Examinando por Autor "Puerta, Ronald"

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MultiProduct Optimization of Cedrelinga cateniformis (Ducke) Ducke in Different Plantation Systems in the Peruvian Amazon
(MDPI, 2025-01-16) Baselly Villanueva, Juan Rodrigo; Fernández Sandoval, Andrés; Salazar Hinostroza, Evelin Judith; Cárdenas-Rengifo, Gloria Patricia; Puerta, Ronald; Chuquizuta Trigoso, Tony Steven; Rufasto Peralta, Yennifer Lisbeth; Vallejos Torres, Geomar; Goycochea Casas, Gianmarco; Araújo Junior, Carlos Alberto; Quiñónez Barraza, Gerónimo; Álvarez Álvarez, Pedro; Garcia Leite, Helio
This study addressed multi-product optimization in Cedrelinga cateniformis plantations in the Peruvian Amazon, aiming to maximize volumetric yields of logs and sawn lumber. Data from seven plantations of different ages and types, established on degraded land, were analyzed by using ten stem profile models to predict taper and optimize wood use. In addition, the structure of each plantation was evaluated using diameter distributions and height–diameter ratios; log and sawn timber production was optimized using SigmaE 2.0 software. The Garay model proved most effective, providing high predictive accuracy (adjusted R2 values up to 0.963) and biological realism. Marked differences in volumetric yield were observed between plantations: older and more widely spaced plantations produced higher timber volumes. Logs of optimal length (1.83–3.05 m) and larger dimension wood (e.g., 25.40 × 5.08 cm) were identified as key contributors to maximizing volumetric yields. The highest yields were observed in mature plantations, in which the total log volume reached 508.1 m3ha−1 and the sawn lumber volume 333.6 m3ha−1 . The findings demonstrate the power of data-driven decision-making in the timber industry. By combining precise modeling and optimization techniques, we developed a framework that enables sawmill operators to maximize log and lumber yields. The insights gained from this research can be used to improve operational efficiency and reduce waste, ultimately leading to increased profitability. These practices promote support for smallholders and the forestry industry while contributing to the long-term development of the Peruvian Amazon.
UAV Flight Orientation and Height Influence on Tree Crown Segmentation in Agroforestry Systems
(Forests, 2026-01-09) Baselly Villanueva, Juan Rodrigo; Fernández Sandoval, Andrés; Pinedo Freyre, Sergio Fernando; Salazar Hinostroza, Evelin Judith; Cárdenas Rengifo, Gloria Patricia; Puerta, Ronald; Huanca Diaz, José Ricardo; Tuesta Cometivos, Gino Anthony; Vallejos Torres, Geomar; Goycochea Casas, Gianmarco; Álvarez Álvarez, Pedro; Ismail, Zool Hilmi
Precise crown segmentation is essential for assessing structure, competition, and productivity in agroforestry systems, but delineation is challenging due to canopy heterogeneity and variability in aerial imagery. This study analyzes how flight height and orientation affect segmentation accuracy in an agroforestry system of the Peruvian Amazon, using RGB images acquired with a DJI Mavic Mini 3 Pro UAV and the instance-segmentation models YOLOv8 and YOLOv11. Four flight heights (40, 50, 60, and 70 m) and two orientations (parallel and transversal) were analyzed in an agroforestry system composed of Cedrelinga cateniformis (Ducke) Ducke, Calycophyllum spruceanum (Benth.) Hook.f. ex K.Schum., and Virola pavonis (A.DC.) A.C. Sm. Results showed that a flight height of 60 m provided the highest delineation accuracy (F1 ≈ 0.88 for YOLOv8 and 0.84 for YOLOv11), indicating an optimal balance between resolution and canopy coverage. Although YOLOv8 achieved the highest precision under optimal conditions, it exhibited greater variability with changes in flight geometry. In contrast, YOLOv11 showed a more stable and robust performance, with generalization gaps below 0.02, reflecting a stronger adaptability to different acquisition conditions. At the species level, vertical position and crown morphological differences (Such as symmetry, branching angle, and bifurcation level) directly influenced detection accuracy. Cedrelinga cateniformis displayed dominant and asymmetric crowns; Calycophyllum spruceanum had narrow, co-dominant crowns; and Virola pavonis exhibited symmetrical and intermediate crowns. These traits were associated with the detection and confusion patterns observed across the models, highlighting the importance of crown architecture in automated segmentation and the potential of UAVs combined with YOLO algorithms for the efficient monitoring of tropical agroforestry systems.