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Estimating maize seedling number with UAV RGB images and advanced image processing methods

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Abstract

Accurately identifying the quantity of maize seedlings is useful in improving maize varieties with high seedling emergence rates in a breeding program. The traditional method is to calculate the number of crops manually, which is labor-intensive and time-consuming. Recently, observation methods utilizing a UAV have been widely employed to monitor crop growth due to their low cost, intuitive nature and ability to collect data without contacting the crop. However, most investigations have lacked a systematic strategy for seedling identification. Additionally, estimating the quantity of maize seedlings is challenging due to the complexity of field crop growth environments. The purpose of this research was to rapidly and automatically count maize seedlings. Three models for estimating the quantity of maize seedlings in the field were developed: corner detection model (C), linear regression model (L) and deep learning model (D). The robustness of these maize seedling counting models was validated using RGB images taken at various dates and locations. The maize seedling recognition rate of the three models were 99.78% (C), 99.9% (L) and 98.45% (D) respectively. The L model can be well adapted to different data to identify the number of maize seedlings. The results indicated that the high-throughput and fast method of calculating the number of maize seedlings is a useful tool for maize phenotyping.

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References

  • Askew, S. D., Wilcut, J. W., & Cranmer, J. R. (2002). Cotton (Gossypium hirsutum) and weed response to flumioxazin applied preplant and postemergence directed. Weed Technology, 16(1), 184–190. https://doi.org/10.1614/0890-037X(2002)016[0184:CGHAWR]2.0.CO;2

    Article  CAS  Google Scholar 

  • Bagheri, N. (2017). Development of a high-resolution aerial remote-sensing system for precision agriculture. International Journal of Remote Sensing, 38(8–10), 2053–2065. https://doi.org/10.1080/01431161.2016.1225182

    Article  Google Scholar 

  • Berge, T. W., Rene, C. H., Aastveit, A. H., & Fykse, H. (2008). Simulating the effects of mapping and spraying resolution and threshold level on accuracy of patch spraying decisions and herbicide use based on mapped weed data. Acta Agriculturae Scandinavica Section B-Soil and Plant Science, 58(3), 216–229. https://doi.org/10.1080/09064710701593087

    Article  Google Scholar 

  • Brichet, N., Fournier, C., Turc, O., Strauss, O., Artzet, S., Pradal, C., et al. (2017). A robot-assisted imaging pipeline for tracking the growths of maize ear and silks in a high-throughput phenotyping platform. Plant Methods, 13(1), 1–12. https://doi.org/10.1186/s13007-017-0246-7

    Article  Google Scholar 

  • Buters, T., Belton, D., & Cross, A. (2019). Seed and seedling detection using unmanned aerial vehicles and automated image classification in the monitoring of ecological recovery. Drones, 3(3), 53. https://doi.org/10.3390/drones3030053

    Article  Google Scholar 

  • Cao, Q., He, M., Men, H., & Wang, C. (2011). Effects of interaction between planting density and nitrogen rate on grain yield and nitrogen use efficiency in winter wheat. Plant Nutrition and Fertilizer Science, 17(4), 815–822.

    CAS  Google Scholar 

  • Dhankhar, P., & Sahu, N. (2013). A review and research of edge detection techniques for image segmentation. International Journal of Computer Science and Mobile Computing, 2(7), 86–92.

    Google Scholar 

  • Doebley, J., Stec, A., & Hubbard, L. (1997). The evolution of apical dominance in maize. Nature, 386(6624), 485–488.

    Article  CAS  Google Scholar 

  • Dyrmann, M., Karstoft, H., & Midtiby, H. S. (2016). Plant species classification using deep convolutional neural network. Biosystems Engineering, 151, 72–80.

    Article  Google Scholar 

  • Everingham, M., Van Gool, L., Williams, C. K., Winn, J., & Zisserman, A. (2010). The pascal visual object classes (voc) challenge. International Journal of Computer Vision, 88(2), 303–338.

    Article  Google Scholar 

  • García-Santillán, I. D., Montalvo, M., Guerrero, J. M., & Pajares, G. (2017). Automatic detection of curved and straight crop rows from images in maize fields. Biosystems Engineering, 156, 61–79. https://doi.org/10.1016/j.biosystemseng.2017.01.013

    Article  Google Scholar 

  • Gnädinger, F., & Schmidhalter, U. (2017). Digital counts of maize plants by unmanned aerial vehicles (UAVs). Remote Sensing, 9(6), 544. https://doi.org/10.3390/rs9060544

    Article  Google Scholar 

  • Harris, C. and Stephens, M. (1988). A combined corner and edge detector. In C. J. Taylor (Eds.), Proceedings of the Fourth Alvey Vision Conference (pp. 147–151). Manchester, UK: Alvey Vision Club

  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770–778).

