快速提取农作物氮素光谱信息实验研究

Study on a Method for Fast Selecting Feature Wavelengths from the Spectral Information of Crop Nitrogen

探索一种快速优选农作物氮素光谱波长方法,对确定作物氮含量有重要意义。基于均匀试验的"均衡分布"特性,设计了一种改进粒子群法。通过均匀分布初始粒子群,采用较少样本点来详细描述优化空间,从而解决标准粒子群法易于陷入局部最优解的问题,并加快了优化收敛速度。应用此改进算法快速提取大豆、棉花、玉米三种作物氮素光谱信息,结合偏最小二乘法(PLS)建立校正模型,结果表明,优选后的波长数目降低约93%,减少了工作量,提高了检测速度,且此结果与提取的三种作物冠层光谱反射特征波段相对应。根据优选后波长建立的校正模型的预测精度提高约34%,增强了预测建模能力,验证了该方法快速提取光谱信息的可行性和有效性。

Research on a method for fast selecting feature wavelengths from the nitrogen spectral information is necessary, which can determine the nitrogen content of crops. Based on the uniformity of uniform design, the present paper proposed an improved particle swarm optimization （PSO） method. The method can choose the initial particle swarm uniformly and describe the optimi- zation space well by fewer sample points, which is helpful to avoiding the local optimum and accelerate the convergence. Then, the method was applied to fast select the nitrogen spectral wavelengths of soybean, cotton and maize. Calibration models based on the partial least square （PLS） method and selected wavelengths were constructed. The results illustrate that compared with the original wavelengths, the number of selected wavelengths decreases about 93%, which means the computation is simplified. Also, the precision of PLS prediction mode based on the selected wavelengths improves by 34% at least, and the prediction ability of calibration model increases greatly. Therefore, the proposed method is both correct and effective.