Spectral Diagnostic Model for Agricultural Robot System Based on Binary Wavelet Algorithm

Sensors

Yuanjiao Feng¹  Ting Gao¹  Jianwu Wang¹  Xiaoyi Wang¹  Chongyang Han²  Ying Zhang²  Weibin Wu²  Jie Li²  Ting Tang² 

[1] College of Natural Resources and Environment, South China Agricultural University, Guangzhou 510642, China;Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China;

关键词: agricultural robotics;    diagnosis model;    hyperspectral image;    binary wavelet algorithm;   

DOI  :  10.3390/s22051822

来源: DOAJ

【 摘 要 】

The application of agricultural robots can liberate labor. The improvement of robot sensing systems is the premise of making it work. At present, more research is being conducted on weeding and harvesting systems of field robot, but less research is being conducted on crop disease and insect pest perception, nutritional element diagnosis and precision fertilizer spraying systems. In this study, the effects of the nitrogen application rate on the absorption and accumulation of nitrogen, phosphorus and potassium in sweet maize were determined. Firstly, linear, parabolic, exponential and logarithmic diagnostic models of nitrogen, phosphorus and potassium contents were constructed by spectral characteristic variables. Secondly, the partial least squares regression and neural network nonlinear diagnosis model of nitrogen, phosphorus and potassium contents were constructed by the high-frequency wavelet sensitivity coefficient of binary wavelet decomposition. The results show that the neural network nonlinear diagnosis model of nitrogen, phosphorus and potassium content based on the high-frequency wavelet sensitivity coefficient of binary wavelet decomposition is better. The R², MRE and NRMSE of nn of nitrogen, phosphorus and potassium were 0.974, 1.65% and 0.0198; 0.969, 9.02% and 0.1041; and 0.821, 2.16% and 0.0301, respectively. The model can provide growth monitoring for sweet corn and a perception model for the nutrient element perception system of an agricultural robot, while making preliminary preparations for the realization of intelligent and accurate field fertilization.

【 授权许可】

Unknown