Real-time moisture measurement on a forage harvester using near-infrared reflectance spectroscopy.

摘要

A mobile, diode-array NIR spectrometer was integrated into the spout of a self-propelled forage harvester to measure crop moisture. Spectral and moisture reference samples were collected in 2004 and 2005 at various field locations in Wisconsin and California, USA, for the development of laboratory and field-based moisture calibrations. Moisture prediction models for whole-plant maize silage (WPCS) developed using laboratory data had a root mean standard error of cross-validation (RMSECV) of 1.1% using five principle components (PCs), while a calibration developed using field data had an RMSECV of 3.3% using four PCs. Lucerne validation results produced RMSECVs of 2.5% using four PCs and 3.7% using three PCs for models using laboratory and field data, respectively. Field data were predicted with calibrations developed using laboratory data with similar error levels, but more spectral information was required. A laboratory-based lucerne model predicted field data with a root mean standard error of prediction (RMSEP) of 3.4% using three PCs as compared to the field model's RMSECV of 3.7% using three PCs. Similar trends were found with WPCS models. Predicting data independent of type of crop resulted in the utilization of more PCs but with higher RMSEPs than the cross-validation results of the predicted dataset. The sensor and associated calibrations were able to predict forage moisture adequately, although more diverse data and further calibration development are needed to improve sensor accuracy to the desired range of +or-2.0 percentage units.