  • Hunt, E. R., Cavigelli, M., Daughtry, C. S., Mcmurtrey, J. E., & Walthall, C. L. (2005). Evaluation of digital photography from model aircraft for remote sensing of crop biomass and nitrogen status. Precision Agriculture, 6(4), 359–378. https://doi.org/10.1007/s11119-005-2324-5

    Article  Google Scholar 

  • Jin, X., Liu, S., Baret, F., Hemerlé, M., & Comar, A. (2017). Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sensing of Environment, 198, 105–114. https://doi.org/10.1016/j.rse.2017.06.007

    Article  Google Scholar 

  • Jin, X., Zarco-Tejada, P. J., Schmidhalter, U., Reynolds, M. P., Hawkesford, M. J., Varshney, R. K., et al. (2020). High-throughput estimation of crop traits: A review of ground and aerial phenotyping platforms. IEEE Geoscience and Remote Sensing Magazine, 9(1), 200–231. https://doi.org/10.1109/MGRS.2020.2998816

    Article  Google Scholar 

  • Kasampalis, D. A., Alexandridis, T. K., Deva, C., Challinor, A., Moshou, D., & Zalidis, G. (2018). Contribution of remote sensing on crop models: a review. Journal of Imaging, 4(4), 52. https://doi.org/10.3390/jimaging4040052

    Article  Google Scholar 

  • Lakshmi, S., & Sankaranarayanan, D. V. (2010). A study of edge detection techniques for segmentation computing approaches. International Journal of Computer Applications Special Issue on Computer Aided Soft Computing Techniques for Imaging and Biomedical Applications. https://doi.org/10.5120/993-25

  • Lin, Z. (2008). UAV for mapping—Low altitude photogrammetric survey. International Archives of Photogrammetry and Remote Sensing, 37, 1183–1186.

    Google Scholar 

  • Liu, S., Baret, F., Andrieu, B., Burger, P., & Hemmerle, M. (2017). Estimation of wheat plant density at early stages using high resolution imagery. Frontiers in Plant Science, 8, 739. https://doi.org/10.3389/fpls.2017.00739

    Article  PubMed  PubMed Central  Google Scholar 

  • Liu, T., Wu, W., Chen, W., Sun, C., Chen, C., Wang, R., et al. (2016). A shadow-based method to calculate the percentage of filled rice grains. Biosystems Engineering, 150, 79–88. https://doi.org/10.1016/j.biosystemseng.2016.07.011

    Article  Google Scholar 

  • Liu, T., Yang, T., Li, C., Li, R., Wu, W., Zhong, X., et al. (2018). A method to calculate the number of wheat seedlings in the 1st to the 3rd leaf growth stages. Plant Methods, 14(1), 1–14. https://doi.org/10.1186/s13007-018-0369-5

    Article  CAS  Google Scholar 

  • Liu, Y., Cen, C., Che, Y., Ke, R., Ma, Y., & Ma, Y. (2020). Detection of Maize Tassels from UAV RGB Imagery with Faster R-CNN. Remote Sensing, 12(2), 338. https://doi.org/10.3390/rs12020338

    Article  CAS  Google Scholar 

  • Madec, S., Jin, X., Lu, H., De Solan, B., Liu, S., Duyme, F., et al. (2019). Ear density estimation from high resolution RGB imagery using deep learning technique. Agricultural and Forest Meteorology, 264, 225–234. https://doi.org/10.1016/j.agrformet.2018.10.013

    Article  Google Scholar 

  • Maes, W. H., & Steppe, K. (2019). Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture. Trends in Plant Science, 24(2), 152–164. https://doi.org/10.1016/j.tplants.2018.11.007

    Article  CAS  PubMed  Google Scholar 

  • Milioto, A., Lottes, P., & Stachniss, C. (2018). Real-time semantic segmentation of crop and weed for precision agriculture robots leveraging background knowledge in CNNs. In 2018 IEEE international conference on robotics and automation (ICRA) (pp. 2229–2235). IEEE

  • Moravec, H. P. (1980). Obstacle avoidance and navigation in the real world by a seeing robot rover. Stanford University, CA, USA, Dept of Computer Science, No. STAN-CS-80-813.

  • Muthukrishnan, R., & Radha, M. (2011). Edge detection techniques for image segmentation. International Journal of Computer Science & Information Technology, 3(6), 259.

    Article  Google Scholar 

  • Nixon, M., & Aguado, A. (2019). Feature extraction and image processing for computer vision. Netherlands: Academic Press.

