Identification of seedling cabbages and weeds using hyperspectral imaging

Target detection is one of research focuses for precision chemical application. This study developed a method to identify seedling cabbages and weeds using hyperspectral imaging. In processing the image data with ENVI software, after dimension reduction, noise reduction, de-correlation for high-dimensional data, and selection of the region of interest, the SAM (Spectral Angle Mapping) model was built for automatic identification of cabbages and weeds. With the HSI (Hyper Spectral Imaging) Analyzer, the training pixels were used to calculate the average spectrum as the standard spectrum. The parameters of the SAM model, which had the best classification results with 3-point smoothing, zero-order derivative, and 6-degrees spectral angle, was determined to achieve the accurate identification of the background, weeds, and cabbages. In comparison, the SAM model can completely separate the plants from the soil background but not perfect for weeds to be separated from the cabbages. In conclusion, the SAM classification model with the HSI analyzer could completely distinguish weeds from background and cabbages.