Detecting Changes in Soil Fertility Properties Using Multispectral UAV Images and Machine Learning in Central Peru
Remote sensing is essential in precision agriculture as this approach provides high-resolution information on the soil's physical and chemical parameters for detailed decision making. Globally, technologies such as remote sensing and machine learning are increasingly being used to infer these parame...
| Autores principales: | , , , , , , , , , |
|---|---|
| Formato: | info:eu-repo/semantics/article |
| Lenguaje: | Inglés |
| Publicado: |
MDPI
2025
|
| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.12955/2681 https://doi.org/10.3390/agriengineering7030070 |
| _version_ | 1855028533537013760 |
|---|---|
| author | Enriquez Pinedo, Lucia Carolina Ortega Quispe, Kevin Abner Ccopi Trucios, Dennis Rios Chavarria, Claudia Sofía Urquizo Barrera, Julio Patricio Rosales, Solanch Rosy Alejandro Mendez, Lidiana Rene Oliva Cruz, Manuel Barboza Castillo, Elgar Pizarro Carcausto , Samuel Edwin |
| author_browse | Alejandro Mendez, Lidiana Rene Barboza Castillo, Elgar Ccopi Trucios, Dennis Enriquez Pinedo, Lucia Carolina Oliva Cruz, Manuel Ortega Quispe, Kevin Abner Patricio Rosales, Solanch Rosy Pizarro Carcausto , Samuel Edwin Rios Chavarria, Claudia Sofía Urquizo Barrera, Julio |
| author_facet | Enriquez Pinedo, Lucia Carolina Ortega Quispe, Kevin Abner Ccopi Trucios, Dennis Rios Chavarria, Claudia Sofía Urquizo Barrera, Julio Patricio Rosales, Solanch Rosy Alejandro Mendez, Lidiana Rene Oliva Cruz, Manuel Barboza Castillo, Elgar Pizarro Carcausto , Samuel Edwin |
| author_sort | Enriquez Pinedo, Lucia Carolina |
| collection | Repositorio INIA |
| description | Remote sensing is essential in precision agriculture as this approach provides high-resolution information on the soil's physical and chemical parameters for detailed decision making. Globally, technologies such as remote sensing and machine learning are increasingly being used to infer these parameters. This study evaluates soil fertility changes and compares them with previous fertilization inputs using high-resolution multispectral imagery and in situ measurements. A UAV-captured image was used to predict the spatial distribution of soil parameters, generating fourteen spectral indices and a digital surface model (DSM) from 103 soil plots across 49.83 hectares. Machine learning algorithms, including classification and regression trees (CART) and random forest (RF), modeled the soil parameters (N-ppm, P-ppm, K-ppm, OM%, and EC-mS/m). The RF model outperformed others, with R² values of 72% for N, 83% for P, 87% for K, 85% for OM, and 70% for EC in 2023. Significant spatiotemporal variations were observed between 2022 and 2023, including an increase in P (14.87 ppm) and a reduction in EC (-0.954 mS/m). High-resolution UAV imagery combined with machine learning proved highly effective for monitoring soil fertility. This approach, tailored to the Peruvian Andes, integrates spectral indices and field-collected data, offering innovative tools to optimize fertilization practices, address soil management challenges, and merge modern technology with traditional methods for sustainable agricultural practices. |
| format | info:eu-repo/semantics/article |
| id | INIA2681 |
| institution | Institucional Nacional de Innovación Agraria |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | INIA26812025-03-24T05:08:33Z Detecting Changes in Soil Fertility Properties Using Multispectral UAV Images and Machine Learning in Central Peru Enriquez Pinedo, Lucia Carolina Ortega Quispe, Kevin Abner Ccopi Trucios, Dennis Rios Chavarria, Claudia Sofía Urquizo Barrera, Julio Patricio Rosales, Solanch Rosy Alejandro Mendez, Lidiana Rene Oliva Cruz, Manuel Barboza Castillo, Elgar Pizarro Carcausto , Samuel Edwin fertility soil mapping CART random forest precision agriculture https://purl.org/pe-repo/ocde/ford#4.01.04 Fertilidad del suelo; Cartografía; Teledetección; Agricultura de precisión Remote sensing is essential in precision agriculture as this approach provides high-resolution information on the soil's physical and chemical parameters for detailed decision making. Globally, technologies such as remote sensing and machine learning are increasingly being used to infer these parameters. This study evaluates soil fertility changes and compares them with previous fertilization inputs using high-resolution multispectral imagery and in situ measurements. A UAV-captured image was used to predict the spatial distribution of soil parameters, generating fourteen spectral indices and a digital surface model (DSM) from 103 soil plots across 49.83 hectares. Machine learning algorithms, including classification and regression trees (CART) and random forest (RF), modeled the soil parameters (N-ppm, P-ppm, K-ppm, OM%, and EC-mS/m). The RF model outperformed others, with R² values of 72% for N, 83% for P, 87% for K, 