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.

Among the ecoregions that comprise the high-Andean zone of the Tropical Andes, the Puna and the Yungas stand out for covering a large part of the Peruvian Eastern Andes. Located in the ecotone of these two ecoregions, the Pampa Hermosa National Sanctuary (PHNS) houses one of the priority areas for conservation in Peru. However, the biodiversity of the high-Andean zone of the sanctuary and its surroundings remains poorly studied. Thus, through camera traps and transects, we sought to inventory for the first time the medium and large mammals from the high-Andean region of the PHNS and its buffer zone. We recorded 11 native and three domestic species of medium and large mammals. The richness of native mammals sampled reached 91.7% of the estimated richness (S est.= 11.99 ± 1.85). Among the native species Odocoileus virginianus had the highest relative frequency (56%). We recorded three endemic mammals from the Tropical Andes, Cuniculus taczanowskii, Tremarctos ornatus, and Pudu mephistopheles. The observed richness was higher than most surveys of medium and large mammals carried out in the Puna-forest ecotone in Peru, where the reported richness ranged from 4 to 13 species. In addition, our records of Leopardus pardalis and Eira barbara are the highest for all distribution of these two carnivores. Our results showed that more than 90% of the species recorded were found in it, demonstrating that the entire high-Andean region of the PHNS and its surroundings has an important value for the local mammal community.

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.