Predicting Forage Quality of Grasslands Using UAV-Borne Imaging Spectroscopy

Wijesingha, Jayan; Astor, Thomas; Schulze‐Brüninghoff, Damian; Wengert, Matthias; Wachendorf, M.

doi:10.3390/rs12010126

Cited by 70 publications

(68 citation statements)

References 52 publications

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“…The support vector machine (SVM) is another successful algorithm and has been widely used with a Gaussian kernel function [39], and it could be used as a benchmark with RF. Some previous studies reported that the Cubist algorithm was superior to RF in predicting LAI [40] and SVM for estimating acid detergent fibre in forage [41]. In addition, Stochastic Gradient Boosting (SGB) also has been applied to biomass estimation, and its high performance has been reported [42].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Leaf Chlorophyll a, b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance

et al. 2020

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Japanese horseradish (wasabi) grows in very specific conditions, and recent environmental climate changes have damaged wasabi production. In addition, the optimal culture methods are not well known, and it is becoming increasingly difficult for incipient farmers to cultivate it. Chlorophyll a, b and carotenoid contents, as well as their allocation, could be an adequate indicator in evaluating its production and environmental stress; thus, developing an in situ method to monitor photosynthetic pigments based on reflectance could be useful for agricultural management. Besides original reflectance (OR), five pre-processing techniques, namely, first derivative reflectance (FDR), continuum-removed (CR), de-trending (DT), multiplicative scatter correction (MSC), and standard normal variate transformation (SNV), were compared to assess the accuracy of the estimation. Furthermore, five machine learning algorithms—random forest (RF), support vector machine (SVM), kernel-based extreme learning machine (KELM), Cubist, and Stochastic Gradient Boosting (SGB)—were considered. To classify the samples under different pH or sulphur ion concentration conditions, the end of the red edge bands was effective for OR, FDR, DT, MSC, and SNV, while a green-peak band was effective for CR. Overall, KELM and Cubist showed high performance and incorporating pre-processing techniques was effective for obtaining estimated values with high accuracy. The best combinations were found to be DT–KELM for chl a (RPD = 1.511–5.17, RMSE = 1.23–3.62 μg cm−2) and chl a:b (RPD = 0.73–3.17, RMSE = 0.13–0.60); CR–KELM for chl b (RPD = 1.92–5.06, RMSE = 0.41–1.03 μg cm−2) and chl a:car (RPD = 1.31–3.23, RMSE = 0.26–0.50); SNV–Cubist for car (RPD = 1.63–3.32, RMSE = 0.31–1.89 μg cm−2); and DT–Cubist for chl:car (RPD = 1.53–3.96, RMSE = 0.27–0.74).

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Section: Introductionmentioning

confidence: 99%

Estimation of Leaf Chlorophyll a, b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance

et al. 2020

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“…Benefits and limitations exist with all techniques, but UAVs whether alone or in fusion with other techniques promise to be a major enabling technology. The scientific literature over the past few years shows emerging benefits from UAV-captured photogrammetric, multispectral and hyperspectral imaging of pasture condition with examples from the USA, Brazil, Europe, Australia and South Africa [13][14][15][16]. An optimum solution using data fusion possibly exists, combining the best aspects of all technologies.…”

Section: Introductionmentioning

confidence: 99%

“…Crude protein and ADF are two commonly used indicators of pasture digestion. Recent UAV studies have demonstrated the use of hyperspectral sensing in detecting both crude protein and ADF in the natural pastures of other countries [15]. UAV hyperspectral sensors are not low-cost; however, for nutrient estimation they are essential.…”

Section: Introductionmentioning

confidence: 99%

Estimating Plant Pasture Biomass and Quality from UAV Imaging across Queensland’s Rangelands

