Using UAV images and phenotypic traits to predict potato morphology and yield in Peru
Precision agriculture aims to improve crop management using advanced analytical tools.In this context, the objective of this study is to develop an innovative predictive model to estimate the yield and morphological quality, such as the circularity and length–width ratio of potato tubers, based on p...
| Autores principales: | , , , , , , , , , , |
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| Formato: | Artículo |
| Lenguaje: | Inglés |
| Publicado: |
MDPI
2024
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| Materias: | |
| Acceso en línea: | http://hdl.handle.net/20.500.12955/2610 https://doi.org/10.3390/agriculture14111876 |
| _version_ | 1855490398095409152 |
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| author | Ccopi Trucios, Dennis Ortega Quispe, Kevin Castañeda Tinco, Italo Rios Chavarria, Claudia Enriquez Pinedo, Lucia Patricio Rosales, Solanch Ore Aquino, Zoila Casanova Nuñez Melgar, David Agurto Piñarreta, Alex Iván Zúñiga López, Luz Noemí Urquizo Barrera, Julio |
| author_browse | Agurto Piñarreta, Alex Iván Casanova Nuñez Melgar, David Castañeda Tinco, Italo Ccopi Trucios, Dennis Enriquez Pinedo, Lucia Ore Aquino, Zoila Ortega Quispe, Kevin Patricio Rosales, Solanch Rios Chavarria, Claudia Urquizo Barrera, Julio Zúñiga López, Luz Noemí |
| author_facet | Ccopi Trucios, Dennis Ortega Quispe, Kevin Castañeda Tinco, Italo Rios Chavarria, Claudia Enriquez Pinedo, Lucia Patricio Rosales, Solanch Ore Aquino, Zoila Casanova Nuñez Melgar, David Agurto Piñarreta, Alex Iván Zúñiga López, Luz Noemí Urquizo Barrera, Julio |
| author_sort | Ccopi Trucios, Dennis |
| collection | Repositorio INIA |
| description | Precision agriculture aims to improve crop management using advanced analytical tools.In this context, the objective of this study is to develop an innovative predictive model to estimate the yield and morphological quality, such as the circularity and length–width ratio of potato tubers, based on phenotypic characteristics of plants and data captured through spectral cameras equipped on UAVs. For this purpose, the experiment was carried out at the Santa Ana Experimental Station in the central Peruvian Andes, where advanced potato clones were planted in December 2023 under three levels of fertilization. Random Forest, XGBoost, and Support Vector Machine models were used to predict yield and quality parameters, such as circularity and the length–width ratio. The results showed that Random Forest and XGBoost achieved high accuracy in yield prediction (R2 > 0.74). In contrast, the prediction of morphological quality was less accurate, with Random Forest standing out as the most reliable model (R2 = 0.55 for circularity). Spectral data significantly improved the predictive capacity compared to agronomic data alone. We conclude that integrating spectral índices and multitemporal data into predictive models improved the accuracy in estimating yield and certain morphological traits, offering key opportunities to optimize agricultural management. |
| format | Artículo |
| id | INIA2610 |
| institution | Institucional Nacional de Innovación Agraria |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | INIA26102025-05-26T01:11:49Z Using UAV images and phenotypic traits to predict potato morphology and yield in Peru Ccopi Trucios, Dennis Ortega Quispe, Kevin Castañeda Tinco, Italo Rios Chavarria, Claudia Enriquez Pinedo, Lucia Patricio Rosales, Solanch Ore Aquino, Zoila Casanova Nuñez Melgar, David Agurto Piñarreta, Alex Iván Zúñiga López, Luz Noemí Urquizo Barrera, Julio Precision agriculture Remote sensing Crop monitoring Machine learning https://purl.org/pe-repo/ocde/ford#4.01.06 Machine learning Precision agriculture aims to improve crop management using advanced analytical tools.In this context, the objective of this study is to develop an innovative predictive model to estimate the yield and morphological quality, such as the circularity and length–width ratio of potato tubers, based on phenotypic characteristics of plants and data captured through spectral cameras equipped on UAVs. For this purpose, the experiment was carried out at the Santa Ana Experimental Station in the central Peruvian Andes, where advanced potato clones were planted in December 2023 under three levels of fertilization. Random Forest, XGBoost, and Support Vector Machine models were used to predict yield and quality parameters, such as circularity and the length–width ratio. The results showed that Random Forest and XGBoost achieved high accuracy in yield prediction (R2 > 0.74). In contrast, the prediction of morphological quality was less accurate, with Random Forest standing out as the most reliable model (R2 = 0.55 for circularity). Spectral data significantly improved the predictive capacity compared to agronomic data alone. We conclude that integrating spectral índices and multitemporal data into predictive models improved the accuracy in estimating yield and certain morphological traits, offering key opportunities to optimize agricultural management. 2024-11-28T15:00:18Z 2024-11-28T15:00:18Z 2024-10-24 info:eu-repo/semantics/article Ccopi-Trucios, D.; Ortega-Quispe, K.; Castañeda-Tinco, I.; Rios-Chavarria,C.; Enriquez-Pinedo, L.; Patricio-Rosales, S.; Ore-Aquino, Z.; Casanova-Nuñez-Melgar, D.; Agurto-Piñarreta, A.; Zuñiga-López, N.; & Urquizo-Barrera, J. (2024). Using UAV images and phenotypic traits to predict potato morphology and yield in Peru. Agriculture, 14(11), 1876. doi: 10.3390/agriculture14111876 2077-0472 http://hdl.handle.net/20.500.12955/2610 https://doi.org/10.3390/agriculture14111876 eng urn:issn: 2077-0472 Agriculture 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 | Precision agriculture Remote sensing Crop monitoring Machine learning https://purl.org/pe-repo/ocde/ford#4.01.06 Machine learning Ccopi Trucios, Dennis Ortega Quispe, Kevin Castañeda Tinco, Italo Rios Chavarria, Claudia Enriquez Pinedo, Lucia Patricio Rosales, Solanch Ore Aquino, Zoila Casanova Nuñez Melgar, David Agurto Piñarreta, Alex Iván Zúñiga López, Luz Noemí Urquizo Barrera, Julio Using UAV images and phenotypic traits to predict potato morphology and yield in Peru |
| title | Using UAV images and phenotypic traits to predict potato morphology and yield in Peru |
| title_full | Using UAV images and phenotypic traits to predict potato morphology and yield in Peru |
| title_fullStr | Using UAV images and phenotypic traits to predict potato morphology and yield in Peru |
| title_full_unstemmed | Using UAV images and phenotypic traits to predict potato morphology and yield in Peru |
| title_short | Using UAV images and phenotypic traits to predict potato morphology and yield in Peru |
| title_sort | using uav images and phenotypic traits to predict potato morphology and yield in peru |
| topic | Precision agriculture Remote sensing Crop monitoring Machine learning https://purl.org/pe-repo/ocde/ford#4.01.06 Machine learning |
| url | http://hdl.handle.net/20.500.12955/2610 https://doi.org/10.3390/agriculture14111876 |
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