Using UAV images and phenotypic traits to predict potato morphology and yield in Peru

• Autores: 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
• Formato: artículo
• Fecha de Publicación: 2024
• Institución: Instituto Nacional de Innovación Agraria
• Repositorio: INIA-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.inia.gob.pe:20.500.12955/2610
• Enlace del recurso: http://hdl.handle.net/20.500.12955/2610; https://doi.org/10.3390/agriculture14111876
• Nivel de acceso: acceso abierto
• Materia: Precision agriculture; Remote sensing; Crop monitoring; Machine learning; https://purl.org/pe-repo/ocde/ford#4.01.06

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.