Soil texture prediction with automated deep convolutional neural networks and population-based learning

Omondiagbe, Osayande Pascal; Lilburne, Linda; Licorish, Sherlock A.; MacDonell, Stephen G.

doi:10.1016/j.geoderma.2023.116521

Cited by 9 publications

(2 citation statements)

References 34 publications

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“…In recent years, rapidly developing vision-near-infrared hyperspectral technology has been widely used in soil texture content estimation to address the contradiction between the demand for big data of soil texture and high cost 8 . Depending on the spectral response relationship between soil spectral reflectance and soil texture, many researchers have used ground object spectrometry to develop soil hyperspectral technology as a conventional means of quantifying soil texture [9][10][11] . However, soil texture inversion based on a ground-object spectrometer usually obtains spotlike data with low density.…”

Section: Estimation and Mapping Of Soil Texture Content Based On Unma...mentioning

confidence: 99%

Estimation and mapping of soil texture content based on unmanned aerial vehicle hyperspectral imaging

Song,

Gao,

Song

et al. 2023

Sci Rep

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Soil texture is one of the important physical and natural properties of soil. Much of the current research focuses on soil texture monitoring using non-imaging geophysical spectrometers. However there are fewer studies utilizing unmanned aerial vehicle (UAV) hyperspectral data for soil texture monitoring. UAV mounted hyperspectral cameras can be used for quickly and accurately obtaining high-resolution spatial information of soil texture. A foundation has been laid for the realization of rapid soil texture surveys using unmanned airborne hyperspectral data without field sampling. This study selected three typical farmland areas in Huangshui Basin of Qinghai as the study area, and a total of 296 soil samples were collected. Data calibration of UAV spectra using laboratory spectra and field in situ spectra to explore the feasibility of applying laboratory soil texture models directly to field conditions. This results show that UAV hyperspectral imagery combined with machine learning can obtain a set of ideal processing methods. The pre-processing of the spectral data can obtain high accuracy of soil texture estimation and good mapping effect. The results of this study can provide effective technical support and decision-making assistance for future agricultural land planning on the Tibetan Plateau. The main innovation of this study is to establish a set of processing procedures and methods applicable to UAV hyperspectral imagery to provide data reference for monitoring soil texture in agricultural fields on the Tibetan Plateau.

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Section: Estimation and Mapping Of Soil Texture Content Based On Unma...mentioning

confidence: 99%

Estimation and mapping of soil texture content based on unmanned aerial vehicle hyperspectral imaging

Song,

Gao,

Song

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Still, few researchers have attempted to tune the CNN hyperparameters in predictive soil mapping studies, despite recent work showing the importance (e.g. Wadoux et al, 2019;Omondiagbe et al, 2023;Taghizadeh-Mehrjardi et al, 2020).…”

mentioning

confidence: 99%

Deep learning with a multi-task convolutional neural network to generate a national-scale 3D soil data product: Particle size distribution of the German agricultural soil-landscape

Ließ,

Sakhaee

2023

Preprint

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Abstract. Many soil functions and processes are controlled by the soil particle size distribution. The generated three-dimensional continuous data product, which covers the particle size fractions of sand, silt, and clay in the agricultural soil-landscape of Germany, has a spatial resolution of 100 m and a depth resolution of 1 cm. This product is an important component for predicting the effects of agricultural management practices and their adaptability to climate change, as well as for analyzing soil functions and numerous risks. The effectiveness of the convolutional neural network (CNN) algorithm in producing multidimensional, multivariate data products is demonstrated. Even though the potential of this deep learning approach to understand and model the complex soil-landscape relationship is virtually limitless, limitations are data-driven. Further research is needed to assess the required complexity and depth of the CNN and the inclusion of the landscape surrounding each soil profile.

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CYPBL: Crop Yield Prediction using Bi-Directional LSTM under PySpark interface

Chaudhary,

Pathak

2024

Multimed Tools Appl

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Soil texture prediction with automated deep convolutional neural networks and population-based learning

Cited by 9 publications

References 34 publications

Estimation and mapping of soil texture content based on unmanned aerial vehicle hyperspectral imaging

Estimation and mapping of soil texture content based on unmanned aerial vehicle hyperspectral imaging

Deep learning with a multi-task convolutional neural network to generate a national-scale 3D soil data product: Particle size distribution of the German agricultural soil-landscape

CYPBL: Crop Yield Prediction using Bi-Directional LSTM under PySpark interface

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