Examinando por Materia "Vegetation indices"

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Agronomic variables outperform multispectral indices for individual plant yield prediction in Andean quinoa
(Elsevier B.V., 2026-04-18) Pizarro Carcausto, Samuel Edwin; García Seguil, Erika Janina; Gavino Lulo, Esthefany Irene; Requena Rojas, Edilson Jimmy; Ortega Quispe, Kevin Abner; Cccopi Trucios, Dennis
Accurate pre-harvest yield estimation is essential for decision-making in high-altitude agriculture. This study evaluated agronomic and multispectral UAV variables for near-harvest prediction of individual quinoa grain weight, with data collected across six phenological stages to identify when predictors achieve reliable performance, under Andean conditions. A total of 374 plants were monitored across six phenological stages at Santa Ana Experimental Station (Huancayo, Peru, 3280 m a.s.l.) during 2024. OLS, Random Forest, Support Vector Machine, and Neural Network models were trained using agronomic-only (AGRO), spectral-only (IND), and combined (COMP) predictor sets, evaluated through 5-fold cross-validation reporting mean ± standard deviation. Agronomic and combined models achieved moderate performance (R² = 0.22–0.25, RPD = 1.10–1.15), suitable for relative plant ranking in breeding programs, while spectral-only models failed across all algorithms (R² ≤ 0.044, CCC ≤ 0.080), constrained by saturation, phenological decoupling, and canopy heterogeneity. Variable importance analysis confirmed that late-season structural traits dominated predictions, while spectral indices contributed marginally despite including red-edge bands. These results challenge spectral-only approaches for individual plant phenotyping in heterogeneous canopies, demonstrating that integrating simple ground measurements with UAV spectral data is essential for reliable quinoa yield estimation.
Estimation of forage biomass in oat (Avena sativa) using agronomic variables through UAV multispectral imaging
(MDPI, 2024-10-06) Urquizo Barrera, Julio Cesar; Ccopi Trucios, Dennis; Ortega Quispe, Kevin; Castañeda Tinco, Italo; Patricio Rosales, Solanch; Passuni Huayta, Jorge; Figueroa Venegas, Deyanira; Enriquez Pinedo, Lucia; Ore Aquino, Zoila; Pizarro Carcausto, Samuel
Accurate and timely estimation of oat biomass is crucial for the development of sustainable and efficient agricultural practices. This research focused on estimating and predicting forage oat biomass using UAV and agronomic variables. A Matrice 300 equipped with a multispectral camera was used for 14 flights, capturing 21 spectral indices per flight. Concurrently, agronomic data were collected at six stages synchronized with UAV flights. Data analysis involved correlations and Principal Component Analysis (PCA) to identify significant variables. Predictive models for forage biomass were developed using various machine learning techniques: linear regression, Random Forests (RFs), Support Vector Machines (SVMs), and Neural Networks (NNs). The Random Forest model showed the best performance, with a coefficient of determination R2 of 0.52 on the test set, followed by Support Vector Machines with an R2 of 0.50. Differences in root mean square error (RMSE) and mean absolute error (MAE) among the models highlighted variations in prediction accuracy. This study underscores the effectiveness of photogrammetry, UAV, and machine learning in estimating forage biomass, demonstrating that the proposed approach can provide relatively accurate estimations for this purpose.
Modelado espacial de propiedades fisicoquímicas y fertilidad del suelo en sistemas agrícolas tropicales bajo distinta heterogeneidad estructural mediante UAV multiespectral y geoestadística
(Facultad de Ciencias Agropecuarias, Universidad Nacional de Trujillo, 2026-04-27) Vega Herrera, Sergio Sebastián; Ysuiza Perez, Alfredo; Perez Tello, Mónica; Goicochea Pinchi, Diego; Rios Rios, Raúl Martín; Dominguez Yap, Percy; García, Leonela; Barrera Torres, Cicerón; Oliva Cruz, Carlos Alberto; Santillán Gonzáles, Manuel Dante; Arratea Pillco, David; Alejos Patiño, Italo
La variabilidad espacial del suelo condiciona la eficiencia productiva, la gestión de nutrientes y la sostenibilidad de los sistemas agrícolas tropicales, especialmente en contextos donde la heterogeneidad limita la implementación de estrategias de manejo sitio-específico. En este estudio se comparó el desempeño de un flujo analítico basado en imágenes UAV multiespectrales, regresión lineal múltiple (MLR) e interpolación geoestadística en dos sistemas agrícolas con distinta heterogeneidad, un sistema multicultivo a escala de estación y un sistema arrocero con diferentes densidades de siembra, ambos ubicados en la Estación Experimental Agraria El Porvenir (San Martín, Perú). Se analizaron 60 muestras en el componente multicultivo y 27 en el sistema arrocero, georreferenciadas a 30 cm de profundidad, evaluando pH, conductividad eléctrica, nitrógeno, fósforo, potasio, carbono orgánico del suelo y textura. Se aplicó un flujo analítico homogéneo en ambos sistemas (correlación de Spearman, MLR stepwise y kriging ordinario). Los resultados evidenciaron diferencias marcadas en el desempeño predictivo, en el sistema arrocero se alcanzaron valores de R² de prueba de 0,93 para nitrógeno y 0,88 para fósforo, mientras que en el sistema multicultivo los mayores R² fueron 0,42 para conductividad eléctrica y 0,37 para limo. Asimismo, los índices espectrales basados en NIR y red edge mostraron mayor asociación con los atributos edáficos evaluados. Los resultados demuestran que el desempeño depende de la heterogeneidad estructural del sistema, donde entornos más homogéneos favorecen la predicción puntual, mientras que sistemas más heterogéneos potencian la zonificación y delimitación de unidades de manejo
