Heavy metal contamination in rivers is a serious environmental and public health concern, especially in areas affected by mining. This study evaluated the levels of contamination and the associated ecological and carcinogenic risks in the sediments of the Cunas River, located in the central highlands of Peru. Sediment samples were collected from upstream and downstream sections. Several metals and metalloids were analyzed, including copper (Cu), chromium (Cr), iron (Fe), manganese (Mn), molybdenum (Mo), nickel (Ni), lead (Pb), vanadium (V), zinc (Zn), antimony (Sb), arsenic (As), and cadmium (Cd). The ecological risk assessment focused on ten of these elements, while carcinogenic and non-carcinogenic risks were assessed for seven metals selected based on their toxicological importance. The results showed that Cd and Pb concentrations were higher in the downstream section. Cd and As exceeded ecological risk thresholds. Regarding human health, As and Pb surpassed the acceptable limits for both the Hazard Index (HI) and the Potential Carcinogenic Risk (PCR). According to EPA guidelines, these values indicate a potentially significant lifetime cancer risk. The main exposure routes include direct contact with sediments and the consumption of aquatic organisms. Continuous monitoring, phytoremediation actions, and restrictions on the use of contaminated water are strongly recommended to reduce ecological and health risks.

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.

River floods are common natural phenomena that occur when the flow of water exceeds the capacity of a river due to excessive rainfall. In the Peruvian territory, the heavy rains of 2010 had consequences of great magnitude, leaving more than 5000 people affected and 25 dead in the Peruvian Andes. This research aimed to analyze and determine the level of risk due to river floods in communities of the Peruvian Andes in terms of hazard and vulnerability, using a semi-quantitative methodology and applying a multi-criteria analysis with vector information and raster from the national spatial data infrastructure that acted as triggering and conditioning factors, as well as conducting fieldwork with the application of targeted surveys. Then, the geoprocessing of thematic maps through GIS software was carried out. The research findings indicate that virtually the entire study area, approximately 99.26%, is at a high level of hazard, with only a small 0.74% classified as very high hazard. In other words, the entire studied territory is susceptible to floods. Furthermore, it is noteworthy that over 75% of the households in this area face vulnerability to floods, resulting in 99.15% of them being categorized at a high-risk level. It is concluded that river flooding represents a potential risk in large areas of the high Andean community due to the existence of various social, economic and environmental factors that make this phenomenon catastrophic.

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 indices and multitemporal data into predictive models improved the accuracy in estimating yield and certain morphological traits, offering key opportunities to optimize agricultural management.

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.