Abstract
With the serious danger to nature and humanity that forest fires are, taken into consideration, this work aims to develop an artificial intelligence model capable of accurately predicting the forest fire risk in a certain region based on four different factors: temperature, wind speed, rain and humidity. Thus, three models were created using three different approaches: Artificial Neural Networks (ANN), Random Forest (RF), and K-Nearest Neighbor (KNN), and making use of an Algerian forest fire dataset. The ANN and RF both achieved high accuracy results of 97%, while the KNN achieved a slightly lower average of 91%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
The paper presents a new control concept based on the process moment instead of the process states or the process output signal. The control scheme is based on separate control of reference tracking and disturbance rejection. The tracking control is achieved by additionally feeding the input of the process model by the scaled output signal of the process model. The advantage of such feedback is that the final state of the process output can be analytically calculated and used for control instead of the actual process output value. The disturbance rejection, including model imperfections, is controlled by feeding back the filtered difference between the process output and the model output to the process input. The performance of tracking and disturbance rejection is simply controlled by two user-defined gains. Several examples have shown that the new control method provides very good and stable tracking and disturbance rejection performance.

Abstract
Reduced-impact logging (RIL) has been recognized as a promising strategy for biodiversity conservation and carbon sequestration within sustainable forest management (SFM) areas. However, monitoring the forest understory-a critical area for assessing logging impacts-remains challenging due to limitations in conventional methods such as field inventories and global navigation satellite system (GNSS) surveys, which are time-consuming, costly, and often lack accuracy in complex environments. Additionally, aerial and satellite imagery frequently underestimate the full extent of disturbances as the forest canopy obscures understory impacts. This study examines the effectiveness of the relative density model (RDM), derived from airborne LiDAR data, for mapping and monitoring understory disturbances. A field-based validation of LiDAR-derived RDM was conducted across 25 sites, totaling 5504.5 hectares within the Jamari National Forest, Rond & ocirc;nia, Brazil. The results indicate that the RDM accurately delineates disturbances caused by logging infrastructure, with over 90% agreement with GNSS field data. However, the model showed the greatest discrepancy for skid trails, which, despite their lower accuracy in modeling, accounted for the largest proportion of the total impacted area among infrastructure. The findings include the mapping of 35.1 km of primary roads, 117.4 km of secondary roads, 595.6 km of skid trails, and 323 log landings, with skid trails comprising the largest proportion of area occupied by logging infrastructure. It is recommended that airborne LiDAR assessments be conducted up to two years post-logging, as impacts become less detectable over time. This study highlights LiDAR data as a reliable alternative to traditional monitoring approaches, with the ability to detect understory impacts more comprehensively for monitoring selective logging in SFM areas of the Amazon, providing a valuable tool for both conservation and climate mitigation efforts.

Abstract
The autonomous navigation of unmanned ground vehicles (UGVs) in unstructured environments, such as agricultural or forestry settings, has been the subject of extensive research by various investigators. The navigation capability of a UGV in unstructured environments requires considering numerous factors, including the quality of data reception that allows reliable interpretation of what the UGV perceives in a given environment, as well as the use these data to control the UGV’s navigation. This article aims to study different PID control algorithms to enable autonomous navigation on a robotic platform. The robotic platform consists of a forestry tractor, used for forest cleaning tasks, which was converted into a UGV through the integration of sensors. Using sensor data, the UGV’s position and orientation are obtained and utilized for navigation by inputting these data into a PID control algorithm. The correct choice of PID control algorithm involved the study, analysis, and implementation of different controllers, leading to the conclusion that the Vector Field control algorithm demonstrated better performance compared to the others studied and implemented in this paper.

Abstract
Since the advent of agriculture, humans have considered phytopharmaceutical products to control pests and reduce losses in farming. Sometimes some of these products, such pesticides, can potentially harm the soil life. In the literature there is evidence that AI and image processing can have a positive contribution to reduce phytopharmaceutical losses, when used in variable rate sprayers. However, it is possible to improve the existing sprayer system's precision, accuracy, and mechanical aspects. This work proposes spraying solution called GraDeS solution (Grape Detection Sprayer). GraDeS solution is a sprayer with two degrees of freedom, controlled by a AI-based algorithm to precisely treat grape bunches diseases. The experiments with the designed sprayer showed two key points. First, the deep learning algorithm recognized and tracked grape bunches. Even with structure movement and bunch covering, the algorithm employs several strategies to keep track of the discovered objects. Second, the robotic sprayer can improve precision in specified areas, such as exclusively spraying grape bunches. Because of the structure's reduced size, the system can be used in medium and small robots.

Abstract
The Digital electronics collaborative enhanced learning (DECEL) project has recently developed an international collaborative education course. Its main objective is to enhance the digital electronics skills of international students by working on a complex, multidisciplinary applied problem using a mixed digital architecture. We have developed a logic level synthesis and dedicated software layers on the Red Pitaya FPGA platform. The diversity of digital concepts to be implemented, from hardware description language (HDL) to high-level languages such as Python or Matlab, forced the students to work together and rapidly improve their skills. Their motivation was fueled by the curiosity of controlling an ultrasound probe to obtain ultrasound signatures. This particular physics, little known to the students, was an additional source of curiosity. The goal of forming an image in a liquid medium was an additional motivating factor for them. The students reported that they learned a lot from the experiment. Thus, the technical parts and pedagogical results are documented in this work for reproducibility.