Design and Performance of a Near-Infrared Spectroscopy Measurement System for In-Field Alfalfa Moisture Measurement

Near-infrared spectroscopy (NIRS) is widely used in fruit and vegetable quality evaluations, usually after harvesting. In particular, the moisture content is a key parameter for determining product quality; processing phase, e.g., drying process; and economical value. NIRS methods are well-established for laboratory practices where the specimens are properly prepared and measurement conditions are well controlled. On the other hand, it is known that in-field NIRS measurements present several difficulties, as many influencing variables, such as mechanical vibrations, electrical and optical disturbances, and dust or dirt in general, can affect the spectral measurement. In this paper, we propose the design and present the prototype of a NIRS-based measuring system for the rapid determination of the moisture content of bales. The new system uses of a halogen lamp illumination unit to recover water absorption spectral data in the range of 900-1700 nm. The compact stainless steel body makes the instrument portable and easy to transport for rapid in-field MC measurements. The prototype system was characterized and its performance extensively evaluated in a laboratory environment. Finally, a preliminary test was carried out, where the moisture contents of 12 freshly harvested crops samples were measured using the partial least squares (PLSs) regression method. The obtained results show that our prototype system can estimate the alfalfa moisture content information with a coefficient of determination R-2 of 0.985 and a root mean square relative error of estimation of 7.1%.

Automated summary

Near-infrared spectroscopy (NIRS) is widely used for fruit and vegetable quality evaluations, especially for moisture content determination. However, in-field measurements present challenges. In this study, we propose and demonstrate a prototype NIRS-based system for moisture content measurement, which was evaluated in both laboratory and field environments. Through preliminary testing, we have shown that our system can accurately estimate the moisture content of alfalfa.