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Morphological estimation of primary branch length of individual apple trees during the deciduous period in modern orchard based on PointNet plus

文献类型： 外文期刊

作者： Sun, Xiaoming 1 ; He, Leilei 1 ; Jiang, Hanhui 1 ; Li, Rui 1 ; Mao, Wulan 1 ; Zhang, Dong 6 ; Majeed, Yaqoob 7 ; Andriyanov, Nikita 8 ; Soloviev, Vladimir 8 ; Fu, Longsheng 1 ;

作者机构： 1.Northwest A&F Univ, Coll Mech & Elect Engn, Yangling 712100, Shaanxi, Peoples R China

2.Minist Agr & Rural Affairs, Key Lab Agr Internet Things, Yangling 712100, Shaanxi, Peoples R China

3.Shaanxi Key Lab Agr Informat Percept & Intelligen, Yangling 712100, Shaanxi, Peoples R China

4.Northwest A&F Univ, Shenzhen Res Inst, Shenzhen 518000, Guangdong, Peoples R China

5.Xinjiang Acad Agr Sci, Inst Agr Mechanizat, Urumqi 830000, Peoples R China

6.Northwest A&F Univ, Coll Hort, Yangling 712100, Shaanxi, Peoples R China

7.Univ Agr Faisalabad, Fac Agr Engn & Technol, Faisalabad 38000, Pakistan

8.Financial Univ Govt Russian Federat, Dept Data Anal & Machine Learning, Moscow 125167, Russia

关键词： Morphological trait; Primary branch instance; Point cloud; Deep learning; Part segmentation

期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE （ 影响因子：8.3； 五年影响因子：8.3 ）

ISSN： 0168-1699

年卷期： 2024 年 220 卷

页码：

收录情况： SCI

摘要： Primary branch length is an important morphological trait of individual apple tree phenotypes. This study presents a novel method for estimating the primary branch lengths of individual apple trees during the deciduous period by distinguishing their instances, i.e., merging those belonging to the same primary branch based on part segmentation outputs of PointNet++. Firstly, colored and colorless 3D-datasets were prepared for training PointNet++ models. The model with higher overall accuracy (OA), class average accuracy (CAA), and mean intersection-over-union (mIoU) was employed to segment the point cloud of a tree into primary branches (PB), trunk (TK), and end-points of primary branches (EPB) of individual apple trees. Skeletonization was applied to the outputs of the three parts of individual apple trees. Subsequently, each primary branch instance was distinguished by determining its corresponding path and retaining the longest path of the same primary branch only. Finally, the primary branch length was estimated by calculating the sum of Euclidean distances between adjacent points on the corresponding path. Results indicated that adding color to point clouds did not improve segmentation accuracy of PointNet++ on segmenting PB, TK, and EPB with similar color features. The PointNet++ model that was trained without color achieved an OA, CAA, and mIoU of 0.84, 0.83, and 0.70, respectively. The proportion of estimated and ground-truth values of the number of primary branches was 93.64 %. The mean absolute percentage error of estimating primary branch lengths was 12.00 %. These findings demonstrate that the proposed method is promising for high-throughput phenotyping of apple trees.

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