Examinando por Autor "Ortega Quispe, Kevin Abner"

Mostrando 1 - 2 de 2

Detecting Changes in Soil Fertility Properties Using Multispectral UAV Images and Machine Learning in Central Peru
(MDPI, 2025-03-06) Enriquez Pinedo, Lucia Carolina; Ortega Quispe, Kevin Abner; Ccopi Trucios, Dennis; Rios Chavarria, Claudia Sofía; Urquizo Barrera, Julio; Patricio Rosales, Solanch Rosy; Alejandro Mendez, Lidiana Rene; Oliva Cruz, Manuel; Barboza Castillo, Elgar; Pizarro Carcausto , Samuel Edwin
Remote sensing is essential in precision agriculture as this approach provides high-resolution information on the soil's physical and chemical parameters for detailed decision making. Globally, technologies such as remote sensing and machine learning are increasingly being used to infer these parameters. This study evaluates soil fertility changes and compares them with previous fertilization inputs using high-resolution multispectral imagery and in situ measurements. A UAV-captured image was used to predict the spatial distribution of soil parameters, generating fourteen spectral indices and a digital surface model (DSM) from 103 soil plots across 49.83 hectares. Machine learning algorithms, including classification and regression trees (CART) and random forest (RF), modeled the soil parameters (N-ppm, P-ppm, K-ppm, OM%, and EC-mS/m). The RF model outperformed others, with R² values of 72% for N, 83% for P, 87% for K, 85% for OM, and 70% for EC in 2023. Significant spatiotemporal variations were observed between 2022 and 2023, including an increase in P (14.87 ppm) and a reduction in EC (-0.954 mS/m). High-resolution UAV imagery combined with machine learning proved highly effective for monitoring soil fertility. This approach, tailored to the Peruvian Andes, integrates spectral indices and field-collected data, offering innovative tools to optimize fertilization practices, address soil management challenges, and merge modern technology with traditional methods for sustainable agricultural practices.
Efficiency of a compound parabolic collector for domestic hot water production using the F- chart method
(International Hellenic University School of Science and Technology, 2024-06-01) Ortega Quispe, Kevin Abner; Huari Vila, Oscar Paul; Ccopi Trucios, Dennis; Lozano Povis, Arlitt Amy; Enriquez Pinedo, Lucia Carolina; Cordova Torres, Betty
Among solar energy technologies, differences exist in terms of costs, performance, and environmental sustainability. Flatplate solar collectors, solar towers, and parabolic dish systems offer high thermal efficiency and versatility, but they may be more costly and bulky compared to other collector models. This study focused on evaluating the efficiency of a cylindrical parabolic collector (CPC) for the production of domestic hot water in a high Andean region of Peru, using the F-Chart method. Its performance was estimated considering the energy demand for hot water in a single-family home with four occupants, in accordance with national regulations and international recommendations. Additionally, the collector area, water temperature, and incident solar radiation were determined based on meteorological data obtained using the PVsyst software. On the other hand, the F-Chart methodology was employed to find the dimensionless factors X and Y of the CPC collector, which allowed estimating the solar fraction factor and the monthly useful energy that can be provided by the designed CPC system. The results showed that, during months of maximum solar radiation, the CPC is capable of satisfying between 129% and 144% of the energy demand for hot water. This indicates that there is a surplus of usable solar energy in the collector during the summer, while in autumn and winter, the solar contribution balances and slightly exceeds the demand. CPC can significantly contribute to the development of high Andean areas by improving quality of life, reducing costs, and promoting environmental sustainability compared to other available technologies.