A novel approach for estimating the flowering rate of litchi based on deep learning and UAV images

Peiyi Lin; Denghui Li; Yuhang Jia; Yingyi Chen; Guangwen Huang; Hamza Elkhouchlaa; Zhongwei Yao; Zhengqi Zhou; Haobo Zhou; Jun Li; Huazhong Lu

doi:10.3389/fpls.2022.966639

A novel approach for estimating the flowering rate of litchi based on deep learning and UAV images

Front Plant Sci. 2022 Aug 25:13:966639. doi: 10.3389/fpls.2022.966639. eCollection 2022.

Authors

Peiyi Lin¹, Denghui Li¹, Yuhang Jia¹, Yingyi Chen¹, Guangwen Huang¹, Hamza Elkhouchlaa¹, Zhongwei Yao¹, Zhengqi Zhou¹, Haobo Zhou¹, Jun Li^{1

2}, Huazhong Lu³

Affiliations

¹ College of Engineering, South China Agricultural University, Guangzhou, China.
² Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou, China.
³ Guangdong Academy of Agricultural Sciences, Guangzhou, China.

Abstract

Litchi flowering management is an important link in litchi orchard management. Statistical litchi flowering rate data can provide an important reference for regulating the number of litchi flowers and directly determining the quality and yield of litchi fruit. At present, the statistical work regarding litchi flowering rates requires considerable labour costs. Therefore, this study aims at the statistical litchi flowering rate task, and a combination of unmanned aerial vehicle (UAV) images and computer vision technology is proposed to count the numbers of litchi flower clusters and flushes in a complex natural environment to improve the efficiency of litchi flowering rate estimation. First, RGB images of litchi canopies at the flowering stage are collected by a UAV. After performing image preprocessing, a dataset is established, and two types of objects in the images, namely, flower clusters and flushes, are manually labelled. Second, by comparing the pretraining and testing results obtained when setting different training parameters for the YOLOv4 model, the optimal parameter combination is determined. The YOLOv4 model trained with the optimal combination of parameters tests best on the test set, at which time the mean average precision (mAP) is 87.87%. The detection time required for a single image is 0.043 s. Finally, aiming at the two kinds of targets (flower clusters and flushes) on 8 litchi trees in a real orchard, a model for estimating the numbers of flower clusters and flushes on a single litchi tree is constructed by matching the identified number of targets with the actual number of targets via equation fitting. Then, the data obtained from the manual counting process and the estimation model for the other five litchi trees in the real orchard are statistically analysed. The average error rate for the number of flower clusters is 4.20%, the average error rate for the number of flushes is 2.85%, and the average error for the flowering rate is 1.135%. The experimental results show that the proposed method is effective for estimating the litchi flowering rate and can provide guidance regarding the management of the flowering periods of litchi orchards.

Keywords: UAV images; convolutional neural network; image analysis; litchi flowering rate; object detection.