2020

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The aim of this research was to test recent developments in the use of Remotely Piloted Aircraft Systems or Unmanned Aerial Vehicles (UAV)/drones to map both pasture quantity as biomass yield and pasture quality as the proportions of key pasture nutrients, across a selected range of field sites throughout the rangelands of Queensland. Improved pasture management begins with an understanding of the state of the resource base, UAV based methods can potentially achieve this at improved spatial and temporal scales. This study developed machine learning based predictive models of both pasture measures. UAV-based structure from motion photogrammetry provided a measure of yield from overlapping high resolution visible colour imagery. Pasture nutrient composition was estimated from the spectral signatures of visible near infrared hyperspectral UAV sensing. An automated pasture height surface modelling technique was developed, tested and used along with field site measurements to predict further estimates across each field site. Both prior knowledge and automated predictive modelling techniques were employed to predict yield and nutrition. Pasture height surface modelling was assessed against field measurements using a rising plate meter, results reported correlation coefficients (R2) ranging from 0.2 to 0.4 for both woodland and grassland field sites. Accuracy of the predictive modelling was determined from further field measurements of yield and on average indicated an error of 0.8 t ha−1 in grasslands and 1.3 t ha−1 in mixed woodlands across both modelling approaches. Correlation analyses between measures of pasture quality, acid detergent fibre and crude protein (ADF, CP), and spectral reflectance data indicated the visible red (651 nm) and red-edge (759 nm) regions were highly correlated (ADF R2 = 0.9 and CP R2 = 0.5 mean values). These findings agreed with previous studies linking specific absorption features with grass chemical composition. These results conclude that the practical application of such techniques, to efficiently and accurately map pasture yield and quality, is possible at the field site scale; however, further research is needed, in particular further field sampling of both yield and nutrient elements across such a diverse landscape, with the potential to scale up to a satellite platform for broader scale monitoring.

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“…Parameters listed are (ADF), ash, dry matter (DM), crude protein (CP), in vivo dry matter digestibility (IVDMD), neutral detergent fibre (NDF) and water-soluble carbohydrates (WSC). References [59][60][61] are cited in the supplementary materials.…”

Section: Supplementary Materialsmentioning

confidence: 99%

Machine Learning Algorithms to Predict Forage Nutritive Value of In Situ Perennial Ryegrass Plants Using Hyperspectral Canopy Reflectance Data

et al. 2020

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Nutritive value (NV) of forage is too time consuming and expensive to measure routinely in targeted breeding programs. Non-destructive spectroscopy has the potential to quickly and cheaply measure NV but requires an intermediate modelling step to interpret the spectral data. A novel machine learning technique for forage analysis, Cubist, was used to analyse canopy spectra to predict seven NV parameters, including dry matter (DM), acid detergent fibre (ADF), ash, neutral detergent fibre (NDF), in vivo dry matter digestibility (IVDMD), water soluble carbohydrates (WSC), and crude protein (CP). Perennial ryegrass (Lolium perenne) was used as the test crop. Independent validation of the developed models revealed prediction capabilities with R2 values and Lin’s concordance values reported between 0.49 and 0.82, and 0.68 and 0.89, respectively. Informative wavelengths for the creation of predictive models were identified for the seven NV parameters. These wavelengths included regions of the electromagnetic spectrum that are usually excluded due to high background variation, however, they contain important information and utilising them to obtain meaningful signals within the background variation is an advantage for accurate models. Non-destructive field spectroscopy along with the predictive models was deployed infield to measure NV of individual ryegrass plants. A significant reduction in labour was observed. The associated increase in speed and reduction of cost makes targeting NV in commercial breeding programs now feasible.

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Predicting Forage Quality of Grasslands Using UAV-Borne Imaging Spectroscopy

Cited by 70 publications

References 52 publications

Estimation of Leaf Chlorophyll a, b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance

Estimation of Leaf Chlorophyll a, b and Carotenoid Contents and Their Ratios Using Hyperspectral Reflectance

Estimating Plant Pasture Biomass and Quality from UAV Imaging across Queensland’s Rangelands

Machine Learning Algorithms to Predict Forage Nutritive Value of In Situ Perennial Ryegrass Plants Using Hyperspectral Canopy Reflectance Data

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