Spectral difference analysis and identification of different maturity blueberry fruit based on hyperspectral imaging using spectral index

Hyperspectral imaging, with many narrow bands of spectra, is strongly capable to detect or classify objects. It has been become one research hotspot in the field of near-ground remote sensing. However, the higher demands for computing and complex operating of instrument are still the bottleneck for hyperspectral imaging technology applied in field. Band selection is a common way to reduce the dimensionality of hyperspectral imaging cube and simplify the design of spectral imaging instrument. In this research, hyperspectral images of blueberry fruit were collected both in the laboratory and in field. A set of spectral bands were selected by analyzing the differences among blueberry fruits at different growth stages and backgrounds. Furthermore, a normalized spectral index was set up using the bands selected to identify the three growth stages of blueberry fruits, aiming to eliminate the impact of background included leaf, branch, soil, illumination variation and so on. Two classifiers of spectral angle mapping (SAM), multinomial logistic regression (MLR) and classification tree were used to verify the results of identification of blueberry fruit. The detection accuracy was 82.1% for SAM classifier using all spectral bands, 88.5% for MLR classifier using selected bands and 89.8% for decision tree using the spectral index. The results indicated that the normalization spectral index can both lower the complexity of computing and reduce the impact of noisy background in field.