The Amazonian forests of Peru are among the most diverse and disturbed by anthropic activities on the planet, today there are still gaps on the composition of the communities and their inter-specific relationships. A forest inventory was carried out in a terraced forest in the Madre de Dios region, 5 blocks were established with 2 rectangular plots of 20m x 500m each to identify and measure individuals with diameter greater than 10 cm. 4429 trees were evaluated and 254 species, 165 genera and 53 families were identified. The distribution of diameter classes and height presents typical patterns of intervened forests, in this case by the extraction of wood decades ago. The average of the biodiversity indices are: Shannon-Wienner 4.039 ± 0.16 and α-Fisher 39.90 ± 9.23, indicating that there is a high diversity of species. The species of greatest ecological importance were: Tetragastris a...

The Amazonian forests of Peru are among the most diverse and disturbed by anthropic activities on the planet, today there are still gaps on the composition of the communities and their inter-specific relationships. A forest inventory was carried out in a terraced forest in the Madre de Dios region, 5 blocks were established with 2 rectangular plots of 20m x 500m each to identify and measure individuals with diameter greater than 10 cm. 4429 trees were evaluated and 254 species, 165 genera and 53 families were identified. The distribution of diameter classes and height presents typical patterns of intervened forests, in this case by the extraction of wood decades ago. The average of the biodiversity indices are: Shannon-Wienner 4.039 ± 0.16 and α-Fisher 39.90 ± 9.23, indicating that there is a high diversity of species. The species of greatest ecological importance were: Tetragastris a...

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 ...

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 ...

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 predi...

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 predi...

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 ...