Abstract
Forests have been harassed by fire in recent years. Whether by human action or for other reasons, the burned area has increased harming fauna and flora. It is fundamental to detect an ignition early in order to firefighters fight the fire minimizing the fire impacts. The proposed Forest Monitoring System aims to improve the nature monitoring and to enhance the existing surveillance systems. A set of innovative operations is proposed that will allow to identify a forest ignition and also will monitor the fauna. For that, a set of sensors are being developed and placed in the forest to transmit data and identify forest fire ignition. This paper addresses a methodology that identifies the optimal positions to place the developed sensors in order to minimize the fire hazard. Some preliminary results are shown by a stochastic algorithm that spread points to position the sensor modules in areas with a high risk of fire hazard.

Abstract
This article describes the development cycle of an educational robot designed to act as an interdisciplinary teaching tool integrated into the curriculum of STEM areas (Science, Technology, Engineering and Mathematics). We focused on the creation of the alpha version of the prototype and its heuristic evaluation by three experts, with the objective of appraising both usability and potential design problems. After all the issues and suggestions from the experts have been resolved and implemented, a beta version was developed and evaluated in its usability by five representatives of end-users with different age ranges and robotics knowledge. The System Usability Scale score of 92.5 points - Best Imaginable - show a very stable and satisfactory robot, with almost no usability problems detected. Copyright

Abstract
Wireless Sensor Networks (WSN) can be used to acquire environmental variables useful for decision-making, such as agriculture and forestry. Installing a WSN on the forest will allow the acquisition of ecological variables of high importance on risk analysis and fire detection. The presented paper addresses two types of WSN developed modules that can be used on the forest to detect fire ignitions using LoRaWAN to establish the communication between the nodes and a central system. The collaboration between these modules generate a heterogeneous WSN; for this reason, both are designed to complement each other. The first module, the HTW, has sensors that acquire data on a wide scale in the target region, such as air temperature and humidity, solar radiation, barometric pressure, among others (can be expanded). The second, the 5FTH, has a set of sensors with point data acquisition, such as flame ignition, humidity, and temperature. To test HTW and 5FTH, a LoRaWAN communication based on the Lorix One gateway is used, demonstrating the acquisition and transmission of forest data (simulation and real cases). Even in internal or external environments, these results allow validating the developed modules. Therefore, they can assist authorities in fighting wildfire and forest surveillance systems in decision-making.

Abstract
This article reports a pedagogical experience developed within the scope of a Ph.D. program in Electrical and Computer Engineering with application to Education. Starting with a contextualization on the evolution of computers and Computational Thinking, the article describes the platform used in this study - code.org -, highlighting the strengths that captivate the students. In the Case Study topic, we describe the study carried out, starting with a description of the students involved, followed by a description of the process and the analysis of the results, ending with the evaluation process performed by the students. The article ends concluding that code.org is a valid option to develop computational thinking at early-ages. Copyright

Abstract
The world's population is estimated to reach nine billion people by the year 2050, which indicates that agricultural productivity must increase sustainably. The mechanisation and automatisation of agricultural tasks is an essential step to face population growth. Ground robots have been developed along the last decade for several agricultural applications, being, the autonomous and safe navigation one of the hardest challenge in this development. Moving autonomously, a mobile platform involves different tasks, such as localisation, mapping, motion control, and path planning, a crucial step for autonomous operations. This article performs a survey of different applications for path planning techniques applied to various agricultural contexts. This paper analyses different agricultural applications and details about the employed path planning method. The conclusion indicates that path planning has been successfully applied to agrarian robots for field coverage and point-to-point navigation, being that coverage path planning is slightly more advanced in this field.

Abstract
Robotics will significantly impact large sectors of the economy with relatively low productivity, such as Agri-Food production. Deploying agricultural robots on the farm is still a challenging task. When it comes to localising the robot, there is a need for a preliminary map, which is obtained from a first robot visit to the farm. Mapping is a semi-autonomous task that requires a human operator to drive the robot throughout the environment using a control pad. Visual and geometric features are used by Simultaneous Localisation and Mapping (SLAM) Algorithms to model and recognise places, and track the robot's motion. In agricultural fields, this represents a time-consuming operation. This work proposes a novel solution-called AgRoBPP-bridge-to autonomously extract Occupancy Grid and Topological maps from satellites images. These preliminary maps are used by the robot in its first visit, reducing the need of human intervention and making the path planning algorithms more efficient. AgRoBPP-bridge consists of two stages: vineyards row detection and topological map extraction. For vineyards row detection, we explored two approaches, one that is based on conventional machine learning technique, by considering Support Vector Machine with Local Binary Pattern-based features, and another one found in deep learning techniques (ResNET and DenseNET). From the vineyards row detection, we extracted an occupation grid map and, by considering advanced image processing techniques and Voronoi diagrams concept, we obtained a topological map. Our results demonstrated an overall accuracy higher than 85% for detecting vineyards and free paths for robot navigation. The Support Vector Machine (SVM)-based approach demonstrated the best performance in terms of precision and computational resources consumption. AgRoBPP-bridge shows to be a relevant contribution to simplify the deployment of robots in agriculture.