Examinando por Materia "Vegetation indices"

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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.
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.
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.