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The estimation of wheat tiller number based on UAV images and gradual change features (GCFs)

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Abstract

Wheat (Triticum aestivum L.) is an essential crop that is widely consumed globally. The tiller density is an important factor affecting wheat yield. Therefore, it is necessary to measure the number of tillers during wheat cultivation and breeding, which requires considerable labor and material resources. At present, there is no effective high-throughput measurement method for tiller number estimation, and the conventional tiller survey method cannot accurately reflect the spatial variation of wheat tiller density within the whole field. Therefore, in order to meet the demand for the thematic map of wheat tiller density at the field scale for the variable operation of nitrogen fertilizer, the multispectral images of wheat in Feekes growth stages 2–3 were obtained by unmanned aerial vehicle (UAV), and the characteristic parameters of the number of tillers were used to construct a model that could accurately estimate the number of tillers. Based on the vegetation index (VIs), this work proposed a gradual change features (GCFs) approach, which can greatly improve the disadvantages of using VIs to estimate tiller number, better reflect the tiller status of the wheat population, and have good results on the estimation of tiller in common models. A Lasso + VIs + GCFs method was constructed for accurate estimation of tiller number in multiple growth periods and fertilizer-treated wheat, with an average RMSE of fewer than 9 tillers per square meter, average MAE less than 8 tillers per square meter, and R2 above 0.7. The results of the study not only proposed a high-throughput measurement method for the number of tillers but also provided a reference for the estimation of tiller number and other agronomic parameters.

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Acknowledgements

This study was funded by the National Natural Science Foundation of China (32172110, 32001465, 31872852), the National Key Research and Development Program of China (2018YFD0300805), the Key Research and Development Program (Modern Agriculture) of Jiangsu Province (BE2020319), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), and the Special Fund for Independent Innovation of Agricultural Science and Technology in Jiangsu, China (CX (21) 3065).

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Authors and Affiliations

  1. Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College, Yangzhou University, Yangzhou, 225009, China

    Tao Liu, Yuanyuan Zhao, Fei Wu, Yuzhuang Zhou, Chengxin Ju, Zhongyang Huo & Chengming Sun

  2. Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou, 225009, China

    Tao Liu, Yuanyuan Zhao, Fei Wu, Yuzhuang Zhou, Chengxin Ju, Zhongyang Huo & Chengming Sun

  3. Lixiahe Institute of Agricultural Sciences of Jiangsu, Key Laboratory of Wheat Biology and Genetic Improvement for Low & Middle Yangtze Valley, Ministry of Agriculture and Rural Affairs, Yangzhou, 225012, China

    Junchan Wang

  4. Zhenjiang Agricultural Science Research Institute of Jiangsu Hilly Area, Jurong, 212400, China

    Chen Chen

  5. Chinese Academy of Agricultural Sciences, Agricultural Information Institute/Key Laboratory of Agro-Information Services Technology, Ministry of Agriculture, Beijing, 100081, China

    Xiaochun Zhong & Shengping Liu

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  1. Tao Liu
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Contributions

LT: Conceptualization, Methodology, Data Curation, Writing-Original Draft, Funding acquisition. ZY: Formal analysis, Data Curation, Writing-review and editing. WF: Data Curation, Writing-review and editing. WJ: Resources, Writing-review and editing. CC: Writing-review and editing, Funding acquisition. ZY: Writing-review and editing. JC: Supervision, Writing-review and editing, Funding acquisition. HZ: Funding acquisition, Writing-review and editing. ZX: Supervision, Writing-review and editing. LS: Supervision, Writing-review and editing. SC: Supervision, Writing-review and editing, Funding acquisition.

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Correspondence to Tao Liu, Xiaochun Zhong or Chengming Sun.

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Liu, T., Zhao, Y., Wu, F. et al. The estimation of wheat tiller number based on UAV images and gradual change features (GCFs). Precision Agric 24, 353–374 (2023). https://doi.org/10.1007/s11119-022-09949-5

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