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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| Formato: | article |
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ArXiv
2025
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| Acceso en línea: | https://arxiv.org/abs/2012.12995 http://hdl.handle.net/20.500.12324/41001 |
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RepoAGROSAVIA41001 |
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dspace |
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Corporación Colombiana de Investigación Agropecuaria |
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Repositorio AGROSAVIA |
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Inglés |
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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 |
| _version_ |
1842256038265356288 |
| 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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