Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China.
College of Engineering, South China Agricultural University, Guangzhou 510642, China.
Sensors (Basel). 2022 Feb 25;22(5):1822. doi: 10.3390/s22051822.
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 , and 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.
农业机器人的应用可以解放劳动力。提高机器人感知系统是使其工作的前提。目前，人们对田间机器人的除草和收获系统进行了更多的研究，但对作物病虫害感知、营养元素诊断和精准施肥系统的研究较少。本研究确定了施氮量对甜玉米氮、磷、钾吸收积累的影响。首先，通过光谱特征变量构建了氮、磷、钾含量的线性、抛物线、指数和对数诊断模型。其次，通过二进制小波分解的高频小波灵敏度系数构建了氮、磷、钾含量的偏最小二乘回归和神经网络非线性诊断模型。结果表明，基于二进制小波分解高频小波灵敏度系数的氮、磷、钾含量神经网络非线性诊断模型较好。氮、磷、钾的 nn 分别为 0.974、1.65%和 0.0198；0.969、9.02%和 0.1041；和 0.821、2.16%和 0.0301。该模型可为甜玉米的生长监测和农业机器人的养分感知系统提供感知模型，为实现田间智能精准施肥奠定初步基础。
