Using vis-NIRS and Machine Learning methods to diagnose sugarcane soil
chemical properties
- URL: http://arxiv.org/abs/2012.12995v2
- Date: Wed, 20 Jan 2021 21:34:30 GMT
- Title: Using vis-NIRS and Machine Learning methods to diagnose sugarcane soil
chemical properties
- Authors: Diego A. Delgadillo-Duran, Cesar A. Vargas-Garc\'ia, Viviana M.
Var\'on-Ram\'irez, Francisco Calder\'on, Andrea C. Montenegro, Paula H.
Reyes-Herrera
- Abstract summary: Knowing chemical soil properties might be determinant in crop management and total yield production.
Traditional property estimation approaches are time-consuming and require complex lab setups.
Property estimation from spectral signals(vis-NIRS), emerged as a low-cost, non-invasive, and non-destructive alternative.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Knowing chemical soil properties might be determinant in crop management and
total yield production. Traditional property estimation approaches are
time-consuming and require complex lab setups, refraining farmers from taking
steps towards optimal practices in their crops promptly. Property estimation
from spectral signals(vis-NIRS), emerged as a low-cost, non-invasive, and
non-destructive alternative. Current approaches use mathematical and
statistical techniques, avoiding machine learning framework. Here we propose
both regression and classification with machine learning techniques to assess
performance in the prediction and infer categories of common soil properties
(pH, soil organic matter, Ca, Na, K, and Mg), evaluated by the most common
metrics. In sugarcane soils, we use regression to estimate properties and
classification to assess soil's property status and report the direct relation
between spectra bands and direct measure of certain properties. In both cases,
we achieved similar performance on similar setups reported in the literature.
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