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CHINESE JOURNAL OF OIL CROP SCIENCES ›› 2020, Vol. 42 ›› Issue (6): 1043-.doi: 10.19802/j.issn.1007-9084.2019234

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Peanut nitrogen nutrition inversion based on unmanned aerial vehicle remote sensing

  

  1. 1. College of Agriculture, South China Agricultural University, Guangzhou 510642, China; 2. Biotechnology Re⁃search Center ,Shandong Academy of Agricultural Sciences, Jinan 250100 , China; 3. School of Intelligent Manufac⁃turing and Equipment, Shenzhen Institute of Institute of Information Technology, Shenzhen 518172, China
  • Online:2020-12-28 Published:2020-11-18

Abstract: Nitrogen is one of the important factors affecting the growth and development of peanuts. At present,
the traditional method of determining the nitrogen content of crops, Kjeldahl nitrogen determination method, is com⁃
plicated and takes a long time. While unmanned aerial sensing has the characteristics of real-time, flexibility and
low-cost. Therefore, in order to achieve rapid, non-destructive and accurate monitoring of peanut nitrogen content,
the visible light camera carried by phantom 4 unmanned aerial vehicle was used to obtain visible light images of dif⁃
ferent growth stages, and the neural network algorithm was used to establish the relationship model between digital
image color information of leaves and the nitrogen content of leaves. The result showed that when the digital image
index was used as the input vector of the network, the average absolute deviation of the constructed model was about
1.5, and the combination of r, g, b(r=R/(R+G+B), g=G/(R+G+B), b=B/(R+G+B))and a, b, c (a=R+G, b=R+B, c=G+
B) had the best fitting effect, and the average absolute deviation was about 0.2, which contained less error. Through
comparison, both methods could accurately predict the value of nitrogen content in peanut leaves.

Key words:  , peanut;nitrogen content;unmanned aerial vehicle (UAV);neural network