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Evaluation of rapeseed flowering dynamics for different genotypes with UAV platform and machine learning algorithm

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Abstract

Rapeseed (Brassica napus L.) is an important oil-bearing cash crop. Effective identification of the rapeseed flowering date is important for yield estimation and disease control. Traditional field measurements of rapeseed flowering are time-consuming, labour-intensive and strongly subjective. In this study, red, green and blue (RGB) images of rapeseed flowering derived from unmanned aerial vehicles (UAVs) were acquired with a total of seventeen available orthomosaic images, covering the whole flowering period for 299 rapeseed varieties. Five different machine learning methods were employed to identify and to extract the flowering areas in each plot. The results suggested that the accuracy of flowering area extraction by the decision tree-based segmentation model (DTSM) was higher than that of naive Bayes, K-nearest neighbours (KNN), random forest (RF) and support vector machine (SVM) in all varieties and flowering dates, with R2 = 0.97 and root mean square error (RMSE) = 0.051 pixels/pixels. Data on the proportion of flowering area and its dynamics showed differences in the time and duration of each flowering date among varieties. All varieties were classified into four clusters based on k-means clustering analysis. There were significant differences in eight phenotypic parameters among the four clusters, especially in the time of maximum flowering ratio and the time entering the early and medium flowering dates. The results from this study could provide a basis for rapeseed breeding based on flowering dynamics.

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Acknowledgements

This work was jointly supported by grants from the National Key Research and Development Program (2016YFD0300202) and Inner Mongolia Science and technology project (2019ZD024).

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    Authors and Affiliations

    1. College of Land Science and Technology, China Agricultural University, 100193, Beijing, China

      Ziwen Xie & Yuntao Ma

    2. Institute of Industrial crops, Jiangsu Academy of Agricultural Sciences, 210014, Nanjing, China

      Song Chen

    3. Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Oil Crops Research Institute of the Chinese Academy of Agricultural Sciences, 430062, Wuhan, China

      Guizhen Gao, Hao Li & Xiaoming Wu

    4. Department of Geography, Environment, and Tourism, Western Michigan University, 49008, Kalamazoo, MI, USA

      Lei Meng

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    Xie, Z., Chen, S., Gao, G. et al. Evaluation of rapeseed flowering dynamics for different genotypes with UAV platform and machine learning algorithm. Precision Agric 23, 1688–1706 (2022). https://doi.org/10.1007/s11119-022-09904-4

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