LAUSR

Abstract In order to rapidly detect the chlorophyll content in the field, the RGB-NIR camera was used to collect the multi-spectral image of maize canopy. The SPAD value was measured at the same time to indicate the chlorophyll content. The multi-spectral images were processed. Considering the features of multi-spectral images, after image median filtering and segmentation to remove the background effect by field soil, dry straw and weeds, 15 image parameters were extracted including the average grey value of each channel (AR, AG, AB and ANIR), the vegetation indices (ANDVI, ANDGI, ARVI, ADVI), vegetation coverage index (VCI), hue average(AH) and image texture parameters (energy (AASM), moment of inertia (ACON), correlation (ACOR), entropy (AEN) and inverse difference moment (AL)). The sensitive parameters were selected by correlation coefficient analysis and RF method. There were 4 and 8 parameters, respectively. After modeling, the LS-SVM model built by RF selected parameters showed better detection accuracy with Rc2=0.87 and Rv2=0.74. It could be used for rapid and non-destructive detection of the chlorophyll content for field maize.