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A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery

Author

Listed:
  • Huasheng Huang
  • Jizhong Deng
  • Yubin Lan
  • Aqing Yang
  • Xiaoling Deng
  • Lei Zhang

Abstract

Appropriate Site Specific Weed Management (SSWM) is crucial to ensure the crop yields. Within SSWM of large-scale area, remote sensing is a key technology to provide accurate weed distribution information. Compared with satellite and piloted aircraft remote sensing, unmanned aerial vehicle (UAV) is capable of capturing high spatial resolution imagery, which will provide more detailed information for weed mapping. The objective of this paper is to generate an accurate weed cover map based on UAV imagery. The UAV RGB imagery was collected in 2017 October over the rice field located in South China. The Fully Convolutional Network (FCN) method was proposed for weed mapping of the collected imagery. Transfer learning was used to improve generalization capability, and skip architecture was applied to increase the prediction accuracy. After that, the performance of FCN architecture was compared with Patch_based CNN algorithm and Pixel_based CNN method. Experimental results showed that our FCN method outperformed others, both in terms of accuracy and efficiency. The overall accuracy of the FCN approach was up to 0.935 and the accuracy for weed recognition was 0.883, which means that this algorithm is capable of generating accurate weed cover maps for the evaluated UAV imagery.

Suggested Citation

  • Huasheng Huang & Jizhong Deng & Yubin Lan & Aqing Yang & Xiaoling Deng & Lei Zhang, 2018. "A fully convolutional network for weed mapping of unmanned aerial vehicle (UAV) imagery," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-19, April.
    • Handle: RePEc:plo:pone00:0196302
    DOI: 10.1371/journal.pone.0196302
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    References listed on IDEAS

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    1. José Manuel Peña & Jorge Torres-Sánchez & Ana Isabel de Castro & Maggi Kelly & Francisca López-Granados, 2013. "Weed Mapping in Early-Season Maize Fields Using Object-Based Analysis of Unmanned Aerial Vehicle (UAV) Images," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-1, October.
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    Cited by:

    1. Steven B Kim & Dong Sub Kim & Xiaoming Mo, 2021. "An image segmentation technique with statistical strategies for pesticide efficacy assessment," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-12, March.
    2. Nur Adibah Mohidem & Nik Norasma Che’Ya & Abdul Shukor Juraimi & Wan Fazilah Fazlil Ilahi & Muhammad Huzaifah Mohd Roslim & Nursyazyla Sulaiman & Mohammadmehdi Saberioon & Nisfariza Mohd Noor, 2021. "How Can Unmanned Aerial Vehicles Be Used for Detecting Weeds in Agricultural Fields?," Agriculture, MDPI, vol. 11(10), pages 1-27, October.

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