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ORIGINAL RESEARCH article

Front. Plant Sci.
Sec. Technical Advances in Plant Science
Volume 15 - 2024 | doi: 10.3389/fpls.2024.1399872

Estimation of Sorghum Seedling Number from Drone Image Based on Support Vector Machine and YOLO Algorithms

Provisionally accepted
Hongxing Chen Hongxing Chen 1,2Hui Chen Hui Chen 1,2*Xiaoyun Huang Xiaoyun Huang 1,2*Song Zhang Song Zhang 1,2*Shengxi Chen Shengxi Chen 1,2*Fulang Cen Fulang Cen 3*Tengbing He Tengbing He 1,2*Quanzhi Zhao Quanzhi Zhao 4*Zhenran Gao Zhenran Gao 1,2*
  • 1 College of Agriculture, Guizhou University, Guiyang, Guizhou Province, China
  • 2 New Rural Development Institute, Guizhou University, Guiyang, Guizhou Province, China
  • 3 Institute of Rice Industry Technology Research, Guizhou University, Guiyang, China, Guiyang, China
  • 4 Institute of Rice Industry Technology Research, College of Agriculture, Guizhou University, Guiyang, Guizhou Province, China

The final, formatted version of the article will be published soon.

    Accurately counting the number of sorghum seedlings from images captured by unmanned aerial vehicles (UAV) is useful for identifying sorghum varieties with high seedling emergence rates in breeding programs. The traditional method is manual counting, which is time-consuming and laborious. Recently, UAV have been widely used for crop growth monitoring because of their low cost, and their ability to collect high-resolution images and other data non-destructively. However, estimating the number of sorghum seedlings is challenging because of the complexity of field environments. The aim of this study was to test three models for counting sorghum seedlings rapidly and automatically from red-green-blue (RGB) images captured at different flight altitudes by a UAV. The three models were a machine learning approach (Support Vector Machines, SVM) and two deep learning approaches (YOLOv5 and YOLOv8). The robustness of the models was verified using RGB images collected at different heights. The R 2 values of the model outputs for images captured at heights of 15 m, 30 m, and 45 m were, respectively, (SVM: 0.67, 0.57, 0.51), (YOLOv5: 0.76, 0.57, 0.56), and (YOLOv8: 0.93, 0.90, 0.71). Therefore, the YOLOv8 model was most accurate in estimating the number of sorghum seedlings. The results indicate that UAV images combined with an appropriate model can be effective for large-scale counting of sorghum seedlings. This method will be a useful tool for sorghum phenotyping.

    Keywords: UAV, Sorghum, Seedling, SVM, YOLO

    Received: 12 Mar 2024; Accepted: 05 Sep 2024.

    Copyright: © 2024 Chen, Chen, Huang, Zhang, Chen, Cen, He, Zhao and Gao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Hui Chen, College of Agriculture, Guizhou University, Guiyang, 550025, Guizhou Province, China
    Xiaoyun Huang, College of Agriculture, Guizhou University, Guiyang, 550025, Guizhou Province, China
    Song Zhang, College of Agriculture, Guizhou University, Guiyang, 550025, Guizhou Province, China
    Shengxi Chen, College of Agriculture, Guizhou University, Guiyang, 550025, Guizhou Province, China
    Fulang Cen, Institute of Rice Industry Technology Research, Guizhou University, Guiyang, China, Guiyang, China
    Tengbing He, College of Agriculture, Guizhou University, Guiyang, 550025, Guizhou Province, China
    Quanzhi Zhao, Institute of Rice Industry Technology Research, College of Agriculture, Guizhou University, Guiyang, 550025, Guizhou Province, China
    Zhenran Gao, College of Agriculture, Guizhou University, Guiyang, 550025, Guizhou Province, China

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