Examinando por Autor "Ore Aquino, Zoila"

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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.
Estimation of height and aerial biomass in Eucalyptus globulus plantations using UAV-LiDAR
(Elsevier B.V., 2024-12-22) Enriquez Pinedo, Lucía; Ortega Quispe, Kevin; Ccopi Trucios, Dennis; Urquizo Barrera, Julio; Rios Chavarría, Claudia; Pizarro Carcausto, Samuel; Matos Calderon, Diana; Patricio Rosales, Solanch; Rodríguez Cerrón, Mauro; Ore Aquino, Zoila; Paz Monge, Michel; Castañeda Tinco, Italo
The lack of precise methods for estimating forest biomass results in both economic losses and incorrect decisions in the management of forest plantations. In response to this issue, this study evaluated the effectiveness of using the DJI Zenmuse L1 LiDAR, mounted on a DJI Matrice 300 RTK UAV, to provide three-dimensional measurements of canopy structure and estimate the aboveground biomass of Eucalyptus globulus. Various LiDAR metrics were employed alongside field measurements to calibrate predictive models using multiple regression and machine learning algorithms. The results at the individual tree level show that RF is the most accurate model, with a coefficient of determination (R²) of 0.76 in the training set and 0.66 in the test set, outperforming Elastic Net (R² of 0.58 and 0.57, respectively). At the plot level, a multiple regression model achieved an R² of 0.647, highlighting LiDAR-derived metrics as key predictors. The findings revealed that the combination of LiDAR with advanced statistical techniques, such as multiple regression and Random Forest, significantly improves the accuracy of biomass estimation, surpassing traditional methods based on allometric equations. Therefore, the use of LiDAR in conjunction with machine learning represents an effective alternative for biomasss estimation, with great potential in such plantations and contribute to more sustainable exploitation of timber resources.
Using UAV images and phenotypic traits to predict potato morphology and yield in Peru
(MDPI, 2024-10-24) 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 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.