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

Abstract

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.

The Intelligent Path Planning System of Agricultural Robot via Reinforcement Learning

“…At present, many scholars have carried out intelligence research [2] and IoT research [3] in agricultural systems. In the work of [4], they study the perception system of crop nutrient elements for agricultural robots, which lays the foundation for agriculturally precise fertilization and saves human labor. In [5], they build a test platform for agricultural mobile robots, using multi-sensor fusion and a self-encoding network to predict the robot's high-precision position, and prevent the location accuracy of the robot being affected by signal interference.…”