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A Robust Deep Learning Approach for the Quantitative Characterization and Clustering of Peach Tree Crowns Based on UAV Images

文献类型： 外文期刊

作者： Hu, Jun 1 ; Zhang, Yanfeng 2 ; Zhao, Dandan 1 ; Yang, Guijun 3 ; Chen, Feiyun 5 ; Zhou, Chengquan 6 ; Chen, Wenxuan 1 ;

作者机构： 1.Zhejiang Acad Agr Sci, Food Sci Inst, Hangzhou 310000, Peoples R China

2.Anhui Acad Agr Sci, Inst Soil & Fertilizer, Hefei 230041, Peoples R China

3.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

4.Beijing Acad Agr & Forestry Sci, Intelligent Equipment Res Ctr, Beijing 100097, Peoples R China

5.Anhui Univ, Sch Life Sci, Hefei 230601, Peoples R China

6.Zhejiang Acad Agr Sci, Inst Agr Equipment, Hangzhou 310000, Peoples R China

关键词： Vegetation; Autonomous aerial vehicles; Laser radar; Remote sensing; Agriculture; Volume measurement; Monitoring; Crown measurement; deep learning; shape clustering; unmanned aerial vehicle (UAV) images; volume estimation

期刊名称：IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING （ 影响因子：8.125； 五年影响因子：8.137 ）

ISSN： 0196-2892

年卷期： 2022 年 60 卷

页码：

收录情况： SCI

摘要： The accurate large-scale measurement of peach crowns is vital in horticultural science and the optimization of orchard management. Nowadays, numerous crown parameters (e.g., crown area, height, and volume) can be obtained via the analysis of point clouds or photographs. Current laser-based sensors provide the required reliable and accurate information; however, they are costly and time-consuming. Therefore, a simpler approach for crown measurement is required. For this purpose, this study presents a pipeline for the monitoring and clustering of 259 peach tree crowns based on unmanned aerial vehicle (UAV) images of a peach orchard in Southeast China. Considering the limitation that the original aerial image dataset contains little information, a data augmentation process is adopted, and an efficient deep learning architecture based on conditional generative adversarial networks (cGANs) was designed to extract the crown area. Then, the shape of the crown area was clustered using an edge detection process and a k-means algorithm. Finally, an ellipsoid volume method (EVM) was applied to estimate the crown volume. Five indicators-namely, Qseg, Sr, Precision, Recall, and F-measure-were employed to evaluate the crown extraction effects, and the average results for testing samples were 0.832, 0.847, 0.851, 0.828, and 0.846, respectively. Compared with other approaches-namely, fully convolutional network (FCN), U-Net, SegNet21, the excess green index (ExG), and the color index of vegetation extraction (CIVE)-the proposed cGAN model performs better, achieving an accuracy improvement of 5%-25%. For the estimation of crown volume, using measurements from a light detection and ranging (LIDAR) scanner as a reference, the correlation coefficient and relativeroot-mean-square error (R-RMSE) were found to be 0.836% and 14.93%, respectively. Overall, the results demonstrate that the proposed method is feasible for measuring peach tree crowns. The wide application of such technology would facilitate applied research in plant phenotyping and precision horticulture.

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