Nutritional quality of the “Algarrobo” neltuma pallida fruit and its relationship with soil properties and vegetation indices in the dry forests of Northern Peru
(MPDI, 2025-09-16) Salazar Coronel, Wilian; Cruz Grimaldo, Camila Leandra; Lastra Paucar, Sphyros Roomel; Rengifo Sanchez, Raihil Rabindranath; Vargas de la Cruz, Celia; Godoy Padilla, David; Sessarego Davila, Emmanuel Alexander; Cruz Luis, Juancarlos Alejandro; Solórzano Acosta, Richard Andi
The dry forests of northern Peru are home to extensive populations of algarrobo (Neltuma spp.). Its fruit serves as feed for goats and is used in various agro-industrial products. However, the nutritional quality can be influenced by the physicochemical properties of the soil and vegetation coverage. The objective of this study was to understand and predict the concentration of protein and ether extracts of carob and evaluate its relationship with soil properties and vegetation indices. Principal component analysis (PCA) and correlation analyses were conducted. The prediction of protein and ether extract was carried out using the Eureqa-Formulize software 1.24.0. In the PCA, protein showed a positive relationship with ash and ether extract but a negative relationship with moisture. Likewise, moderate correlations were observed between protein and ash content (0.51). Protein also showed positive correlations with pH (r = 0.19), BI (r = 0.22), and NDSI (r = 0.22). Additionally, the ether extract exhibited correlations with sand content (r = 0.22), Ca2+ (r = −0.26), Cu (r = −0.20), S5 (r = 0.26), and Si (r = 0.24). Protein predictions showed moderate performance (CC = 0.73 and R2 = 0.53), as did ether extracts (CC = 0.68 and R2 = 0.46). These findings contribute to a better understanding of the factors that influence the nutritional quality of carob and can be used for the development of sustainable management strategies in the dry forests of northern Peru.
Soil organic carbon content mapping along the coast of northern Peru: an ensemble machine learning approach
(Frontiers Media SA, 2026-03-26) Salazar Coronel, Wilian; Carbajal Llosa, Carlos Miguel; Chuchon Remon, Rodolfo Juan
Introduction: Soil organic carbon (SOC) content plays a fundamental role in regulating the global carbon cycle and mitigating climate change. It is also a key marker of soil health and a vital plant component. Its distribution in space varies in dry ecosystems, where climate and land use affect it. This study aimed to estimate and map SOC in the Motupe River Basin, northern Peru, by applying machine learning algorithms and ensemble methods. Methods: Four predictive models were evaluated: Support Vector Regression (SVR), Random Forest (RF), Artificial Neural Network (ANN), and Extreme Gradient Boosting (XGBoost), together with two ensemble approaches—simple averaging and weighted — integrating topographic, climatic, edaphic, and vegetation indices variables. Spatial autocorrelation was minimized by spatial block cross-validation. Uncertainty was measured with bootstrapping and the Prediction Interval Ratio (PIR) derived from 90% prediction intervals. Results and discussion: Best performance was achieved by XGBoost (R² = 0.83), weighted ensemble (R² = 0.70), and RF (R² = 0.63). The most influential predictors were EVI, GNDVI, temperature, TRI, and pH. SOC contents showed relatively higher concentrations (>0.7%) in areas with greater vegetation density, within a semi-arid context where SOC levels are generally low. In contrast, lower areas exhibited reduced SOC contents (< 0.6%). The uncertainty analysis indicated that SOC predictions had high to moderate confidence (PIR < 0.2) in the middle-and upper zones of the basin, and moderate confidence (0.1–0.2) in the lower areas. The results suggest that machine learning and ensemble methods improve SOC prediction, benefiting the sustainable management of soil fertility and quality in arid and semi-arid ecosystems of northern Peru.
Yield predictions of four hybrids of maize (Zea mays) using multispectral images obtained from UAV in the Coast of Peru
(MDPI, 2022-10-26) Saravia Navarro, David; Salazar Coronel, Wilian; Valqui Valqui, Lamberto; Quille Mamani, Javier Alvaro; Porras Jorge, Zenaida Rossana; Corredor Arizapana, Flor Anita; Barboza Castillo, Elgar; Vásquez Pérez, Héctor Vladimir; Casas Diaz, Andrés V.; Arbizu Berrocal, Carlos Irvin
Early assessment of crop development is a key aspect of precision agriculture. Shortening the time of response before a deficit of irrigation, nutrients and damage by diseases is one of the usual concerns in agriculture. Early prediction of crop yields can increase profitability for the farmer’s economy. In this study, we aimed to predict the yield of four maize commercial hybrids (Dekalb7508, Advanta9313, MH_INIA619 and Exp_05PMLM) using vegetation indices (VIs). A total of 10 VIs (NDVI, GNDVI, GCI, RVI, NDRE, CIRE, CVI, MCARI, SAVI, and CCCI) were considered for evaluating crop yield and plant cover at 31, 39, 42, 46 and 51 days after sowing (DAS). A multivariate analysis was applied using principal component analysis (PCA), linear regression, and r-Pearson correlation. Highly significant correlations were found between plant cover with VIs at 46 (GNDVI, GCI, RVI, NDRE, CIRE and CCCI) and 51 DAS (GNDVI, GCI, NDRE, CIRE, CVI, MCARI and CCCI). The PCA showed clear discrimination of the dates evaluated with VIs at 31, 39 and 51 DAS. The inclusion of the CIRE and NDRE in the prediction model contributed to estimating the performance, showing greater precision at 51 DAS. The use of unmanned aerial vehicles (UAVs) to monitor crops allows us to optimize resources and helps in making timely decisions in agriculture in Peru.