Field mapping of chemical composition of forage using hyperspectral imaging in a grass meadow

Suzuki, Yumiko; Tanaka, Katsuyuki; Kato, Wataru; Okamoto, Hiroshi; Kataoka, Takao; Shimada, Hiroshi; Sugiura, Toshihiro; Shima, Eikichi

doi:10.1111/j.1744-697x.2008.00122.x

Cited by 12 publications

(2 citation statements)

References 27 publications

(31 reference statements)

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“…Hyperspectral data can be used to obtain information on the quality of forage on both spatial and temporal scales [8, 10]. This extra information can be used to parameterize mathematical models of forage growth and their response to environmental and management variables (e.g.…”

Section: Discussionmentioning

confidence: 99%

Predicting the quality of ryegrass using hyperspectral imaging

et al. 2019

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Background 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. Results 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. Conclusion 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.

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Section: Discussionmentioning

confidence: 99%

Predicting the quality of ryegrass using hyperspectral imaging

et al. 2019

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“…Therefore, its direction must be moved in order to scan a target area (Okamoto et al. 2006a; Suzuki et al. 2008b).…”

Section: Methodsmentioning

confidence: 99%

Mapping the spatial distribution of botanical composition and herbage mass in pastures using hyperspectral imaging

et al. 2012

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Quantifying the vegetation heterogeneity in pastures is important to understand the behavior and performance of grazing animals. In this study, we developed a technique for mapping spatial distribution of botanical composition (perennial ryegrass, white clover and other plants) and herbage mass of perennial ryegrass in a grazed pasture using a hyperspectral imaging system, which can glean more detailed spectral information than conventional multispectral imaging that uses standard band frequencies. Plant portions were extracted from hyperspectral image using normalized difference vegetation index threshold and classified into the three plant species or species groups using linear discriminant analysis models. The overall success rate of plant extraction and discrimination between plant species or species groups was 91.6 and 80.3%, respectively. Herbage mass of perennial ryegrass was estimated using partial least squares regression analysis, where the estimated values showed a coefficient of determination of 0.60 against the observed values. In consequence of the models, our results suggested that the difference of leaf cell structure and photosynthesis pigments influenced discrimination between plant species or species group and estimation of the herbage mass. The results demonstrate the potential of the hyperspectral imaging system for creating high spatial resolution maps of vegetation characteristics in pastures

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Comparing how accurately four different proximal spectrometers can estimate pasture nutritive characteristics: effects of spectral range and data type

et al. 2022

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The nutrition of grazing ruminants can be optimized by allocating pasture according to its nutritive characteristics, provided that nutritive concentrations are determined in near-real time. Current proximal spectrometers can provide accurate predictive results but are bulky and expensive. This study compared an industry standard, ‘control’, proximal spectrometer, often used for scientific estimation of pasture nutrient concentrations in situ (350–2500 nm spectral range), with three lower-cost, ‘next-generation’, handheld spectrometers. The candidate sensors included a hyperspectral camera (397–1004 nm), and two handheld spectrometers (908–1676 nm and 1345–2555 nm respectively). Pasture samples (n = 145) collected from two paddocks on a working Australian dairy farm, over three timepoints, were scanned in situ by each instrument and then analysed for eight nutritive parameters. Chemometric models were then developed for each nutrient using data from each sensor (split into 80:20 calibration and validation sets). According to Lin’s Concordance Correlation Coefficient (LCCC) from independent validation (n = 29), the hyperspectral camera was the best candidate instrument (LCCC from 0.31 to 0.85, and 0.67 on average), rivalling the control sensor (LCCC from 0.41 to 0.84, and 0.67 on average). Consideration was given to whether the hyperspectral camera’s success was due to spectral range or data type/capture method. It was found that the 400–920 nm (trimmed) spectral region was slightly less sensitive in principle to nutrient concentrations than higher spectral ranges. Therefore, the predictive performance of the camera was attributed to the advantage of gathering data as hyperspectral images as opposed to single spectra.

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Field mapping of chemical composition of forage using hyperspectral imaging in a grass meadow

Cited by 12 publications

References 27 publications

Predicting the quality of ryegrass using hyperspectral imaging

Predicting the quality of ryegrass using hyperspectral imaging

Mapping the spatial distribution of botanical composition and herbage mass in pastures using hyperspectral imaging

Comparing how accurately four different proximal spectrometers can estimate pasture nutritive characteristics: effects of spectral range and data type

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