To schedule fruit tree Syzygium samarangense to bloom and bear fruit is a challenging work, which requires highly experienced and knowledgeable professions. Specific amount of fertilizers should be supplemented at specific timing. Therefore, it is also a labor-intensive work. Our goal is to provide a method to automatically identify nutritional and growing conditions of Syzygium samarangense. In this work, we applied both multispectral and hyperspectral imaging techniques to measure parts of Syzygium samarangense, including branches, leaves, flowers, and fuiits. We examined several important spectral indexes: water content index, biomass content index, structure index, and chlorophyll content index. A custom-built hyperspectral imaging system was used here. The system includes two spectrographs, and two charge-coupled devices One of the spectrographs disperses light in visible wavelength, and the other spectrograph works in short wavelength infrared light region. With line-scanning data acquisition, the collected reflectance data included two-dimensional spatial image as well as reflectance spectrum. The hyperspectral imaging data were compared with results from a commercial multispectral imagery. The selected lightweight multispectral imagery was portable by an UAV. Among the spectral indexes we examined, data from the two techniques were highly linear correlated, indicating that data from multispectral imagery were sufficiently reliable for orchard management. On the other hand, additional spectral characteristics were only shown in hyperspectral imaging data. Calculated images that mapped hyperspectral indexes showed patterned of diseased area in leaves. Therefore, we built an on-site orchard monitoring procedure that combines both multispectral and hyperspectral imaging techniques for wax apple tree.