Predicting the quality of ryegrass using hyperspectral imaging

Abstract

The quality of forage plants is a crucial component of animal performance and a limiting factor in pasture based production systems. Key forage attributes that may require improvement include the sugar, lipid, protein and energy contents of the vegetative parts of these plants. The aim of this study was to evaluate the potential capacity of hyperspectral imaging (HSI) for non-invasive assessment of forage chemical composition. Hyperspectral image data within the visible near-infrared range into the extended near-infrared covering 550–1700 nm wavelengths were obtained from 185 accessions of ryegrass ( Lolium perenne ), which were also analysed for 13 forage quality attributes. Medium to high predictive power was observed for the HSI models of total sugars (R 2 validation of 0.58), high molecular weight sugars (R 2 validation of 0.63), %Ash (R 2 validation of 0.50) and %Nitrogen (R 2 validation of 0.70). Significant HSI models with low R 2 validation of 0.1–0.5 were also obtained for low molecular weight sugars, NDF (%), ADF (%), DOMD (% DM), ME (MJ/kg DM), DM (%), Ca (mg/g) and OM (%). We also observed significant differences in the chemical composition between the pseudostems and leaves of the plants for each accession. The power of HSI for prediction of these differences within plants was also demonstrated. This study paves the way for the HSI technology to be used for in-field estimation of forage composition attributes in perennial ryegrass. This will allow more rapid genetic-based selection and breeding for a trait that is normally expensive to measure providing a cheaper, non-destructive and high throughput screening tool.