PubAg

Main content area

Green methodology for soil organic matter analysis using a national near infrared spectral library in tandem with learning machine

Author:
de Santana, Felipe B., de Souza, André M., Poppi, Ronei J.
Source:
The Science of the total environment 2019 v.658 pp. 895-900
ISSN:
0048-9697
Subject:
algorithms, artificial intelligence, chromium, oxidation, potassium, precision agriculture, prediction, soil fertility, soil organic matter, soil sampling, wastes, Brazil
Abstract:
Precision agriculture requires faster and automatic responses for fertility parameters, especially regarding soil organic matter (SOM). In Brazil, the standard methodology for SOM determination is a wet procedure based on the oxidation of the sample by an excess of potassium dichromate based on Walkley–Black method. This methodology has serious drawbacks, since, at a national level, generates approximately 600,000 L/year of toxic acid waste containing Cr3+ and possibly Cr6+, besides time consuming and expensive. Herein, we present a faster green methodology that can eliminate the generation of these hazardous wastes and reduces the costs of analysis by approximately 80%, democratizing the soil fertility information and increasing the productivity. The methodology is based on the use of a national near infrared spectral library with approximately 43,000 samples and learning machine data analysis based on a random forest algorithm. The methodology was validated by submitting the prediction results of 12 blind soil samples to a proficiency assay used for fertility soil laboratories qualification, receiving the maximum quality excellence index, indicating that it is suitable for use in routine analysis.
Agid:
6264571