Estimación de los niveles nutricionales de hojas de cítricos mediante análisis no destructivo aplicando técnicas de aprendizaje automático
One of the characteristics of all living beings is that adequate nutrition has a positive impact on health. In the case of plants, and specifically in fruit trees, adequate nutrition is also essential for them to grow healthy and produce fruits in the highest quantity and quality possible. Theref...
| Autores principales: | , , , , , , , , |
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| Formato: | Objeto de conferencia |
| Lenguaje: | Español |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/20.500.11939/8890 |
| _version_ | 1855492581145706496 |
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| author | Miralles, Guillem Rodríguez-Carretero, Isabel Cubero, Sergio Martínez, Marcelino Mateo, Fernando Albert, Francisco Quinones, Ana Blasco, José Gómez-Sanchis, Juan |
| author_browse | Albert, Francisco Blasco, José Cubero, Sergio Gómez-Sanchis, Juan Martínez, Marcelino Mateo, Fernando Miralles, Guillem Quinones, Ana Rodríguez-Carretero, Isabel |
| author_facet | Miralles, Guillem Rodríguez-Carretero, Isabel Cubero, Sergio Martínez, Marcelino Mateo, Fernando Albert, Francisco Quinones, Ana Blasco, José Gómez-Sanchis, Juan |
| author_sort | Miralles, Guillem |
| collection | ReDivia |
| description | One of the characteristics of all living beings is that adequate nutrition has a positive
impact on health. In the case of plants, and specifically in fruit trees, adequate nutrition is also
essential for them to grow healthy and produce fruits in the highest quantity and quality
possible. Therefore, optimal nutrition is key for any farmer. However, excessive use of fertilisers
can harm the environment and be a waste of resources for farmers. One of the keys to achieving
adequate fertilisation is an accurate diagnosis of the nutritional status of the tree. Traditionally,
this diagnosis is made by destructive ionomics analysis, which represents a high economic cost
and a delay in obtaining the results. This work proposes Vis-NIR hyperspectral imaging and
machine learning regression models to estimate the concentrations of macronutrients (N, P, K,
and Ca) and micronutrients (Mn and Fe) in citrus leaves. The methodology involved the
application of several machine learning regression methods (linear regression, partial least
squares, random forest, support vector regression, and Ada Boost). Data were normalised with
standard normal variable (SNV), and principal component analysis (PCA) was used to reduce
dimensionality. The results were promising in estimating nutrients with R2 greater than 0,50 in
all cases, especially nitrogen (R2 of 0.77). |
| format | Objeto de conferencia |
| id | ReDivia8890 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Español |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| record_format | dspace |
| spelling | ReDivia88902025-04-25T14:50:58Z Estimación de los niveles nutricionales de hojas de cítricos mediante análisis no destructivo aplicando técnicas de aprendizaje automático Miralles, Guillem Rodríguez-Carretero, Isabel Cubero, Sergio Martínez, Marcelino Mateo, Fernando Albert, Francisco Quinones, Ana Blasco, José Gómez-Sanchis, Juan N01 Agricultural engineering machine learning Spectroscopy precision agriculture Citrus One of the characteristics of all living beings is that adequate nutrition has a positive impact on health. In the case of plants, and specifically in fruit trees, adequate nutrition is also essential for them to grow healthy and produce fruits in the highest quantity and quality possible. Therefore, optimal nutrition is key for any farmer. However, excessive use of fertilisers can harm the environment and be a waste of resources for farmers. One of the keys to achieving adequate fertilisation is an accurate diagnosis of the nutritional status of the tree. Traditionally, this diagnosis is made by destructive ionomics analysis, which represents a high economic cost and a delay in obtaining the results. This work proposes Vis-NIR hyperspectral imaging and machine learning regression models to estimate the concentrations of macronutrients (N, P, K, and Ca) and micronutrients (Mn and Fe) in citrus leaves. The methodology involved the application of several machine learning regression methods (linear regression, partial least squares, random forest, support vector regression, and Ada Boost). Data were normalised with standard normal variable (SNV), and principal component analysis (PCA) was used to reduce dimensionality. The results were promising in estimating nutrients with R2 greater than 0,50 in all cases, especially nitrogen (R2 of 0.77). 2024-05-13T10:46:50Z 2024-05-13T10:46:50Z 2023 conferenceObject Miralles, G., Rodríguez-Carretero, I., Cubero, S., Martínez, M., Mateo, F., Albert, F., Quiñones, A., Blasco, J., Gómez-Sanchís, J. (2023) Estimación de los niveles nutricionales de hojas de cítricos mediante análisis no destructivo aplicando técnicas de aprendizaje automático. XII Congreso Ibérico de Agroingeniería, Sevilla, pp. 169-176. https://hdl.handle.net/20.500.11939/8890 es 2023 XII Congreso Ibérico de Agroingeniería Sevilla Este trabajo ha sido parcialmente financiado por los proyectos TED2021-130117B-C31 y C33, financiados por MCIN/AEI/1 0,13039/501100011033 y por la UE “NextGenerationEU”/PRTR, y los proyectos GVA-IVIA 52203, 52204 y la UE a través del FEDER de la Generalitat Valenciana 2021-2027. info:eu-repo/grantAgreement/ERDF/PCV 2021-2027/52203/ES/Sostenibilidad y economía circular como ejes de desarrollo del sector agrario valenciano: suelo, agua y biodiversidad/SostE-SABio info:eu-repo/grantAgreement/ERDF/PCV 2021-2027/52204/ES/Tecnología inteligente para una agricultura digital, sostenible y precisa en la comunitat valenciana/AgrIntel·ligència-CV Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ openAccess electronico |
| spellingShingle | N01 Agricultural engineering machine learning Spectroscopy precision agriculture Citrus Miralles, Guillem Rodríguez-Carretero, Isabel Cubero, Sergio Martínez, Marcelino Mateo, Fernando Albert, Francisco Quinones, Ana Blasco, José Gómez-Sanchis, Juan Estimación de los niveles nutricionales de hojas de cítricos mediante análisis no destructivo aplicando técnicas de aprendizaje automático |
| title | Estimación de los niveles nutricionales de hojas de cítricos mediante análisis no destructivo aplicando técnicas de aprendizaje automático |
| title_full | Estimación de los niveles nutricionales de hojas de cítricos mediante análisis no destructivo aplicando técnicas de aprendizaje automático |
| title_fullStr | Estimación de los niveles nutricionales de hojas de cítricos mediante análisis no destructivo aplicando técnicas de aprendizaje automático |
| title_full_unstemmed | Estimación de los niveles nutricionales de hojas de cítricos mediante análisis no destructivo aplicando técnicas de aprendizaje automático |
| title_short | Estimación de los niveles nutricionales de hojas de cítricos mediante análisis no destructivo aplicando técnicas de aprendizaje automático |
| title_sort | estimacion de los niveles nutricionales de hojas de citricos mediante analisis no destructivo aplicando tecnicas de aprendizaje automatico |
| topic | N01 Agricultural engineering machine learning Spectroscopy precision agriculture Citrus |
| url | https://hdl.handle.net/20.500.11939/8890 |
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