Using vis-NIRS and Machine Learning methods to diagnose sugarcane soil chemical properties

Knowing chemical soil properties might be determinant in crop management and total yield production. Traditional soil properties estimation approaches are time-consuming and require complex lab setups, refraining farmers from promptly taking steps towards optimal practices in their crops. Soil pr...

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Main Authors:	Delgadillo Durana, Diego A., Vargas García, Cesar A., Varón Ramíreza, Viviana M., Calderón, Francisco C., Montenegroa, Andrea C., Reyes Herreraa, Paula H.
Format:	article
Language:	Inglés
Published:	ArXiv 2025
Subjects:	Preparación del suelo - F07 Saccharum officinarum Análisis del suelo Aprendizaje automático Transitorios http://aims.fao.org/aos/agrovoc/c_6727 http://aims.fao.org/aos/agrovoc/c_7198 http://aims.fao.org/aos/agrovoc/c_49834
Online Access:	https://arxiv.org/abs/2012.12995 http://hdl.handle.net/20.500.12324/41001

id	RepoAGROSAVIA41001
record_format	dspace
institution	Corporación Colombiana de Investigación Agropecuaria
collection	Repositorio AGROSAVIA
language	Inglés
topic	Preparación del suelo - F07 Saccharum officinarum Análisis del suelo Aprendizaje automático Transitorios http://aims.fao.org/aos/agrovoc/c_6727 http://aims.fao.org/aos/agrovoc/c_7198 http://aims.fao.org/aos/agrovoc/c_49834
spellingShingle	Preparación del suelo - F07 Saccharum officinarum Análisis del suelo Aprendizaje automático Transitorios http://aims.fao.org/aos/agrovoc/c_6727 http://aims.fao.org/aos/agrovoc/c_7198 http://aims.fao.org/aos/agrovoc/c_49834 Delgadillo Durana, Diego A. Vargas García, Cesar A. Varón Ramíreza, Viviana M. Calderón, Francisco C. Montenegroa, Andrea C. Reyes Herreraa, Paula H. Using vis-NIRS and Machine Learning methods to diagnose sugarcane soil chemical properties
description	Knowing chemical soil properties might be determinant in crop management and total yield production. Traditional soil properties estimation approaches are time-consuming and require complex lab setups, refraining farmers from promptly taking steps towards optimal practices in their crops. Soil properties estimation from its spectral signals, vis-NIRS, emerged as a lowcost, non-invasive, and non-destructive alternative. Current approaches use mathematical and statistical techniques, avoiding machine learning frameworks. This proposal uses vis-NIRS in sugarcane soils and machine learning techniques such as three regression and six classification methods. The scope is to assess performance in predicting and inferring categories of common soil properties (pH, soil organic matter OM, Ca, Na, K, and Mg), evaluated by the most common metrics. We use regression to estimate properties and classification to assess soil property status. In both cases, we achieved comparable performance on similar setups reported in the literature for property estimation for pH(R2=0.8, =0.89), OM(R2=0.37, =0.63), Ca(R2=0.54, =0.74), Mg(R2=0.44, =0.66) in the validation set.
format	article
author	Delgadillo Durana, Diego A. Vargas García, Cesar A. Varón Ramíreza, Viviana M. Calderón, Francisco C. Montenegroa, Andrea C. Reyes Herreraa, Paula H.
author_facet	Delgadillo Durana, Diego A. Vargas García, Cesar A. Varón Ramíreza, Viviana M. Calderón, Francisco C. Montenegroa, Andrea C. Reyes Herreraa, Paula H.
author_sort	Delgadillo Durana, Diego A.
title	Using vis-NIRS and Machine Learning methods to diagnose sugarcane soil chemical properties
title_short	Using vis-NIRS and Machine Learning methods to diagnose sugarcane soil chemical properties
title_full	Using vis-NIRS and Machine Learning methods to diagnose sugarcane soil chemical properties
title_fullStr	Using vis-NIRS and Machine Learning methods to diagnose sugarcane soil chemical properties
title_full_unstemmed	Using vis-NIRS and Machine Learning methods to diagnose sugarcane soil chemical properties
title_sort	using vis-nirs and machine learning methods to diagnose sugarcane soil chemical properties
publisher	ArXiv
publishDate	2025
url	https://arxiv.org/abs/2012.12995 http://hdl.handle.net/20.500.12324/41001
work_keys_str_mv	AT delgadilloduranadiegoa usingvisnirsandmachinelearningmethodstodiagnosesugarcanesoilchemicalproperties AT vargasgarciacesara usingvisnirsandmachinelearningmethodstodiagnosesugarcanesoilchemicalproperties AT varonramirezavivianam usingvisnirsandmachinelearningmethodstodiagnosesugarcanesoilchemicalproperties AT calderonfranciscoc usingvisnirsandmachinelearningmethodstodiagnosesugarcanesoilchemicalproperties AT montenegroaandreac usingvisnirsandmachinelearningmethodstodiagnosesugarcanesoilchemicalproperties AT reyesherreraapaulah usingvisnirsandmachinelearningmethodstodiagnosesugarcanesoilchemicalproperties
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spelling	RepoAGROSAVIA410012025-06-17T03:01:50Z Using vis-NIRS and Machine Learning methods to diagnose sugarcane soil chemical properties Using vis-NIRS and Machine Learning methods to diagnose sugarcane soil chemical properties Delgadillo Durana, Diego A. Vargas García, Cesar A. Varón Ramíreza, Viviana M. Calderón, Francisco C. Montenegroa, Andrea C. Reyes Herreraa, Paula H. Preparación del suelo - F07 Saccharum officinarum Análisis del suelo Aprendizaje automático Transitorios http://aims.fao.org/aos/agrovoc/c_6727 http://aims.fao.org/aos/agrovoc/c_7198 http://aims.fao.org/aos/agrovoc/c_49834 Knowing chemical soil properties might be determinant in crop management and total yield production. Traditional soil properties estimation approaches are time-consuming and require complex lab setups, refraining farmers from promptly taking steps towards optimal practices in their crops. Soil properties estimation from its spectral signals, vis-NIRS, emerged as a lowcost, non-invasive, and non-destructive alternative. Current approaches use mathematical and statistical techniques, avoiding machine learning frameworks. This proposal uses vis-NIRS in sugarcane soils and machine learning techniques such as three regression and six classification methods. The scope is to assess performance in predicting and inferring categories of common soil properties (pH, soil organic matter OM, Ca, Na, K, and Mg), evaluated by the most common metrics. We use regression to estimate properties and classification to assess soil property status. In both cases, we achieved comparable performance on similar setups reported in the literature for property estimation for pH(R2=0.8, =0.89), OM(R2=0.37, =0.63), Ca(R2=0.54, =0.74), Mg(R2=0.44, =0.66) in the validation set. 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Using vis-NIRS and Machine Learning methods to diagnose sugarcane soil chemical properties

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