Monitoring agroforestry systems remains challenging due to canopy heterogeneity and the coexistence of species with contrasting dynamics. While field-based methods offer high accuracy, they are inefficient for rapid and multitemporal structural assessments. This study integrated LiDAR and multispectral data collected using a Matrice 350 RTK equipped with a Zenmuse L2 sensor and a RedEdge-P camera. Raw LiDAR data were processed in DJI Terra v4.1 and subsequently pre-processed and corrected in TerraSolid v23.011, whereas multispectral products were generated in Agisoft Metashape Professional v2.2.1. The derived metrics indicated greater growth in System A, driven by fast-growing species, whereas System B showed an overall reduction with slight increases in the upper percentiles. In addition, MSAVI and MTVI2 were sensitive to canopy structure, while GNDVI and NDRE responded to foliage conte...

Romerillo (Retrophyllum rospigliosii), a vulnerable conifer native to the cloud forests of Cajamarca, Peru, persists in small remnants at high altitudes in San Ignacio province, where its integration into agroforestry systems may support both conservation and sustainable production. This study aimed to model the growth of R. rospigliosii associated with coffee (Coffea arabica L.) using diameter and height as indicators. Field data were collected over 18 months in two experimental plots and the study analyzed 329 individuals selected from 600 initially planted, with monthly monitoring to evaluate early growth and survival dynamics. The data were analyzed with nonlinear mathematical models, including Schumacher, Chapman–Richards, and Weibull, with model selection based on goodness-of-fit and prediction statistics such as R², AIC, and BIC. Results showed that Schumacher provided the best...

Accurate estimation of biomass and carbon in agroforestry systems is essential to assess their contribution to climate change mitigation and to improve their management. In this context, UAV-mounted LiDAR technology emerges as a fast, accurate, and non-destructive alternative for the structural characterization of cocoa agroforestry systems. This study aimed to estimate and analyze structural parameters, mainly tree height and diameter at breast height (DBH), as well as to calculate aboveground biomass and carbon sequestration in a cocoa agroforestry system, using LiDAR data obtained with a DJI Matrice 350 RTK UAV equipped with a Zenmuse L2 sensor, complemented by automatic tree segmentation in LiDAR360 and the application of species-specific allometric equations. The results showed a 93 % segmentation efficiency, with accuracies of 0.93 and 0.99 for DBH and height estimations, respectiv...

Determining pasture productivity and nutritional value through non-destructive approaches aimed at optimizing forage resource management and improving efficiency in livestock systems has become an urgent priority. In this context, the objective of this study was to evaluate the performance of machine learning models in predicting biomass production and the nutritional contribution of different pasture species, as well as to assess the role of vegetation indices (VIs) in these predictions. To this end, a multispectral sensor mounted on a DJI Matrice 350 RTK platform was used, together with agronomic, yield, and nutritional variables. The curated dataset was subsequently analyzed using linear and polynomial models, as well as tree-based algorithms and support vector machines. Model validation was performed using a group-constrained random partitioning scheme (Group Shuffle Split), with spe...