Robustness of visible near‐infrared and mid‐infrared spectroscopic models to changes in the quantity and quality of crop residues in soil

Greenberg, Isabel; Linsler, Deborah; Vohland, Michael; Ludwig, Bernard

doi:10.1002/saj2.20067

Cited by 10 publications

(9 citation statements)

References 27 publications

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“…Following calibration with paired reference data and spectral measurements, models can be used to predict a range of soil properties. However, the resulting model accuracy depends on the prediction mechanisms for the soil property of interest [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Mid-Infrared and X-ray Fluorescence Data Fusion Approaches for Prediction of Soil Properties at the Field Scale

Greenberg

Vohland

Seidel

et al. 2023

Sensors

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Previous studies investigating multi-sensor fusion for the collection of soil information have shown variable improvements, and the underlying prediction mechanisms are not sufficiently understood for spectrally-active and -inactive properties. Our objective was to study prediction mechanisms and benefits of model fusion by measuring mid-infrared (MIR) and X-ray fluorescence (XRF) spectra, texture, total and labile organic carbon (OC) and nitrogen (N) content, pH, and cation exchange capacity (CEC) for n = 117 soils from an arable field in Germany. Partial least squares regression models underwent a three-fold training/testing procedure using MIR spectra or elemental concentrations derived from XRF spectra. Additionally, two sequential hybrid and two high-level fusion approaches were tested. For the studied field, MIR was superior for organic properties (ratio of prediction to interquartile distance of validation (RPIQV) for total OC = 7.7 and N = 5.0)), while XRF was superior for inorganic properties (RPIQV for clay = 3.4, silt = 3.0, and sand = 1.8). Even the optimal fusion approach brought little to no accuracy improvement for these properties. The high XRF accuracy for clay and silt is explained by the large number of elements with variable importance in the projection scores >1 (Fe ≈ Ni > Si ≈ Al ≈ Mg > Mn ≈ K ≈ Pb (clay only) ≈ Cr) with strong spearman correlations (±0.57 < rs < ±0.90) with clay and silt. For spectrally-inactive properties relying on indirect prediction mechanisms, the relative improvements from the optimal fusion approach compared to the best single spectrometer were marginal for pH (3.2% increase in RPIQV versus MIR alone) but more pronounced for labile OC (9.3% versus MIR) and CEC (12% versus XRF). Dominance of a suboptimal spectrometer in a fusion approach worsened performance compared to the best single spectrometer. Granger-Ramanathan averaging, which weights predictions according to accuracy in training, is therefore recommended as a robust approach to capturing the potential benefits of multiple sensors.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Mid-Infrared and X-ray Fluorescence Data Fusion Approaches for Prediction of Soil Properties at the Field Scale

Greenberg

Vohland

Seidel

et al. 2023

Sensors

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“…The use of NIR spectroscopy to analyze samples is fast, non-destructive, efficient, and accurate, with no consumption of chemical reagents and no environmental pollution [ 12 , 13 , 14 , 15 ]. In the past, NIR spectroscopy was applied to identify a range of substances, including the detection of cement raw material content [ 16 ] and zearalenone and deoxynivalenol in maize [ 17 ], prediction of the Ca concentration in apples [ 18 ], content of 20 minerals in beef [ 19 ], residue amount of cyhalothrin in Brassica rapa [ 20 ], authentication and traceability of lemon [ 21 ], and so forth [ 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Determination of Pork Meat Storage Time Using Near-Infrared Spectroscopy Combined with Fuzzy Clustering Algorithms

Jun

et al. 2022

Foods

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The identification of pork meat quality is a significant issue in food safety. In this paper, a novel strategy was proposed for identifying pork meat samples at different storage times via Fourier transform near-infrared (FT-NIR) spectroscopy and fuzzy clustering algorithms. Firstly, the FT-NIR spectra of pork meat samples were collected by an Antaris II spectrometer. Secondly, after spectra preprocessing with multiplicative scatter correction (MSC), the orthogonal linear discriminant analysis (OLDA) method was applied to reduce the dimensionality of the FT-NIR spectra to obtain the discriminant information. Finally, fuzzy C-means (FCM) clustering, K-harmonic means (KHM) clustering, and Gustafson–Kessel (GK) clustering were performed to establish the recognition model and classify the feature information. The highest clustering accuracies of FCM and KHM were both 93.18%, and GK achieved a clustering accuracy of 65.90%. KHM performed the best in the FT-NIR data of pork meat considering the clustering accuracy and computation. The overall experiment results demonstrated that the combination of FT-NIR spectroscopy and fuzzy clustering algorithms is an effective method for distinguishing pork meat storage times and has great application potential in quality evaluation of other kinds of meat.

