Abstract
ABSTRACT Soil reflectance spectroscopy has become an innovative method for soil property quantification supplying data for studies in soil fertility, soil classification, digital soil mapping, while reducing laboratory time and applying a clean technology. This paper describes the implementation of a Graphical User Interface (GUI) using R named AlradSpectra. It contains several tools to process spectroscopic data and generate models to predict soil properties. The GUI was developed to accomplish tasks such as perform a large range of spectral preprocessing [...]
Highlights
Soil reflectance spectroscopy has made it possible to study soil fertility, granulometry quantification, soil class discrimination, while reducing laboratory time, as well as the use of chemical products (Demattê et al, 2019; Moura-Bueno et al, 2019)
This paper describes the implementation of a Graphical User Interface (GUI) using R named AlradSpectra
In the partial least squares regression (PLSR) models, the performances were similar to multiple linear regression (MLR) and support vector machines (SVM)
Summary
Soil reflectance spectroscopy has made it possible to study soil fertility, granulometry quantification, soil class discrimination, while reducing laboratory time, as well as the use of chemical products (Demattê et al, 2019; Moura-Bueno et al, 2019). Spectroscopy in the visible (Vis: 400-700 nm), near infrared (NIR: 701-1100 nm), and short-wave infrared (SWIR: 1101-2500 nm) regions of the electromagnetic spectrum associated with chemometric methods has allowed the quantification of physical, chemical, and mineralogical soil properties (Viscarra Rossel and Behrens, 2010) This technique has become a well-established method to assess soil properties rapidly and accurately in the laboratory (Ben Dor et al, 2015), with the possibility of predicting several properties in just one spectral reading, facilitating data acquisition from large amounts of samples, and without the use of environmentally hazardous chemicals (Dotto et al, 2016, 2018; Demattê et al, 2019). The application of linear regression, ordinary least-squares regression, data mining, and machine learning algorithms are examples of modeling methods
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
