ABSTRACT

Computer vision systems can be an alternative to traditional methods of analyzing the quality of forage crops, allowing the instantaneous, non-destructive monitoring of the crop, with cost reduction. This study aimed to evaluate the quality parameters of Tifton 85 (Cynodon spp.) using digital images, relating spectral indices to the quality parameters of this forage. In the experimental area, four levels of nitrogen fertilization were applied and the analyses were made at different times after the standardization cut (14, 28, 42, and 56 days). The quality parameters evaluated were mineral matter, crude protein, and neutral detergent fiber. From images obtained in the visible (RGB) and near-infrared (RGNIR) spectral regions, spectral indices were generated. Principal component analysis was applied to summarize the information obtained by spectral indices into a single principal component (PCI). PCI associated with spectral indices was related to forage quality parameters for each cutting time using simple quadratic regression models. The relationships between mineral matter and spectral indices were variable over time. Crude protein and neutral detergent fiber showed the highest relationships with the spectral indices obtained by RGNIR images already at the initial times. Thus, although the RGB images have shown satisfactory results to obtain information about the quality of Tifton 85, the NIR band tends to increase the reliability of the relationships at early times.

Keywords:
Cynodon spp.; Pastures; Computer vision; Digital images