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“…In addition to soil properties important for classification (e.g., texture) and plant productivity (e.g., total Nitrogen [N t ] content, pH), visNIRS and MIRS have applications in monitoring soil organic carbon (OC) dynamics (Greenberg et al, 2020), with implications for both soil quality and climate change. Soil OC content is an important determinant of soil quality due to its role in nutrient cycling, aggregate stability, water infiltration and erosion prevention (Wiesmeier et al, 2019), and is therefore an indicator of land degradation (Decision 22/COP.11; UNCCD, 2013).…”

Section: Introductionmentioning

confidence: 99%

Performance of field‐scale lab vs in situ visible/near‐ and mid‐infrared spectroscopy for estimation of soil properties

Greenberg

Seidel

Vohland

et al. 2021

European J Soil Science

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Comparison of laboratory versus in situ visible/near‐ (visNIR) and mid‐infrared (MIR) spectroscopy for prediction of various soil properties is required to demonstrate trade‐offs between accuracy and efficiency. Field measurements were made on an arable field in Germany (silt loam Haplic Luvisol) using visNIR (ASD FieldSpec 3 Hi‐Res) and MIR (Agilent Technologies 4300 Handheld FTIR) and material was collected for lab visNIR (Foss XDS Rapid Content Analyzer) and MIR (Bruker‐TENSOR 27) measurements on dried and ground soil and determination of total, labile (>63 μm light), stabilized (>63 μm heavy + <63 μm oxidizable) and resistant organic carbon (OC) content, total nitrogen (Nt), pH and texture. Partial least squares regression models were calculated for five repeated partitions of the dataset (n = 238) into training (75%) and test (25%) sets. Lab spectral models outperformed in situ models for total OC (root mean squared error [RMSE] = 0.24–1.0 g kg−1), Nt (RMSE = 0.026–0.088 g kg−1), pH (RMSE = 0.12–0.28) and texture (RMSE = 0.53–1.5%). For both lab and field spectra, the accuracy of visNIR models was comparable or slightly better than MIR for sand, silt and clay. Spectral estimations for labile (RMSE = 0.34–0.47 g kg−1) and stabilized OC (RMSE = 0.41–0.85 g kg−1) were slightly (lab spectra) to substantially (field spectra) inferior to estimations from multiple linear regressions using total OC, Nt, clay and pH as predictors. Variable importance in the projection scores elucidated differences in spectral prediction mechanisms by spectrometer and OC fraction, and found mineral spectral signatures were highly important. For this field‐scale study with 14% median soil gravimetric water content (GWC), the loss of accuracy from lab to field measurement was lower for visNIR than MIR. Analysis of the driest soils (<9% GWC) found field MIR outperformed field visNIR for OC and Nt estimation and vice versa for the wettest soils (>18%), demonstrating the moisture dependence of performance rankings. Highlights Lab vs field visible/near‐ (visNIRS) and mid‐infrared (MIRS) spectroscopy require comparison for prediction of soil C fractions, N, pH and texture. Lab MIRS prediction of C, N and pH were superior, while texture estimations were comparable or slightly inferior to lab visNIRS. At 14% median soil water content, the loss of accuracy from lab to field measurement was lower for visNIRS than MIRS. The ranking of field visNIRS vs MIRS performance for C and N estimation is moisture dependent.

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Robustness of visible near‐infrared and mid‐infrared spectroscopic models to changes in the quantity and quality of crop residues in soil

Cited by 10 publications

References 27 publications

Evaluation of Mid-Infrared and X-ray Fluorescence Data Fusion Approaches for Prediction of Soil Properties at the Field Scale

Evaluation of Mid-Infrared and X-ray Fluorescence Data Fusion Approaches for Prediction of Soil Properties at the Field Scale

Determination of Pork Meat Storage Time Using Near-Infrared Spectroscopy Combined with Fuzzy Clustering Algorithms

Performance of field‐scale lab vs in situ visible/near‐ and mid‐infrared spectroscopy for estimation of soil properties

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