85% for OM, and 70% for EC in 2023. Significant spatiotemporal variations were observed between 2022 and 2023, including an increase in P (14.87 ppm) and a reduction in EC (-0.954 mS/m). High-resolution UAV imagery combined with machine learning proved highly effective for monitoring soil fertility. This approach, tailored to the Peruvian Andes, integrates spectral indices and field-collected data, offering innovative tools to optimize fertilization practices, address soil management challenges, and merge modern technology with traditional methods for sustainable agricultural practices. The Ministry of Agrarian Development and Irrigation (MIDAGRI) of the Peruvian Government provided funding for this study through the project “Creación del servicio de agricultura de precisión en los Departamentos de Lambayeque, Huancavelica, Ucayali y San Martín 4 Departamentos" (grant number CUI 2449640). It also received support from the Vice-Rectorate for Research of the Universidad Nacional del Amazonas Toribio Rodríguez de Mendoza—UNTRM. Special thanks are extended to the collaborators involved in field data collection and assistants of the Precision Agriculture Project (CUI 2449640) as well as other research programs of the “Estación Experimental Agraria Santa Ana”, INIA. 2025-03-24T05:08:33Z 2025-03-24T05:08:33Z 2025-03-06 info:eu-repo/semantics/article Enriquez, L.; Ortega, K.; Ccopi, D.; Rios, C.; Urquizo, J.; Patricio, S.; Alejandro, L.; Oliva-Cruz, M.; Barboza, E.; Pizarro, S. Detecting Changes in Soil Fertility Properties Using Multispectral UAV Images and Machine Learning in Central Peru. AgriEngineering 2025, 7, 70. https://doi.org/10.3390/agriengineering7030070 http://hdl.handle.net/20.500.12955/2681 https://doi.org/10.3390/agriengineering7030070 eng AgriEngineering info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by/4.0/ application/pdf application/pdf MDPI CH Instituto Nacional de Innovación Agraria Repositorio Institucional - INIA |
| spellingShingle | fertility soil mapping CART random forest precision agriculture https://purl.org/pe-repo/ocde/ford#4.01.04 Fertilidad del suelo; Cartografía; Teledetección; Agricultura de precisión Enriquez Pinedo, Lucia Carolina Ortega Quispe, Kevin Abner Ccopi Trucios, Dennis Rios Chavarria, Claudia Sofía Urquizo Barrera, Julio Patricio Rosales, Solanch Rosy Alejandro Mendez, Lidiana Rene Oliva Cruz, Manuel Barboza Castillo, Elgar Pizarro Carcausto , Samuel Edwin Detecting Changes in Soil Fertility Properties Using Multispectral UAV Images and Machine Learning in Central Peru |
| title | Detecting Changes in Soil Fertility Properties Using Multispectral UAV Images and Machine Learning in Central Peru |
| title_full | Detecting Changes in Soil Fertility Properties Using Multispectral UAV Images and Machine Learning in Central Peru |
| title_fullStr | Detecting Changes in Soil Fertility Properties Using Multispectral UAV Images and Machine Learning in Central Peru |
| title_full_unstemmed | Detecting Changes in Soil Fertility Properties Using Multispectral UAV Images and Machine Learning in Central Peru |
| title_short | Detecting Changes in Soil Fertility Properties Using Multispectral UAV Images and Machine Learning in Central Peru |
| title_sort | detecting changes in soil fertility properties using multispectral uav images and machine learning in central peru |
| topic | fertility soil mapping CART random forest precision agriculture https://purl.org/pe-repo/ocde/ford#4.01.04 Fertilidad del suelo; Cartografía; Teledetección; Agricultura de precisión |
| url | http://hdl.handle.net/20.500.12955/2681 https://doi.org/10.3390/agriengineering7030070 |
| work_keys_str_mv | AT enriquezpinedoluciacarolina detectingchangesinsoilfertilitypropertiesusingmultispectraluavimagesandmachinelearningincentralperu AT ortegaquispekevinabner detectingchangesinsoilfertilitypropertiesusingmultispectraluavimagesandmachinelearningincentralperu AT ccopitruciosdennis detectingchangesinsoilfertilitypropertiesusingmultispectraluavimagesandmachinelearningincentralperu AT rioschavarriaclaudiasofia detectingchangesinsoilfertilitypropertiesusingmultispectraluavimagesandmachinelearningincentralperu AT urquizobarrerajulio detectingchangesinsoilfertilitypropertiesusingmultispectraluavimagesandmachinelearningincentralperu AT patriciorosalessolanchrosy detectingchangesinsoilfertilitypropertiesusingmultispectraluavimagesandmachinelearningincentralperu AT alejandromendezlidianarene detectingchangesinsoilfertilitypropertiesusingmultispectraluavimagesandmachinelearningincentralperu AT olivacruzmanuel detectingchangesinsoilfertilitypropertiesusingmultispectraluavimagesandmachinelearningincentralperu AT barbozacastilloelgar detectingchangesinsoilfertilitypropertiesusingmultispectraluavimagesandmachinelearningincentralperu AT pizarrocarcaustosamueledwin detectingchangesinsoilfertilitypropertiesusingmultispectraluavimagesandmachinelearningincentralperu |