In digital farming, the use of technology to increase agricultural production through automated tasks has recently integrated the development of AgBots for more reliable data collection using autonomous navigation. These AgBots are equipped with various sensors such as GNSS, cameras, and LiDAR, but these sensors can be prone to limitations such as low accuracy for under-canopy navigation with GNSS, sensitivity to outdoor lighting and platform vibration with cameras, and LiDAR occlusion issues. In order to address these limitations and ensure robust autonomous navigation, this paper presents a sensor selection methodology based on the identification of environmental conditions using sensor data. Through the extraction of features from GNSS, images, and point clouds, we are able to determine the feasibility of using each sensor and create a selection vector indicating its viability. Our results demonstrate that the proposed methodology effectively selects between the use of cameras or LiDAR within crops and GNSS outside of crops, at least 87% of the time. The main problem found is that, in the transition from inside to outside and from outside to inside the crop, GNSS features take 20 s to adapt. We compare a variety of classification algorithms in terms of performance and computational cost and the results show that our method has higher performance and lower computational cost. Overall, this methodology allows for the low-cost selection of the most suitable sensor for a given agricultural environment.

In the context of the new industrial revolution - Industry 4.0, the smart factory concept brought, to manufacturing, the idea of using large amounts of data acquired from a machining process and a set of mathematical techniques, discovering correlations, patterns, or trends in this database. Thus, machine tools are the focus of research in order to monitor and analyze the quality of the machining process based on data from embedded sensors. Based on this strategy, a signature process was created, that consists in capturing behavior patterns of a machine, such as machining conditions, machining quality, or tool wear. This article deal with a comparison between three Multinomial Logistic Regressions: the first using only time domain data, the second using only frequency domain data, and finally, the third using time and frequency domain data to identify the pattern of feed rate, depth of cut, and material being machined. It was observed that the methods had a precision of 96.25%, 37.92%, and 99.58%, respectively, showing that this methodology has great predictive efficiency and could be used to monitor the cutting parameters and material studied in this paper.

The advancement of technology and the needs imposed by the market, such as high surface quality and reduction of manufacturing costs, have made machine tools the focus of numerous researches, aimed at improving their performance in different industrial processes. Some of these researches have as objective the instrumentation of the machine, aiming at the monitoring of some variables related to the machining process. To carry out such monitoring, systems with sensors are used that allow the measurement of physical quantities that are closely linked to the cutting parameters (feed, cutting speed and cutting depth). These parameters are of fundamental importance for the process and help in the good machinability of the materials, an error in the configuration of these parameters, before or during the process, can cause wear or even breakage of the tool. The main quantities used in monitoring machine tools are mechanical vibration (correlated with the rotation of the workpiece and the movement and wear of the tool), temperature (which varies mainly with the plastic deformation at the workpiece-tool interface and the friction at the workpiece-tool and chip-tool interfaces) and sound vibration (related to stiffness and friction between materials). This article presents the development of a system that monitors acceleration, temperature, and sound emitted during an external turning process, using an embedded system (data acquisition and processing boards) with low cost and high computational power. To determine the process signature that relates temperature, vibration and audio to the machined material and to the cutting parameters (feed and depth of cut), an Artificial Neural Network (ANN) was used, which obtained an accuracy rate of approximately 80%, indicating that the monitoring system is capable of generating data that allows the determination of the process signature.