Real-time monitoring of crop responses to environmental deviations represents a new avenue for applications of remote and proximal sensing. Combining the high-throughput devices with novel machine learning (ML) approaches shows promise in the monitoring of agricultural production. The 3 × 2 multispectral arrays with responses at 610 and 680 nm (red), 730 and 760 nm (red-edge) and 810 and 860 nm (infrared) spectra were used to assess the occurrence of leaf rolling (LR) in 545 experimental maize plots measured four times for calibration dataset (n = 2180) and 145 plots measured once for external validation. Multispectral reads were used to calculate 15 simple normalized vegetation indices. Four ML algorithms were assessed: single and multilayer perceptron (SLP and MLP), convolutional neural network (CNN) and support vector machines (SVM) in three validation procedures, which were stratified cross-validation, random subset validation and validation with external dataset. Leaf rolling occurrence caused visible changes in spectral responses and calculated vegetation indexes. All algorithms showed good performance metrics in stratified cross-validation (accuracy >80%). SLP was the least efficient in predictions with external datasets, while MLP, CNN and SVM showed comparable performance. Combining ML with multispectral sensing shows promise in transition towards agriculture based on data-driven decisions especially considering the novel Internet of Things (IoT) avenues.

Virtualization has experienced a dramatic expansion recently and today is ubiquitous in modern IT industry since it provides numerous benefits to companies and individual users. It increases efficiency, flexibility and scalability of IT equipment by enabling different software-based environments on a single physical hardware. Each virtual machine is a separate instance that is completely independent and separated from the computer hardware and it runs on emulated hardware. Emulated hardware is managed by virtualization tool that provides lower resources when compared to physical hardware. This paper presents a performance evaluation of three different virtual machines run by three recent versions of Windows operating system, namely Windows 7^TM Professional, Windows 8.1^TM Professional and Windows 10^TM Professional, on a host computer system run by Linux Ubuntu. Performance measurement results show that Window 7 is the most suitable virtual operating system since it obtains the best performance when run on a Linux host.

Fires represent a serious threat to humans, property, and the environment. Timely detection and effective extinguishing are essential to reduce harmful consequences. This paper focuses on the analysis of contemporary methods, technologies, datasets and approaches for fire detection, prevention and suppression. Fire detection methods are divided into those applicable to indoor and outdoor settings, with special emphasis on challenges and solutions in each category. The methods reviewed in this paper include those based on image analysis, which include advanced image processing and machine learning techniques, enabling systems to recognize visual signs of fire. Furthermore, a review of publicly available datasets was conducted to provide a better understanding of currently existing fire analysis and detection methods. Next, an evaluation of sensor-based fire detection approaches was carried out, focusing on their capability to identify a wide range of combustion products. Fire suppression methods, including traditional practices and contemporary technologies such as robotic and drone systems, are reviewed, with particular emphasis on extinguishing fires caused by lithium-ion batteries, which require a unique approach due to their specific characteristics. The goal of this paper is to outline the current state of research and existing technologies, identify their advantages and disadvantages, and highlight potential areas for further research and development.

Fire protection is one of the activities that follow the development of technology in real-time and implements all the innovations of a detection system. This paper presents a unique solution for the development of an autonomous robot for the prevention, detection, and extinguishing of fires by studying the problem of choosing the optimal early-detection sensor in the infrared part of the spectrum, which characterizes the highest level of excitation in the state of prevention. The robot is equipped with several different sensors arranged in a hierarchical structure. Thermal detection has proven to be a significant investment that can be adapted to the different complexity of the objects to be protected, taking into account image processing and modular implementation of the required sensors. To this end, it is necessary to calibrate systems for different thermal cameras. The calibration procedure on seven cameras and two pyrometers resulted in data required for input-data correction and anomaly detection. The results of the analysis confirmed that devices of a higher price range have a lower deviation from the reference value compared to low-cost technical solutions. At the same time, results were observed indicating malfunction of more expensive devices, whose data exceed the specified nominal accuracy. Thanks to the performed calibration procedure and the obtained results, the observed problem is not an obstacle for implementation in an autonomous robotic system and can be used to correct the input data required for computer analysis.