4.5 Article

Detection and Analysis of Degree of Maize Lodging Using UAV-RGB Image Multi-Feature Factors and Various Classification Methods

Journal

Publisher

MDPI
DOI: 10.3390/ijgi10050309

Keywords

unmanned aerial vehicles (UAVs); digital surface model; lodging level; object-oriented classification; color and texture features

Funding

  1. National Key Research and Development Program of China [2016YFD0300605]
  2. National Natural Science Foundation of China [42071426]
  3. Central Public-interest Scientific Institution Basal Research Fund for Chinese Academy of Agricultural Sciences [Y2020YJ07, S2018QY01]

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A method was developed to accurately determine the degree of crop lodging by fusing supervised and object-oriented classifications based on spectrum, texture, and canopy structure data. Factors affecting lodging degree were analyzed based on spatial distribution, enabling determination of optimal sowing date, density, and fertilization method.
Maize (Zea mays L.), one of the most important agricultural crops in the world, which can be devastated by lodging, which can strike maize during its growing season. Maize lodging affects not only the yield but also the quality of its kernels. The identification of lodging is helpful to evaluate losses due to natural disasters, to screen lodging-resistant crop varieties, and to optimize field-management strategies. The accurate detection of crop lodging is inseparable from the accurate determination of the degree of lodging, which helps improve field management in the crop-production process. An approach was developed that fuses supervised and object-oriented classifications on spectrum, texture, and canopy structure data to determine the degree of lodging with high precision. The results showed that, combined with the original image, the change of the digital surface model, and texture features, the overall accuracy of the object-oriented classification method using random forest classifier was the best, which was 86.96% (kappa coefficient was 0.79). The best pixel-level supervised classification of the degree of maize lodging was 78.26% (kappa coefficient was 0.6). Based on the spatial distribution of degree of lodging as a function of crop variety, sowing date, densities, and different nitrogen treatments, this work determines how feature factors affect the degree of lodging. These results allow us to rapidly determine the degree of lodging of field maize, determine the optimal sowing date, optimal density and optimal fertilization method in field production.

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