Comparative Analysis of Machine and Deep Learning Models for Soil Properties Prediction from Hyperspectral Visual Band Article Swipe
Dristi Datta
,
Manoranjan Paul
,
Manzur Murshed
,
Shyh Wei Teng
,
Leigh M. Schmidtke
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/environments10050077
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/environments10050077
Estimating various properties of soil, including moisture, carbon, and nitrogen, is crucial for studying their correlation with plant health and food production. However, conventional methods such as oven-drying and chemical analysis are laborious, expensive, and only feasible for a limited land area. With the advent of remote sensing technologies like multi/hyperspectral imaging, it is now possible to predict soil properties non-invasive and cost-effectively for a large expanse of bare land. Recent research shows the possibility of predicting those soil contents from a wide range of hyperspectral data using good prediction algorithms. However, these kinds of hyperspectral sensors are expensive and not widely available. Therefore, this paper investigates different machine and deep learning techniques to predict soil nutrient properties using only the red (R), green (G), and blue (B) bands data to propose a suitable machine/deep learning model that can be used as a rapid soil test. Another objective of this research is to observe and compare the prediction accuracy in three cases i. hyperspectral band ii. full spectrum of the visual band, and iii. three-channel of RGB band and provide a guideline to the user on which spectrum information they should use to predict those soil properties. The outcome of this research helps to develop a mobile application that is easy to use for a quick soil test. This research also explores learning-based algorithms with significant feature combinations and their performance comparisons in predicting soil properties from visual band data. For this, we also explore the impact of dimensional reduction (i.e., principal component analysis) and transformations (i.e., empirical mode decomposition) of features. The results show that the proposed model can comparably predict the soil contents from the three-channel RGB data.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/environments10050077
- https://www.mdpi.com/2076-3298/10/5/77/pdf?version=1683202061
- OA Status
- gold
- Cited By
- 27
- References
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- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4372346657
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Raw OpenAlex JSON
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https://openalex.org/W4372346657Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/environments10050077Digital Object Identifier
- Title
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Comparative Analysis of Machine and Deep Learning Models for Soil Properties Prediction from Hyperspectral Visual BandWork 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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2023-05-04Full publication date if available
- Authors
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Dristi Datta, Manoranjan Paul, Manzur Murshed, Shyh Wei Teng, Leigh M. SchmidtkeList of authors in order
- Landing page
-
https://doi.org/10.3390/environments10050077Publisher landing page
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https://www.mdpi.com/2076-3298/10/5/77/pdf?version=1683202061Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2076-3298/10/5/77/pdf?version=1683202061Direct OA link when available
- Concepts
-
Hyperspectral imaging, RGB color model, Computer science, Machine learning, Artificial intelligence, Remote sensing, Deep learning, Environmental science, GeologyTop concepts (fields/topics) attached by OpenAlex
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27Total citation count in OpenAlex
- Citations by year (recent)
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2025: 16, 2024: 10, 2023: 1Per-year citation counts (last 5 years)
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96Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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