Estimating soil organic carbon of sown biodiverse permanent pastures in Portugal using near infrared spectral data and artificial neural networks

Geoderma ◽  
2021 ◽  
Vol 404 ◽  
pp. 115387
Author(s):  
Tiago G. Morais ◽  
Camila Tufik ◽  
Ana E. Rato ◽  
Nuno R. Rodrigues ◽  
Ivo Gama ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Spectral Data ◽  
Near Infrared ◽  
Infrared Spectral ◽  
Artificial Neural

scholarly journals Analysis of the Oil Content of Rapeseed Using Artificial Neural Networks Based on Near Infrared Spectral Data

2014 ◽  
Vol 2014 ◽  
pp. 1-5 ◽  
Author(s):  
Dazuo Yang ◽  
Hao Li ◽  
Chenchen Cao ◽  
Fudi Chen ◽  
Yibing Zhou ◽  
...  
Keyword(s):  
Neural Networks ◽  
Spectral Data ◽  
Oil Content ◽  
Near Infrared ◽  
Nonlinear Method ◽  
Practical Applications ◽  
Infrared Spectral ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Analytical Approaches

The oil content of rapeseed is a crucial property in practical applications. In this paper, instead of traditional analytical approaches, an artificial neural network (ANN) method was used to analyze the oil content of 29 rapeseed samples based on near infrared spectral data with different wavelengths. Results show that multilayer feed-forward neural networks with 8 nodes (MLFN-8) are the most suitable and reasonable mathematical model to use, with an RMS error of 0.59. This study indicates that using a nonlinear method is a quick and easy approach to analyze the rapeseed oil’s content based on near infrared spectral data.

scholarly journals Application of artificial neural networks to estimate soil organic carbon in a high-organic-matter Mollisol

2017 ◽  
Vol 7 ◽  
Author(s):  
Rocío Moreno ◽  
Andrea Inés Irigoyen ◽  
María Gloria Monterubbianesi ◽  
Guillermo Alberto Studdert
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Crop Yields ◽  
Categorical Variables ◽  
Buenos Aires Province ◽  
Organic C ◽  
C Cycle ◽  
Artificial Neural

Soil organic carbon (SOC) has a key role in the global carbon (C) cycle. The complex relationships among the components of C cycle makes difficult the modelling of SOC variation. Artificial neural networks (ANN) are models capable to determine interrelationships based on information. The objective was to develop and evaluate models based on the ANN technique to estimate the SOC in Mollisols of the Southeastern of Buenos Aires Province, Argentina (SEBA). Data from three long term experiments was used. Management and meteorological variables were selected as input. Management information included numerical variables (initial SOC (SOCI); number of years from the beginning of the experiment (Year), proportion of soybean in the crop sequence; (Prop soybean); crop yields (Yield), proportion of cropping in the crop rotation (Prop agri), and categorical variables (Crop, Tillage). In addition, two meteorological inputs (minimum (Tmin) and mean air temperature (Tmed)), were selected. The ANNs were adequate to estimate SOC in the upper 0.20 m of Mollisols of the SEBA. The model with the best performance included six management variables (SOCI, Year, Prop soybean, Tillage, Yield, Prop agri) and one meteorological variable (Tmin), all of them easily available and with low level of uncertainty. Soil organic C changes related to soil use in the SEBA could be satisfactorily estimated using an ANN developed with simple and easily available input variables. Artificial neural network technique appears as a valuable tool to develop robust models to help predicting SOC changes.

Building a near infrared spectral library for soil organic carbon estimation in the Limpopo National Park, Mozambique

Geoderma ◽  
2012 ◽  
Vol 183-184 ◽  
pp. 41-48 ◽  
Author(s):  
A.H. Cambule ◽  
D.G. Rossiter ◽  
J.J. Stoorvogel ◽  
E.M.A. Smaling
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
National Park ◽  
Near Infrared ◽  
Spectral Library ◽  
Infrared Spectral

scholarly journals Age grading An. gambiae and An. arabiensis using near infrared spectra and artificial neural networks

PLoS ONE ◽  
2019 ◽  
Vol 14 (8) ◽  
pp. e0209451 ◽  
Author(s):  
Masabho P. Milali ◽  
Maggy T. Sikulu-Lord ◽  
Samson S. Kiware ◽  
Floyd E. Dowell ◽  
George F. Corliss ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Infrared Spectra ◽  
Near Infrared ◽  
Near Infrared Spectra ◽  
Artificial Neural ◽  
Age Grading
Sign in / Sign up

Export Citation Format

Share Document