Near infrared diffuse reflectance spectroscopy for rapid and comprehensive soil condition assessment in smallholder cacao farming systems of Papua New Guinea

Singh, Kanika; Majeed, Israr; Panigrahi, Niranjan; Vasava, Hitesh B.; Fidelis, Chris; Karunaratne, Senani; Bapiwai, Peter; Yinil, David; Sanderson, Todd; Snoeck, Didier; Das, Bhabani S.; Minasny, Budiman; Field, Damien J.

doi:10.1016/j.catena.2019.104185

Cited by 19 publications

(12 citation statements)

References 45 publications

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“…A set of 156 data points from previous harvests of the trial were included to expand the calibration, making a total of 605 plants. Convex hull and Mahalanobis distance were then used to identify spectral outliers which were removed from the analysis if the H value was over 0.6, with sixty-five samples excluded [32]. In total, 540 samples with both lab results and spectra were used in this experiment to build and test the NV predictive models.…”

Section: Study Sitementioning

confidence: 99%

“…To optimise the signal to noise ratio, Savitzky Golay smoothing was applied, with an interval width of 11 nm [33]. To reduce the impact of light scattering, a spectral scatter correction technique, standard normal variate (SNV) was used to scale each spectrum based on their standard deviation and mean [32].…”

Section: Spectra Data Pre-processingmentioning

confidence: 99%

“…R version 3.5.3 was also use for the data splitting; Conditional Latin hypercube sampling was used to split the samples with corresponding lab results into a calibration set of 75% and a validation set of 25% [32]. The calibration set included 405 samples and the validation set included 135 samples.…”

Section: Splitting Data As Model Calibration and Validationmentioning

confidence: 99%

“…Selection of appropriate data for model calibration is important to ensure the sample population is accurately represented by the calibration set, especially for heterogeneous, compositionally complex samples [18]. Simple random splitting of data will not guarantee appropriate selection 2010, so conditional Latin HyperCube sampling was used [32]. This approach is used to select optimal calibration samples through multidimensional consideration of wavelengths, which is important for the development of robust models.…”

Section: Advantages Of the Data Mining Approach For Nv Analysis As Wementioning

confidence: 99%

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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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Section: Study Sitementioning

confidence: 99%

Section: Spectra Data Pre-processingmentioning

confidence: 99%

Section: Splitting Data As Model Calibration and Validationmentioning

confidence: 99%

Section: Advantages Of the Data Mining Approach For Nv Analysis As Wementioning

confidence: 99%

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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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“…Being an economically important crop, the focus on industry growth is reflected in the Cacao Industry Strategic Plan 2016-2025 (CISP), which has set an ambitious target of 310,000 tonnes of annual cacao exports by 2030. Accomplishing an almost eightfold increase in cacao production for export over the next few years represents a significant task for the PNG cacao industry (Singh, Majeed, et al, 2019). Therefore, it is correct to state that cacao is important for both the PNG economy and the small holder livelihoods, who are the major producers.…”

Section: Introductionmentioning

confidence: 99%

Optimization of spectral pre‐processing for estimating soil condition on small farms

Singh

Fidelis²,

Yinil³

et al. 2020

Soil Use and Management

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The concepts of soil security (especially relating to soil condition) provide a useful framework in building spectral libraries. Spectral libraries can be used with the purpose of assessing soil condition by measuring soil organic carbon (SOC) or increasing productivity through soil nutrient management. A spectral library was generated by measuring SOC and nutrients (nitrogen, phosphorous and potassium) and spectral reflectance data over the visible to near-infrared range (350-2,500 nm) in soil samples collected from four production systems in Papua New Guinea (PNG).The spectral library was analysed using SpecOptim, a software tool developed at the James Hutton Institute to explore spectral pre-processing and calibration options.From 192 model combinations of model, the best one was identified for each study area. Different combinations of data were also explored (e.g. by farm or all together).We believe that at the local-scale, soil carbon and nitrogen variability can be captured; however, the spectrally inactive properties such as phosphorous and potassium need to have a higher variability and therefore pooling is required in order to predict properties chemometrically. The SpecOptim software is a useful tool where analysis of spectral data can be difficult to determine. Specifically, it helped improve the accuracy of predictions by 2% for C and N (except for East New Britain site) compared with previously used pre-processing techniques and calibration models while automating identification of the optimal pre-processing approach. We believe that we have developed research-based evidence for using spectral libraries to fit with the soil priority areas of PNG.

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Soil Sensing

Adamchuk

Biswas

Huang

et al. 2021

Sensing Approaches for Precision Agriculture

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Near infrared diffuse reflectance spectroscopy for rapid and comprehensive soil condition assessment in smallholder cacao farming systems of Papua New Guinea

Cited by 19 publications

References 45 publications

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

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

Optimization of spectral pre‐processing for estimating soil condition on small farms

Soil Sensing

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