Using UAV Images and Phenotypic Traits to Predict Potato Morphology and Yield in Peru

Dennis Ccopi, Kevin Ortega (), Italo Castañeda, Claudia Rios, Lucia Enriquez, Solanch Patricio, Zoila Ore, David Casanova, Alex Agurto, Noemi Zuñiga and Julio Urquizo
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Dennis Ccopi: Dirección de Desarrollo Tecnológico Agrario, Instituto Nacional de Innovación Agraria (INIA), Carretera Saños Grande, Hualahoyo Km 8 Santa Ana, Huancayo 12007, Peru
Kevin Ortega: Dirección de Desarrollo Tecnológico Agrario, Instituto Nacional de Innovación Agraria (INIA), Carretera Saños Grande, Hualahoyo Km 8 Santa Ana, Huancayo 12007, Peru
Italo Castañeda: Dirección de Desarrollo Tecnológico Agrario, Instituto Nacional de Innovación Agraria (INIA), Carretera Saños Grande, Hualahoyo Km 8 Santa Ana, Huancayo 12007, Peru
Claudia Rios: Dirección de Desarrollo Tecnológico Agrario, Instituto Nacional de Innovación Agraria (INIA), Carretera Saños Grande, Hualahoyo Km 8 Santa Ana, Huancayo 12007, Peru
Lucia Enriquez: Dirección de Desarrollo Tecnológico Agrario, Instituto Nacional de Innovación Agraria (INIA), Carretera Saños Grande, Hualahoyo Km 8 Santa Ana, Huancayo 12007, Peru
Solanch Patricio: Dirección de Desarrollo Tecnológico Agrario, Instituto Nacional de Innovación Agraria (INIA), Carretera Saños Grande, Hualahoyo Km 8 Santa Ana, Huancayo 12007, Peru
Zoila Ore: Dirección de Desarrollo Tecnológico Agrario, Instituto Nacional de Innovación Agraria (INIA), Av. La Molina, 1981, Lima 15024, Peru
David Casanova: Dirección de Desarrollo Tecnológico Agrario, Instituto Nacional de Innovación Agraria (INIA), Av. La Molina, 1981, Lima 15024, Peru
Alex Agurto: Dirección de Desarrollo Tecnológico Agrario, Instituto Nacional de Innovación Agraria (INIA), Av. La Molina, 1981, Lima 15024, Peru
Noemi Zuñiga: Facultad de Agronomía, Universidad Nacional de Centro del Perú, Carretera Central Km 37, El Mantaro, Jauja 12150, Peru
Julio Urquizo: Dirección de Desarrollo Tecnológico Agrario, Instituto Nacional de Innovación Agraria (INIA), Carretera Saños Grande, Hualahoyo Km 8 Santa Ana, Huancayo 12007, Peru

Agriculture, 2024, vol. 14, issue 11, 1-23

Abstract: 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 (R 2 > 0.74). In contrast, the prediction of morphological quality was less accurate, with Random Forest standing out as the most reliable model (R 2 = 0.55 for circularity). Spectral data significantly improved the predictive capacity compared to agronomic data alone. We conclude that integrating spectral indices and multitemporal data into predictive models improved the accuracy in estimating yield and certain morphological traits, offering key opportunities to optimize agricultural management.

Keywords: crop monitoring; machine learning; precision agriculture; remote sensing; agricultural innovation (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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