- Title
- Comparative analysis of machine and deep learning models for soil properties prediction from hyperspectral visual band
- Creator
- Datta, Dristi; Paul, Manoranjan; Murshed, Manzur; Teng, Shyh Wei; Schmidtke, Leigh
- Date
- 2023
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/194236
- Identifier
- vital:18322
- Identifier
-
https://doi.org/10.3390/environments10050077
- Identifier
- ISSN:2076-3298
- Abstract
- 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.
- Publisher
- MDPI AG
- Relation
- Environments Vol. 10, no. 5 (2023), p. 77
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- https://creativecommons.org/licenses/by/4.0/
- Rights
- Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland
- Rights
- Open Access
- Subject
- Accuracy; Algorithms; Applications programs; Carbon; Carbon content; Chemical analysis; Comparative analysis; Datasets; Deep learning; Digital cameras; Drying ovens; Empirical analysis; Empirical mode decomposition; Food plants; Food production; Geospatial data; Hyperspectral imaging; Learning algorithms; Learning-based algorithms; Machine learning; Microelectromechanical systems; Microorganisms; Mobile computing; Moisture effects; Noise; Nutrients; Performance assessment; Performance prediction; Predictions; Principal component analysis; Principal components analysis; Production methods; Rapid soil test; Remote sensing; RGB band; Satellites; Sensors; Soil moisture; Soil nutrients; Soil properties; Soil testing; Soils; 4104 Environmental management; 4105 Pollution and contamination
- Full Text
- Reviewed
- Funder
- This research was funded by Soil CRC Australia (No. 2.S.006 PhD Scholarship), with contribution from Charles Sturt University
- Hits: 3500
- Visitors: 3368
- Downloads: 31
| Thumbnail | File | Description | Size | Format | |||
|---|---|---|---|---|---|---|---|
| View Details Download | SOURCE1 | Published version | 659 KB | Adobe Acrobat PDF | View Details Download |