    Google Scholar 

  • Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1), 62–66. https://doi.org/10.1109/tsmc.1979.4310076

    Article  Google Scholar 

  • Quan, L., Feng, H., Lv, Y., Wang, Q., Zhang, C., Liu, J., et al. (2019). Maize seedling detection under different growth stages and complex field environments based on an improved Faster R-CNN. Biosystems Engineering, 184, 1–23. https://doi.org/10.1016/j.biosystemseng.2019.05.002

    Article  Google Scholar 

  • Radoglou-Grammatikis, P., Sarigiannidis, P., Lagkas, T., & Moscholios, I. (2020). A compilation of UAV applications for precision agriculture. Computer Networks, 172, 107148. https://doi.org/10.1016/j.comnet.2020.107148

    Article  Google Scholar 

  • Ren, S., He, K., Girshick, R., & Sun, J. (2017). Faster r-cnn: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis & Machine Intelligence, 39(6), 1137–1149. https://doi.org/10.1109/tpami.2016.2577031

    Article  Google Scholar 

  • Shakhatreh, H., Sawalmeh, A. H., Al-Fuqaha, A., Dou, Z., Almaita, E., Khalil, I., et al. (2019). Unmanned aerial vehicles (UAVs): A survey on civil applications and key research challenges. IEEE Access, 7, 48572–48634. https://doi.org/10.1002/net.21818

    Article  Google Scholar 

  • Shi, Y., Wang, N., Taylor, R. K., & Raun, W. R. (2015). Improvement of a ground-LiDAR-based corn plant population and spacing measurement system. Computers and Electronics in Agriculture, 112, 92–101. https://doi.org/10.1016/j.compag.2014.11.026

    Article  Google Scholar 

  • Steinwand, M. A., & Ronald, P. C. (2020). Crop biotechnology and the future of food. Nature Food, 1(5), 273–283. https://doi.org/10.1038/s43016-020-0072-3

    Article  Google Scholar 

  • Sundar, H., Silver, D., Gagvani, N., & Dickinson, S. (2003, May). Skeleton based shape matching and retrieval. In 2003 Shape Modeling International. (pp. 130–139). IEEE.

  • Sylvester, G., (Ed.) (2018). E-agriculture in action: Drones for agriculture. Food and Agriculture Organization of the United Nations and International Telecommunication Union.

  • Tyagi, & Avinash, C. (2016). Towards a second green revolution. Irrigation and Drainage, 65(4), 388–389.

    Article  Google Scholar 

  • Wang X S., Cheng, C. (2015). Weed seeds classification based on PCANet deep learning baseline. In 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA) (pp. 408–415). IEEE

  • Wu, W., Liu, T., Zhou, P., Yang, T., Li, C., Zhong, X., et al. (2019). Image analysis-based recognition and quantification of grain number per panicle in rice. Plant Methods, 15(1), 122. https://doi.org/10.1186/s13007-019-0510-0

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  • Yu, Z., Cao, Z., Wu, X., Bai, X., Qin, Y., Zhuo, W., et al. (2013). Automatic image-based detection technology for two critical growth stages of maize: Emergence and three-leaf stage. Agricultural and Forest Meteorology, 174, 65–84. https://doi.org/10.1016/j.agrformet.2013.02.011

    Article  Google Scholar 

  • Zhen, S., Li, M., Gao, Q., Li, Q., & Yan, J. (2018). Analysis and future direction of maize production in heilongjiang province. Chinese Journal of Agricultural Resources and Regional Planning, 2015(28), 91–99.

    Google Scholar 

  • Zhou, C. Q., Yang, G. J., Liang, D., Yang, X., & Xu, Bo. (2018). An integrated skeleton extraction and pruning method for spatial recognition of maize seedlings in MGV and UAV remote images. IEEE Transactions on Geoscience and Remote Sensing, 56(8), 4618–4632. https://doi.org/10.1109/TGRS.2018.2830823

    Article  Google Scholar 

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Acknowledgements

This research was supported by the National Key Research and Development Program of China (Grant 2021YFD1201602), National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, China, National Natural Science Foundation of China (Grant Nos. 42071426, 51922072, 51779161, and 51009101), 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 Science, Chinese Academy of Agricultural Sciences, Beijing, 100081, China

    Shuaibing Liu, Dameng Yin, Xiaobin Xu, Lei Shi & Xiuliang Jin

  2. National Nanfan Research Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya, 572024, China

    Shuaibing Liu, Dameng Yin, Lei Shi & Xiuliang Jin

  3. School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, China

    Shuaibing Liu

  4. National Engineering Research Center for Information Technology in Agriculture, Beijing, 100089, China

    Haikuan Feng, Zhenhai Li & Xiaobin Xu

  5. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, 430079, China

    Xiaobin Xu

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  1. Shuaibing Liu
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Correspondence to Xiuliang Jin.

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Liu, S., Yin, D., Feng, H. et al. Estimating maize seedling number with UAV RGB images and advanced image processing methods. Precision Agric 23, 1604–1632 (2022). https://doi.org/10.1007/s11119-022-09899-y

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