Examinando por Autor "Alejandro Mendez, Lidiana Rene"

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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.
Spatial Variability of Soil Acidity and Lime Requirements for Potato Cultivation in the Huánuco Highlands
(MDPI, 2024-12-13) Quispe Matos, Kenyi Rolando; Mejía, Sharon; Carbajal Llosa, Carlos Miguel; Alejandro Mendez, Lidiana Rene; Verástegui Martinez, Patricia; Solórzano Acosta, Richard Andi
Soil acidity is a major limiting factor for potato production in Peru's high Andean region. This study aims to predict the spatial variability of soil acidity as a fundamental tool for recommending site-specific liming treatments and to identify the physical-chemical characteristics most closely related to soil acidity. The soil samples were collected from five locations in the province of Pachitea, Huánuco. Descriptive statistics, principal component analysis (PCA), and Pearson correlation analysis were used to identify the soil properties contributing most to total variance and those most strongly correlated with soil acidity. The ordinary geostatistical kriging method evaluated the predictive accuracy for 23 soil properties and liming requirements over a 28,463 ha area, at a spatial resolution of 10 m. Results showed that the Plaza Punta and Buenos Aires locations had more degraded acidic soils, with frequencies between 55% and 100% above the general mean (30.94 ± 24.87%) and the critical threshold (25%) for potato cultivation. Variables such as exchangeable calcium percentage (ECP), Ca2+, Mg2+, sand content, and organic matter strongly correlated with soil acidity, while exchangeable H+ and ECP were the main contributors to the total variance. Geostatistical analysis revealed that Mg2+ and Ca2+ had the highest R² values (0.87 and 0.76, respectively), indicating a strong fit between observed and predicted values in the spatial analysis of soil acidity. It is concluded that the agricultural dolomite requirements in the localities of Plaza Punta and Buenos Aires exhibit high spatial predictability. Additionally, the analysis of diverse soil physicochemical properties is emphasized as critical for determining precise application rates.