The objective of this project was to automate the detection and counting process of stacked eucalypt (hybrid Eucalyptus urophylla x Eucalyptus grandis) timber in the forestry industry using the YOLOv8 model. The dataset consists of 230 diverse images of eucalypt roundwood, including images of roundwood separated on a rail and stacked timber. The annotations were made using LabelImg, ensuring accurate delineation of target objects on the log surfaces. The YOLOv8 model is customized with a CSPDarknet53 backbone, C2f module, and SPPF layer for efficient computation. The model was trained using an AdamW optimizer and implemented using Ultralytics YOLOv8.0.137, Python-3.10.12, and torch-2.0.1 + cu118 with CUDA support on NVIDIA T1000 (4096MiB). For model evaluation, the precision, recall, and mean Average Precision at a 50% confidence threshold (mAP50) were calculated. The best results were achieved at epoch 261, with a precision of 0.814, recall of 0.812, and mAP50 of 0.844 on the training set and a precision of 0.778, recall of 0.798, and mAP50 of 0.839 on the validation set. The model’s generalization was tested on separate images, demonstrating robust detection and accurate counting. The model effectively identified roundwood that was widely spaced, scattered, and overlapping. However, when applied to stacked timber, the automatic counting was not very accurate, especially when using images. In contrast, when using video, the relative percentage error for automatic counting significantly decreased to −12.442%. In conclusion, video proved to be more effective than images for counting stacked timber, while photographs should be reserved for the counting of individual roundwood pieces.

Frosty pod rot disease of cacao (FPR), caused by the fungus Moniliophthora roreri , has severely impacted the production of cocoa in Latin America since its discovery. Prior to the 1950s, FPR was known only from Colombia and Ecuador. However, beginning in the 1970s, its geographical range has dramatically expanded throughout most of the cacao‐producing regions of the Americas. The origin of the pathogen remains unknown. In this study, we evaluated the genetic diversity of M . roreri from areas spanning, as much as possible, its current geographical range using simple‐sequence repeat markers and a publicly available single‐nucleotide polymorphism data set. Two hotspots of genetic diversity were found: coastal Ecuador and the inter‐Andean Magdalena Valley of Colombia, neither of which correspond to the Amazonian origin of the host. However, both areas were early centres of intense cultivation of cacao. Our results indicate that M . roreri was introduced into both areas from its centre of origin, where intensive cacao cultivation probably led to the increase of inoculum and further dissemination of the disease. Current invasions can be traced to two genotypes responsible for all known instances of the pathogen in Central America, the Caribbean, Peru and Bolivia. We also report for the first time M . roreri in Maynas (Peruvian Amazon), which is probably the result of a recent introduction from Colombia.

This survey presents a structured review of model predictive control (MPC) strategies for autonomous UAV landings, covering both theory and practice. As UAVs are increasingly used in critical missions, robust and precise landing control is essential. The paper introduces the MPC framework—linear and nonlinear models, constraints, and optimization—and analyses 48 articles from 2017 to early April 2025, identified via Scopus. These studies are organized into six thematic areas: maritime platform landings, landings on moving ground vehicles, fixed‐wing UAVs in constrained settings, robust and fault‐tolerant control, sensor fusion and vision‐based methods, and cooperative multi‐agent strategies. Advanced topics include neural optimization, economic and distributed MPC, and learning‐based approaches using Gaussian Processes and deep neural networks to enhance real‐time adaptability under uncertainty. Bibliometric analysis reveals trends in publication frequency, geographic distribution, and research clustering. The review also highlights key challenges such as real‐time computation, robust perception, and sensor integration in GPS‐denied or turbulent environments. This work offers a comprehensive reference for the development of intelligent and resilient MPC‐based control systems for autonomous UAV landings.

The Guazuma crinita Mart. is a dominant species of great economic importance for the inhabitants of the Peruvian Amazon, standing out for its rapid growth and being harvested at an early age. Understanding its vertical growth is a challenge that researchers have continued to study using different hypsometric modeling techniques. Currently, machine learning techniques, especially artificial neural networks, have revolutionized modeling for forest management, obtaining more accurate predictions; it is because we understand that it is of the utmost importance to adapt, evaluate and apply these methods in this species for large areas. The objective of this study was to build and evaluate the efficiency of the use of a deep neural network for the prediction of the total height of Guazuma crinita Mart. from a large-scale continuous forest inventory. To do this, we explore different configurations of the hidden layer hyperparameters and define the variables according to the function HT = f(x) where HT is the total height as the output variable and x is the input variable(s). Under this criterion, we established three HT relationships: based on the diameter at breast height (DBH), (i) HT = f(DBH); based on DBH and Age, (ii) HT = f(DBH, Age) and based on DBH, Age and Agroclimatic variables, (iii) HT = f(DBH, Age, Agroclimatology), respectively. In total, 24 different configuration models were established for each function, concluding that the deep artificial neural network technique presents a satisfactory performance for the predictions of the total height of Guazuma crinita Mart. for modeling large areas, being the function based on DBH, Age and agroclimatic variables, with a performance validation of RMSE = 0.70, MAE = 0.50, bias% = −0.09 and VAR = 0.49, showed better accuracy than the others.

The quantification of a stand’s wood stock is one of the most important procedures for Tectona spp. (teak) management. An optimal method for estimating tree volume must accommodate the variation of...