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A fast and robust method for plant count in sunflower and maize at different seedling stages using high-resolution UAV RGB imagery

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Abstract

Acquiring the crop plant count is critical for enhancing field decision-making at the seedling stage. Remote sensing using unmanned aerial vehicles (UAVs) provide an accurate and efficient way to estimate plant count. However, there is a lack of a fast and robust method for counting plants in crops with equal spacing and overlapping. Moreover, previous studies only focused on the plant count of a single crop type. Therefore, this study developed a method to fast and non-destructively count plant numbers using high-resolution UAV images. A computer vision-based peak detection algorithm was applied to locate the crop rows and plant seedlings. To test the method’s robustness, it was used to estimate the plant count of two different crop types (maize and sunflower), in three different regions, at two different growth stages, and on images with various resolutions. Maize and sunflower were chosen to represent equidistant crops with distinct leaf shapes and morphological characteristics. For the maize dataset (with different regions and growth stages), the proposed method attained R2 of 0.76 and relative root mean square error (RRMSE) of 4.44%. For the sunflower dataset, the method resulted in R2 and RRMSE of 0.89 and 4.29%, respectively. These results showed that the proposed method outperformed the watershed method (maize: R2 of 0.48, sunflower: R2 of 0.82) and better estimated the plant numbers of high-overlap plants at the seedling stage. Meanwhile, the method achieved higher accuracy than watershed method during the seedling stage (2–4 leaves) of maize in both study sites, with R2 up to 0.78 and 0.91, respectively, and RRMSE of 2.69% and 4.17%, respectively. The RMSE of plant count increased significantly when the image resolution was lower than 1.16 cm and 3.84 cm for maize and sunflower, respectively. Overall, the proposed method can accurately count the plant numbers for in-field crops based on UAV remote sensing images.

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Acknowledgements

The study was supported by the National Key Research and Development Program of China (grant 2021YFD1201602), National Natural Science Foundation of China (Grant No. 42071426, 51922072, 51779161, 51009101), and Central Public‐interest Scientific Institution Basal Research Fund for Chinese Academy of Agricultural Sciences (Grant Nos. Y2020YJ07), the Agricultural Science and Technology Innovation Program of the Chinese Academy of Agricultural Sciences, Hainan Yazhou Bay Seed Lab (B21HJ0221), and Special Fund for Independent Innovation of Agricultural Science and Technology in Jiangsu, China(CX(21)3065).

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Authors and Affiliations

  1. Institute of Crop Sciences, Chinese Academy of Agricultural Sciences/Key Laboratory of Crop Physiology and Ecology, Ministry of Agriculture, Beijing, 100081, People’s Republic of China

    Yi Bai, Chenwei Nie, Minghan Cheng, Shuaibing Liu, Xun Yu, Mingchao Shao, Zixu Wang, Siyu Wang, Nuremanguli Tuohuti, Lei Shi, Bo Ming & Xiuliang Jin

  2. National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, China

    Yi Bai, Hongwu Wang, Minghan Cheng & Xiuliang Jin

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  1. Yi Bai
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  2. Chenwei Nie
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  3. Hongwu Wang
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  13. Xiuliang Jin
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Appendix 1

Appendix 1

See Tables 8 and 9.

Table 8 The nitrogen application methods in the Xinxiang site
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Table 9 The maize verities planted in the Xinxiang and Gongzhuling sites
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Bai, Y., Nie, C., Wang, H. et al. A fast and robust method for plant count in sunflower and maize at different seedling stages using high-resolution UAV RGB imagery. Precision Agric 23, 1720–1742 (2022). https://doi.org/10.1007/s11119-022-09907-1

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