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Soil

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Quantitative estimation of soil properties using hybrid features and RNN variants.

Chemosphere
Estimating soil properties is important for maximizing the production of crops in sustainable agriculture. The hyperspectral data next input depends upon the previous one, and the current techniques do not take advantage of this sequential nature of ...

Robotic Assay for Drought (RoAD): an automated phenotyping system for brassinosteroid and drought responses.

The Plant journal : for cell and molecular biology
Brassinosteroids (BRs) are a group of plant steroid hormones involved in regulating growth, development, and stress responses. Many components of the BR pathway have previously been identified and characterized. However, BR phenotyping experiments ar...

Prediction of soil-bearing capacity on forest roads by statistical approaches.

Environmental monitoring and assessment
The soil-bearing capacity is one of the important criteria in dimensioning the superstructure. In Turkey, predictability of California Bearing Ratio values, which may be used in the planning and dimensioning of forest roads, of which about 26% lacks ...

Determining soil particle-size distribution from infrared spectra using machine learning predictions: Methodology and modeling.

PloS one
Accuracy of infrared (IR) models to measure soil particle-size distribution (PSD) depends on soil preparation, methodology (sedimentation, laser), settling times and relevant soil features. Compositional soil data may require log ratio (ilr) transfor...

Global soil moisture data derived through machine learning trained with in-situ measurements.

Scientific data
While soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term data...

Machine Learning Strategy for Soil Nutrients Prediction Using Spectroscopic Method.

Sensors (Basel, Switzerland)
The research presented in this paper is based on the hypothesis that the machine learning approach improves the accuracy of soil properties prediction. The correlations obtained in this research are important for understanding the overall strategy fo...

Soil Nutrient Estimation and Mapping in Farmland Based on UAV Imaging Spectrometry.

Sensors (Basel, Switzerland)
Soil nutrient is one of the most important properties for improving farmland quality and product. Imaging spectrometry has the potential for rapid acquisition and real-time monitoring of soil characteristics. This study aims to explore the preprocess...

Mapping soil salinity using a combined spectral and topographical indices with artificial neural network.

PloS one
Monitoring the status of natural and ecological resources is necessary for conservation and protection. Soil is one of the most important environmental resources in agricultural lands and natural resources. In this research study, we used Landsat 8 a...

Predictive modeling of swell-strength of expansive soils using artificial intelligence approaches: ANN, ANFIS and GEP.

Journal of environmental management
This study presents the development of new empirical prediction models to evaluate swell pressure and unconfined compression strength of expansive soils (PUCS-ES) using three soft computing methods, namely artificial neural networks (ANNs), adaptive ...

A model based on Bayesian confirmation and machine learning algorithms to aid archaeological interpretation by integrating incompatible data.

PloS one
The interpretation of archaeological features often requires a combined methodological approach in order to make the most of the material record, particularly from sites where this may be limited. In practice, this requires the consultation of differ...