Performance of mid infrared spectroscopy to predict nutrients for agricultural soils in selected areas of Ethiopia

Lelago, Alemu; Bibiso, Mesfin

doi:10.1016/j.heliyon.2022.e09050

Cited by 8 publications

(6 citation statements)

References 21 publications

(31 reference statements)

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“…The results obtained in this study are consistent with the findings of [51], who evaluated the predictive ability of the MIR region for CIC (Cation Exchange Capacity). In their research, promising results were obtained using PLSR models, with a coefficient of determination (R 2 ) of 0.92 and an RPD value of 3.5.…”

Section: Resultssupporting

confidence: 91%

Cation Exchange Capacity in Grazing Systems and a Case Study for Quantification by Hyperspectral Imaging

Rodríguez Monroy,

Ortega Monsalve,

Fernando Galeano-Vasco

et al. 2024

Agricultural Sciences

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This chapter provides an overview of cation exchange capacity (CEC) and its importance as an indicator of soil fertility, particularly in the assessment of grassland quality. The limitations of traditional methods are highlighted, and the need to explore more agile approaches to grassland quality assessment is emphasized. The increasing use of hyperspectral information (HSI) as an accurate tool for measuring soil properties, which promotes more effective and sustainable rangeland management, is further explored. This provides data on soil fertility and forage quality, enabling more accurate decisions. The benefits and challenges of using HSI data to estimate CEC and its potential to improve pasture and forage production will also be examined. HSI technology allows information to be collected and analyzed from reflected light at different wavelengths, providing a clear understanding of soil physical and chemical properties. In addition, a case study illustrating the estimation of CIC using hyperspectral cameras in the department of Antioquia, Colombia, is presented. The chapter emphasizes the relevance of this topic in the rangeland context and concludes with a future outlook that anticipates a change in the management and understanding of grazing systems.

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Section: Resultssupporting

confidence: 91%

Cation Exchange Capacity in Grazing Systems and a Case Study for Quantification by Hyperspectral Imaging

Rodríguez Monroy,

Ortega Monsalve,

Fernando Galeano-Vasco

et al. 2024

Agricultural Sciences

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“…By comparing the outcomes of the developed model, it can be shown that an improved RPD value indicates the ability to successfully combine NIR data with the prediction of total polyphenols. Many research articles have been published previously having RPD < 3 37‐39 . The probability of different variable selection techniques for the total polyphenol prediction in black tea was thus successfully proven in the current investigation.…”

Section: Resultsmentioning

confidence: 60%

“…Many research articles have been published previously having RPD < 3. [37][38][39] The probability of different variable selection techniques for the total polyphenol prediction in black tea was thus successfully proven in the current investigation.…”

Section: Si-cars-pls Resultsmentioning

confidence: 74%

Comparison of Si‐GA‐PLS and Si‐CARS‐PLS build algorithms for quantitation of total polyphenols in black tea using the spectral analytical system

et al. 2023

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BACKGROUNDThe objective of the current study was to compare two machine learning approaches for the quantification of total polyphenols by choosing the optimal spectral intervals utilizing the synergy interval partial least squares (Si‐PLS) model. To increase the resilience of built models, the genetic algorithm (GA) and competitive adaptive reweighted sampling (CARS) were applied to a subset of variables.RESULTSThe collected spectral data were divided into 19 sub‐interval selections totaling 246 variables, yielding the lowest root mean square error of cross‐validation (RMSECV). The performance of the model was evaluated using the correlation coefficient for calibration (RC), prediction (RP), RMSECV, root mean square error of prediction (RMSEP) and residual predictive deviation (RPD) value. The Si‐GA‐PLS model produced the following results: PCs = 9; RC = 0.915; RMSECV = 1.39; RP = 0.8878; RMSEP = 1.62; and RPD = 2.32. The performance of the Si‐CARS‐PLS model was noted to be best at PCs = 10, while RC = 0.9723, RMSECV = 0.81, RP = 0.9114, RMSEP = 1.45 and RPD = 2.59.CONCLUSIONThe build model's prediction ability was amended in the order PLS < Si‐PLS < CARS‐PLS when full spectroscopic data were used and Si‐PLS < Si‐GA‐PLS < Si‐CARS‐PLS when interval selection was performed with the Si‐PLS model. Finally, the developed method was successfully used to quantify total polyphenols in tea. © 2023 Society of Chemical Industry.

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“…Recent studies include the prediction of soil available ions after the input of rock powders, with spectra in the Range of Visible (Vis), Near-Infrared (NIR), and Short-Wave Infrared (SWIR), achieving reasonable results [14]. It has also been used to predict nutrients and other attributes in agricultural soils, with successful results for Ca, CEC, TN, Mg, pH and Fe [15]. In the Vis-NIR range, it has achieved poor results for in-fi eld prediction of pH and reasonable results for liming requirements [16].…”

Section: Agriculturementioning

confidence: 99%

Recent Trends on the use of Infrared Spectroscopy for Soil Assessment

Jamil Maia*

2023

J Biomed Res Environ Sci

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Infrared spectroscopy has emerged as a powerful tool to assess soil properties for both environmental science and agriculture. Here, we explore its recent trends and developments for soil assessment. This technique is an alternative that counters the limitations of traditional laboratory methods, offering a cost-effective and non-destructive approach. Here, the latest trends in the innovation landscape of infrared spectroscopy for soil assessment are explored, providing insights on its broad range of applications and into the future trajectory of this technology. Firstly, we delve into its applications in agriculture, highlighting its potential for prediction of many soil attributes. Next, we explore soil carbon assessment, emphasizing the importance of estimating soil organic carbon and soil carbon stock for soil quality. Soil pollution and elemental contents are addressed, focusing on the prediction of potentially toxic elements concentrations in soil, strongly relevant for environmental monitoring. Infrared spectroscopy emerges as a valuable tool for rapid and non-hazardous elemental content assessment. Soil physical properties prediction, traditionally limited to soil texture analysis, is extended through the application of novel approaches, shedding light on the broader potential of this technology for soil quality assessment. The ongoing developments in statistical modeling and technological innovation are also showcased, mainly focused on machine learning methods. Lastly, the importance of soil spectral libraries is emphasized, such as the Global Soil Spectral Calibration Library and Estimation Service, and Brazilian Soil Spectral Library. In conclusion, infrared spectroscopy has become an important tool in soil assessment, offering a multitude of applications across environmental and agricultural contexts. This review underscores the growing potential of this technology in advancing the standardization and reproducibility of sustainable soil assessment procedures, ensuring a brighter future for soil science.

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Performance of mid infrared spectroscopy to predict nutrients for agricultural soils in selected areas of Ethiopia

Cited by 8 publications

References 21 publications

Cation Exchange Capacity in Grazing Systems and a Case Study for Quantification by Hyperspectral Imaging

Cation Exchange Capacity in Grazing Systems and a Case Study for Quantification by Hyperspectral Imaging

Comparison of Si‐GA‐PLS and Si‐CARS‐PLS build algorithms for quantitation of total polyphenols in black tea using the spectral analytical system

Recent Trends on the use of Infrared Spectroscopy for Soil Assessment

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