Mapping several soil types using hyperspectral datasets and advanced machine learning methods Article Swipe
Amol D. Vibhute
,
K. V. Kale
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.rio.2023.100503
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1016/j.rio.2023.100503
Specifying surface soil types is vital for healthy agricultural management to enhance food production. Recent advancements in machine learning are essential in soil science, quantitatively predicting and classifying soil types. The current study concentrates on generating and testing the spectral library to classify surface soil types. It deals with advanced machine learning methods in evaluating soil physicochemical properties and soil type classification using Spectroradiometer and satellite imagery. The proposed methodology tests Phulambri Tehsil's agricultural regions in Aurangabad district, Maharashtra, India. The soil properties determination has been accomplished using partial least square regression (PLSR) models, enabling the relationship between soil profiles and reflectance spectra. The soil type classification has been done on identified values of specific properties in sampled profiles. Machine learning methods such as PLSR, support vector machine (SVM), and spectral angle mapper (SAM), along with principal component analysis (PCA) and minimum-noise-fraction (MNF), were used. The study showed that the three major soil classes have accurately identified and mapped from satellite images using machine learning methods with more than 95% classification accuracy. The results proved to be useful for soil study in heterogeneous areas. Therefore, this study is used in precision farming to enhance food production and management.
Related Topics
Concepts
Spectroradiometer
Hyperspectral imaging
Principal component analysis
Support vector machine
Environmental science
Soil test
Artificial intelligence
Remote sensing
Machine learning
Computer science
Precision agriculture
Soil classification
Soil type
Satellite imagery
Soil science
Soil water
Reflectivity
Agriculture
Geology
Geography
Archaeology
Physics
Optics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.rio.2023.100503
- OA Status
- gold
- Cited By
- 10
- References
- 67
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4385615187
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4385615187Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.rio.2023.100503Digital Object Identifier
- Title
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Mapping several soil types using hyperspectral datasets and advanced machine learning methodsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-07-01Full publication date if available
- Authors
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Amol D. Vibhute, K. V. KaleList of authors in order
- Landing page
-
https://doi.org/10.1016/j.rio.2023.100503Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.rio.2023.100503Direct OA link when available
- Concepts
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Spectroradiometer, Hyperspectral imaging, Principal component analysis, Support vector machine, Environmental science, Soil test, Artificial intelligence, Remote sensing, Machine learning, Computer science, Precision agriculture, Soil classification, Soil type, Satellite imagery, Soil science, Soil water, Reflectivity, Agriculture, Geology, Geography, Archaeology, Physics, OpticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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10Total citation count in OpenAlex
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2025: 7, 2024: 3Per-year citation counts (last 5 years)
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67Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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