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ORIGINAL RESEARCH article

Front. Plant Sci.
Sec. Plant Nutrition
Volume 15 - 2024 | doi: 10.3389/fpls.2024.1380306

Reducing Soil and Leaf Shadow Interference in UAV Imagery for Cotton Nitrogen Monitoring

Provisionally accepted
Yin Caixia Yin Caixia 1Yin Caixia Yin Caixia 1Lv Xin Lv Xin 2*Qin Shizhe Qin Shizhe 2*Ma Lulu Ma Lulu 2*Zhang Ze Zhang Ze 3*Tang Qiuxiang Tang Qiuxiang 1*
  • 1 Xinjiang Agricultural University, Ürümqi, China
  • 2 Shihezi University, Shihezi, Xinjiang Uyghur Region, China
  • 3 xinjiang, shihezi, China

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

    The accuracy of monitoring cotton nitrogen content may be compromised in drone imagery, where individual leaves can be shadowed by others. To eliminate the interference of soil and leaf shadows on cotton spectral data and to reliably monitor cotton nitrogen content, it is essential to address these challenges. In this work, green light (550 nm) is divided into 10 levels to limit soil and leaf shadows (LS) on cotton spectrum. How many shadow has an influence on cotton spectra may be determined by the strong correlation between the vegetation index (VI) and leaf nitrogen content (LNC). Several machine learning methods were utilized to predict LNC using less disturbed VI. R-Square (R 2 ), root mean square error (RMSE), and mean absolute error (MAE) were used to evaluate the performance of the model. The results showed that: (ⅰ)after the spectrum were preprocessed by gaussian filter (GF), SG smooth (SG), and combination of GF and SG (GF&SG), the significant relationship between VI and LNC was greatly improved, so the Standard deviation of datasets was also decreased greatly; (ii) the image pixels were classified twice sequentially. Following the first classification, the influence of soil on vegetation index (VI) decreased. Following secondary classification, the influence of soil and LS to VI can be minimized. The relationship between the VI and LNC had improved significantly; (ⅲ) After classifying the image pixels, the VI of 2-3, 2-4, and 2-5 have a stronger relationship with LNC accordingly. Correlation coefficients(r) can reach to 0.5. That optimizes monitoring performance when combined with GF&SG to predict LNC, support vector machine regression (SVMR) has the better performance, R 2 , RMSE, and MAE up to 0.86, 1.01, and 0.71, respectively. The UAV image classification technique in this study can minimize the negative effects of soil and LS on cotton spectrum, allowing for efficient and timely predict LNC.

    Keywords: UAV, Image pixels, vegetation index, Leaf nitrogen content, hyperspectral

    Received: 01 Feb 2024; Accepted: 25 Jun 2024.

    Copyright: © 2024 Caixia, Caixia, Xin, Shizhe, Lulu, Ze and Qiuxiang. 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:
    Lv Xin, Shihezi University, Shihezi, 832003, Xinjiang Uyghur Region, China
    Qin Shizhe, Shihezi University, Shihezi, 832003, Xinjiang Uyghur Region, China
    Ma Lulu, Shihezi University, Shihezi, 832003, Xinjiang Uyghur Region, China
    Zhang Ze, xinjiang, shihezi, China
    Tang Qiuxiang, Xinjiang Agricultural University, Ürümqi, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.