Assessment of Aerial Agrichemical Spraying Effect Using Moderate-Resolution Satellite Imagery.

Remote sensing technique can be used to examine the effects of agrichemical application on the performance of field crops at a large scale in an effort to develop precision agricultural aerial spraying technology. In this study, an airplane M-18B at the 4-m flight height was used to spray a mix of agrichemicals (a fungicide and a plant growth regulator) to control rice leaf blast disease and improve the growth vigor of rice plants in the field. After the aerial spraying, satellite imagery of tested area was acquired and processed to calculate vegetation indices (VIs). Ground agrichemical concentration data were also collected. The relationships between droplets deposition and VIs were analyzed. The results indicated that the highest correlation coefficient between single phase spectral feature (NDVI) and droplets deposition points density (DDPD, points·cm-2) was 0.315 with P-value of 0.035 while the highest correlation coefficient between temporal change characteristic (MSAVI) and droplets deposition volume density (DDVD, µL·cm-2) was 0.312 with P-value of 0.038). Rice plants with the greatest growth vigor were all detected within the spraying swath, with a gradual decrease in the vigor of rice plants with the increase of droplets drift distance. There were similar trend patterns in the changes of the spraying effects based on the spatial interpolation maps of droplets deposition data and spectral characteristics. Therefore, vegetation indexes, NDVI and MSAVI calculated from satellite imagery can be used to determine the aerial spraying effects in the field on a large scale.

[Study on a method for fast selecting feature wavelengths from the spectral information of crop nitrogen].

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 optimization 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.