During the last decades photovoltaic solar energy has continuously increased its share in the electricity mix and has already surpassed 5% globally. Even though photovoltaic (PV) installations are considered to require very little maintenance, their efficient exploitation relies on accounting for certain environmental factors that affect energy generation. One of these factors is the soiling of the PV surface, which could be observed in different forms, such as dust and bird droppings. In this study, visible spectrum data and machine learning algorithms were used for the identification of soiling. A methodology for preprocessing the images is proposed, which puts focus on any soiling of the PV surface. The performance of six classification machine learning algorithms is evaluated and compared—convolutional neural network (CNN), support vector machine (SVM), random forest (RF), k-nearest neighbor (kNN), naïve-Bayes, and decision tree. During the training and validation phase, RF proved to be the best-performing model with an F1 score of 0.935, closely followed by SVM, CNN, and kNN. However, during the testing phase, the trained CNN achieved the highest performance, reaching F1 = 0.913. SVM closely followed it with a score of 0.895, while the other two models returned worse results. Some results from the application of the optimal model after specific weather events are also presented in this study. They confirmed once again that the trained convolutional neural network can be successfully used to evaluate the soiling state of photovoltaic surfaces.

Animal welfare is a daily concern for livestock farmers. It is known that the activity of cows characterizes their general physiological state and deviations from the normal parameters could be an indicator of different kinds of diseases and conditions. This pilot study investigated the application of machine learning for identifying the behavioral activity of cows using a collar-mounted gyroscope sensor and compared the results with the classical accelerometer approach. The sensor data were classified into three categories, describing the behavior of the animals: “standing and eating”, “standing and ruminating”, and “laying and ruminating”. Four classification algorithms were considered—random forest ensemble (RFE), decision trees (DT), support vector machines (SVM), and naïve Bayes (NB). The training relied on manually classified data with a total duration of 6 h, which were grouped into 1s, 3s, and 5s piles. The obtained results showed that the RFE and DT algorithms performed the best. When using the accelerometer data, the obtained overall accuracy reached 88%; and when using the gyroscope data, the obtained overall accuracy reached 99%. To the best of our knowledge, no other authors have previously reported such results with a gyroscope sensor, which is the main novelty of this study.

This article describes a remote lab for fruit and vegetable drying, and discusses feedback from students who used the lab in their training. A major conclusion of the survey was that there is interest in the remote laboratory because it provides practical experience in the training of an automation and control engineer. It can be concluded that the remote laboratory has a positive effect on the students' perception and understanding of the real manufacturing process.

In this paper is analyzed the application of wireless technologies in Internet of Things (IoT) livestock monitoring systems. Initially are initialized the most important factors of the wireless technologies and characteristics of the wireless sensor networks, which limit their application. Next, are reviewed numerous applications of LoRa, ZigBee, WiFi, BLE, GPRS/3G/4G as well as other communication technologies. Their main areas of application, commonly used data sources, as well as the acceptable frequency and data volume of the sensor readings are evaluated. The results of this study provide the necessary information for designing a wide range of IoT systems, used for monitoring of indoor and outdoor bread animals, of the parameters of their environment and of different technological processes in livestock farms.

The degradation of pastures and meadows is a global problem with a wide range of impacts. It affects farmers in different ways, such as decreases in cattle production, milk yield, and forage quality. Still, it also has other side effects, such as loss of biodiversity, loss of resources, etc. In this study, the degradation of a semi-natural pasture near the village of Obichnik, Bulgaria, was evaluated using machine learning algorithms, and an unmanned aerial vehicle (UAV) obtained visual spectrum images. A high-quality (HQ) orthomosaic of the area was created and numerous regions of interest were manually marked for training and validation purposes. Three machine learning algorithms were used—Maximum likelihood, Random trees (RT), and Support Vector Machine (SVM). Furthermore, object-based and pixel-based approaches were utilized. The obtained results indicate that the object-based RT and SVM models provide significantly better accuracy, with their Cohen’s Kappa reaching 0.86 and 0.82, respectively. The performed classification showed that approximately 61% of the investigated pasture area is covered with grass, which indicates light-to-medium degradation.

In this article the application of genetic algorithm for tuning of HVAC cascade system is proposed. The tuning procedure for a cascade system is very time-consuming and practice shows that additional controller tuning is needed when classical method is used. The main problem in classical method is the interconnection between the parameters of the two controllers. The proposed optimal tuning procedure overcomes the disadvantages. It is based on the following criteria: minimum integral square error, minimum settling time and minimum overshoot. The best process quality is achieved with PI controller in the inner loop and a PID controller in the outer loop of the cascade HVAC system. The proposed method for simultaneous tuning of controller parameters in a cascade control system can be applied in different control systems.