Algoritmos de aprendizaje de máquina para la predicción de propiedades fisicoquímicas del suelo mediante información espectral: una revisión sistemática

Vargas-Zapata, Mateo; Sierra, Marisol Medina; Galeano-Vasco, Luis; Muñoz, Mario

doi:10.19053/20278306.v12.n1.2022.14212

Cited by 4 publications

(6 citation statements)

References 44 publications

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“…Table 1 shows the elements in the search. This is a quantitative perspective, different from other studies with a qualitative approach (Cardona-Arbeláez et al, 2019;Macías-Rojas et al, 2022;Vargas-Zapata et al, 2022). Mapping, evolution, and application trends in co-citation analysis: a scientometric approach…”

Section: Methodsmentioning

confidence: 93%

Mapping, evolution, and application trends in co-citation analysis: a scientometric approach

Robledo-Giraldo,

Figueroa-Camargo,

Zuluaga-Rojas

et al. 2023

rev. investig. desarro. innov

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This study aims to explore the mapping, evolution, and application trends of co-citation analysis. To accomplish this goal, a comprehensive search was conducted using Scopus and Web of Science, resulting in 1298 relevant studies. Further analysis was conducted on scientific production, country, author, journal, and network data. The Tree of Science algorithm was applied to demonstrate the development of co-citation analysis. The results make three significant contributions to scientometric research: Firstly, a scientific mapping is presented highlighting the scientific output, main journals, and key researchers; secondly, the advancements of co-citation analysis are presented through the Tree of Science metaphor; lastly, the study identifies the three main subtopics within co-citation analysis through citation analysis. These findings will assist researchers and librarians in recognizing the crucial contributions and applications of co-citation analysis.

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Section: Methodsmentioning

confidence: 93%

Mapping, evolution, and application trends in co-citation analysis: a scientometric approach

Robledo-Giraldo,

Figueroa-Camargo,

Zuluaga-Rojas

et al. 2023

rev. investig. desarro. innov

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“…By utilizing HSI images, soil reflectance is captured across multiple spectral bands, allowing for the detection of interactions among soil chemical elements. This novel approach has the potential to overcome some of the limitations associated with traditional laboratory methods, facilitating a more precise and detailed assessment of CEC in different soil areas [15].…”

Section: Methods Used For the Determination Of Cecmentioning

confidence: 99%

“…This approach reduces the need for traditional laboratory analysis and provides faster results, which can be particularly useful in situations where frequent monitoring of CEC is required over large areas or in resource-limited conditions [17]. It is an emerging technology that successfully predicts the physicochemical properties of soils [15], including CEC, pH, clay content, carbon content, total nitrogen content, and other elements, by correlating spectral data extracted from images with their chemical and physical concentrations [18][19][20].…”

Section: Spectroscopy An Alternative To Assess Soil Fertilitymentioning

confidence: 99%

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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“…Te best performing RF model was the one that used the transformation of the frst derivative of the SG of the spectral data and the transformation for �� OC √ . Based on a literature review, Vargas et al [19] concluded that the RF and SVM algorithms are useful for determining the OC in soil. Tese algorithms have also been studied by other authors.…”

Section: Statistical Modelsmentioning

confidence: 99%

Use of Machine Learning Models for Prediction of Organic Carbon and Nitrogen in Soil from Hyperspectral Imagery in Laboratory

Monsalve,

Cerón-Muñoz,

Galeano-Vasco

et al. 2023

Journal of Spectroscopy

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Organic carbon and total nitrogen are essential nutrients for plant growth. The presence of these nutrients at acceptable levels can create an optimal environment for the development of crops of interest. The application of spectroscopic techniques and the use of machine learning algorithms have made it possible to calibrate models capable of predicting the number of elements present in the soil. One of these techniques is hyperspectral imaging, which captures portions of the electromagnetic spectrum where the materials present in the soil can be differentiated due to the vibrations of chemical bonds. The objective of this research is to use statistical models to predict OC and N in soils from hyperspectral images. Transformations were applied to spectral and chemical data and the models used were Random Forest (RF) and Support Vector Machine (SVM). To select the best model, the values of the coefficient of determination (R2), root mean square error of prediction (RMSEP), and the ratio of performance to deviation (RPD) were considered. For OC, the values found for the RF model were an R2 of 0.87, an RMSEP of 0.10, and an RPD of 6.74; the SVM model presented an R2 of 0.92, an RMSEP of 0.20, and an RPD of 3.56. For the variable N, the values found for the RF model were an R2 of 0.79, an RMSEP of 0.03, and an RPD of 5.44; for the SVM model, they were an R2 of 0.87, an RMSEP of 0.08, and an RPD of 2.76. The RF model showed a better fit for both variables. The SVM model also produced acceptable results. The results show that machine learning models are a good alternative for analysing soil-related variables.

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Algoritmos de aprendizaje de máquina para la predicción de propiedades fisicoquímicas del suelo mediante información espectral: una revisión sistemática

Cited by 4 publications

References 44 publications

Mapping, evolution, and application trends in co-citation analysis: a scientometric approach

Mapping, evolution, and application trends in co-citation analysis: a scientometric approach

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

Use of Machine Learning Models for Prediction of Organic Carbon and Nitrogen in Soil from Hyperspectral Imagery in Laboratory

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