of Artificial Intelligence, South China Agricultural University, Guangzhou, China.
² National Center for International Collaboration Research on Precision Agricultural Aviation Pesticides Spraying Technology, Guangzhou, China.
³ School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, China.
⁴ Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China.
⁵ Department of Biological and Agricultural Engineering, Texas A&M University, College Station, TX, United States.

Abstract

The fast and precise detection of dense litchi fruits and the determination of their maturity is of great practical significance for yield estimation in litchi orchards and robot harvesting. Factors such as complex growth environment, dense distribution, and random occlusion by leaves, branches, and other litchi fruits easily cause the predicted output based on computer vision deviate from the actual value. This study proposed a fast and precise litchi fruit detection method and application software based on an improved You Only Look Once version 5 (YOLOv5) model, which can be used for the detection and yield estimation of litchi in orchards. First, a dataset of litchi with different maturity levels was established. Second, the YOLOv5s model was chosen as a base version of the improved model. ShuffleNet v2 was used as the improved backbone network, and then the backbone network was fine-tuned to simplify the model structure. In the feature fusion stage, the CBAM module was introduced to further refine litchi's effective feature information. Considering the characteristics of the small size of dense litchi fruits, the 1,280 × 1,280 was used as the improved model input size while we optimized the network structure. To evaluate the performance of the proposed method, we performed ablation experiments and compared it with other models on the test set. The results showed that the improved model's mean average precision (mAP) presented a 3.5% improvement and 62.77% compression in model size compared with the original model. The improved model size is 5.1 MB, and the frame per second (FPS) is 78.13 frames/s at a confidence of 0.5. The model performs well in precision and robustness in different scenarios. In addition, we developed an Android application for litchi counting and yield estimation based on the improved model. It is known from the experiment that the correlation coefficient R ² between the application test and the actual results was 0.9879. In summary, our improved method achieves high precision, lightweight, and fast detection performance at large scales. The method can provide technical means for portable yield estimation and visual recognition of litchi harvesting robots.

Keywords: ShuffleNet v2; YOLOv5; litchi; object detection; yield estimation.