Diffuse reflectance spectroscopy is a well-established non-destructive technique for in-situ estimation of internal fruit quality properties. However, the operating range of the conventionally used instruments is limited to a few cm and often requires direct surface contact with the fruit. Alternative non-destructive approaches, such as hyperspectral imaging, allow for space between the sensor and the object, but in return, they require controlled illumination conditions commonly realized using dark chambers. In this work, we present a novel approach toward remote sensing of relevant fruit quality parameters on the case study of estimating total soluble solids (TSS) and dry matter content (DMC) in apples using a prototype supercontinuum-based multispectral LiDAR (MSL). Experimental results are acquired over a stand-off range of 0.5 m under uncontrolled illumination conditions. The spectral data is acquired across the 580–900 nm spectral range of the supercontinuum source, and of 0.73 is achieved for estimating TSS and DMC using a random forest regression. These results on the estimated parameters are comparable to those reported previously in the literature for in-house developed prototypes relying on fruit contact or immediate proximity. In contrast, our experiments demonstrated TSS and DMC estimation at larger distances relative to typical reflectance spectroscopy instruments and without controlled illumination conditions typically mandated by hyperspectral imaging. Moreover, we demonstrate how our results translate to the estimation of TSS and DMC from experimentally generated multispectral 3D point clouds at a stand-off range of 5 m, demonstrating the potential of simultaneous acquisition of spectral and geometrical data at even higher ranges, showcasing the possibility of new use-cases.
