An automatic method for counting wheat tiller number in the field with terrestrial LiDAR
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Plant Methods Open Access
RESEARCH
An automatic method for counting wheat tiller number in the field with terrestrial LiDAR Yuan Fang1†, Xiaolei Qiu1†, Tai Guo1, Yongqing Wang1, Tao Cheng1, Yan Zhu1, Qi Chen2, Weixing Cao1, Xia Yao1*, Qingsong Niu3, Yongqiang Hu3 and Lijuan Gui3
Abstract Background: The tiller number per unit area is one of the main agronomic components in determining yield. A real-time assessment of this trait could contribute to monitoring the growth of wheat populations or as a primary phenotyping indicator for the screening of cultivars for crop breeding. However, determining tiller number has been conventionally dependent on tedious and labor-intensive manual counting. In this study, an automatic tiller-counting algorithm was developed to estimate the tiller density under field conditions based on terrestrial laser scanning (TLS) data. The novel algorithm, which is named ALHC, involves two steps: (1) the use of an adaptive layering (AL) algorithm for cluster segmentation and (2) the use of a hierarchical clustering (HC) algorithm for tiller detection among the clusters. Three field trials during the 2016–2018 wheat seasons were conducted to validate the algorithm with twenty different wheat cultivars, three nitrogen levels, and two planting densities at two ecological sites (Rugao & Xuzhou) in Jiangsu Province, China. Result: The results demonstrated that the algorithm was promising across different cultivars, years, growth stages, planting densities, and ecological sites. The tests from Rugao and Xuzhou in 2016–2017 and Rugao in 2017–2018 showed that the algorithm estimated the tiller number of the wheat with regression coefficient ( R2) values of 0.61, 0.56 and 0.65, respectively. In short, tiller counting with the ALHC generally underestimated the tiller number and performed better for the data with lower plant densities, compact plant types and the jointing stage, which were associated with overlap and noise between plants and inside the dense canopy. Conclusions: Differing from the previous methods, the ALHC proposed in this paper made full use of 3D crop information and developed an automatic tiller counting method that is suitable for the field environment. Keywords: Terrestrial laser scanning (TLS), Tiller number, Adaptive layering (AL) algorithm, Hierarchical clustering (HC) algorithm, Automatic method, Wheat Background Wheat is one of the primary food crops, feeding more than half of the world’s population [1, 2]. With a predicted *Correspondence: [email protected] † Yuan Fang and Xiaolei Qiu are co-first-authors 1 National Engineering and Technology Center for Information Agriculture, Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture, Jiangsu Key Laboratory for Information Agriculture, Jiangsu Collaborative InnovationCenter for Modern Crop Production, Nanjing Agricultural University, Nanjing, China Full list of author information is available at the end of the article
world population of 9 billion in 2050, the demand for wheat is expected
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