The study focused on a method of detection for bee colony infestation with the Varroa destructor mite, based on the measurements of the chemical properties of beehive air. The efficient detection of varroosis was demonstrated. This method of detection is based on a semiconductor gas sensor array and classification module. The efficiency of detection was characterized by the true positive rate (TPR) and true negative rate (TNR). Several factors influencing the performance of the method were determined. They were: (1) the number and kind of sensors, (2) the classifier, (3) the group of bee colonies, and (4) the balance of the classification data set. Gas sensor array outperformed single sensors. It should include at least four sensors. Better results of detection were attained with a support vector machine (SVM) as compared with the k-nearest neighbors (k-NN) algorithm. The selection of bee colonies was important. TPR and TNR differed by several percent for the two examined groups of colonies. The balance of the classification data was crucial. The average classification results were, for the balanced data set: TPR = 0.93 and TNR = 0.95, and for the imbalanced data set: TP = 0.95 and FP = 0.53. The selection of bee colonies and the balance of classification data set have to be controlled in order to attain high performance of the proposed detection method.

Honey bees are subject to a number of stressors. In recent years, there has been a worldwide decline in the population of these insects. Losses raise a serious concern, because bees have an indispensable role in the food supply of humankind. This work is focused on the method of assessment of honey bee colony infestation by Varroa destructor. The approach allows to detect several categories of infestation: “Low”, “Medium” and “High”. The method of detection consists of two components: (1) the measurements of beehive air using a gas sensor array and (2) classification, which is based on the measurement data. In this work, we indicate the sensitivity of the bee colony infestation assessment to the timing of measurement data collection. It was observed that the semiconductor gas sensor responses to the atmosphere of a defined beehive, collected during 24 h, displayed temporal variation. We demonstrated that the success rate of the bee colony infestation assessment also altered depending on the time of day when the gas sensor array measurement was done. Moreover, it was found that different times of day were the most favorable to detect the particular infestation category. This result could indicate that the representation of the disease in the beehive air may be confounded during the day, due to some interferences. More studies are needed to explain this fact and determine the best measurement periods. The problem addressed in this work is very important for scheduling the beekeeping practices aimed at Varroa destructor infestation assessment, using the proposed method.

A serious factor that limits the environmental applications of drones is the disturbance of the air pollution concentration field by the drone propulsion system. This work presents a gas-sensing drone offering measurements that are unaffected by this phenomenon. The novel development was based on the idea that, during measurements, the sensing device should be spatially separated from a zone influenced by the drone’s rotors. To attain this goal, special equipment was designed that allows one to undock and lower the sensing device for measurement, lift it and dock for flight. The field experiments demonstrated the full functionality of the developed system and its superiority compared to a sensing platform mounted at the bottom of the drone. Higher measurement sensitivity and resolution were attained by lowering the sensing platform to the measurement point. This solution minimizes the rotor flow effect, ground effect, and pollution concentration field flattening. The test in real conditions confirmed that the designed construction assures drone stability. The presented technology may be an important step in developing effective mobile measurement tools that allow one to reach poorly accessible or dangerous places and perform measurements at a low cost and with high efficiency.

Urban beekeeping has become increasingly popular, despite having different objectives than the large-scale professional/commercial one. Not so much focused on financial profit, urban apiculture provides valuable educational opportunities, promotes the greening of cities, and creates environmental awareness. Many companies and institutions make it a valuable component of their public relations strategy. Urban beekeeping benefits may be enhanced by applying specialized technical infrastructure. In particular, the use of modern technologies attracts the younger generation to be involved in beekeeping. This paper aims to present the abilities of the automatic monitoring system, which meets the goals of urban beekeeping. The set of equipment consists of a sensor module, video cameras, a unit for data acquisition, and a system for data transmission, presentation, and visualization. The monitoring system allows for real-time observation of the beehive interior and entrance. It continuously measures selected physical and chemical parameters of the beehive atmosphere and provides a record of the beehive’s weight. Additionally, ambient air is monitored for reference. The video streaming and measurement results are available to the public online through the dedicated webpage. System performance was evaluated regarding the interactions with honey bees, sensor measurement characteristics, and data collection efficiency. Three months of operation in the urban apiary confirmed that the system is fully functional and fulfills its objectives. System development continues.

This work presents a multisensor device which is intended as an element of IoT for indoor environment (IE) monitoring. It is a portable, small-size, lightweight, energy-efficient direct-reading instrument. The device has an innovative design and construction. It offers real-time measurements of a wide spectrum of physical and chemical quantities (light intensity, temperature, relative humidity, pressure, CO₂ concentration, content of volatile organic compounds including formaldehyde, NO₂, and particulate matter), data storage (microSD; server as an option), transmission (WiFi; GSM and Ethernet as options), and visualization (smartphone application; PC as an option). Commercial low-cost sensors were utilized, which have been arranged in the individual sensing modules. In the case of gas sensors, dynamic exposure was chosen to ensure a minimum response time. The MQTT protocol was applied for data transmission and communication with other devices, as well as with the user. The multisensor device can collect huge amounts of data about the indoor environment to provide the respective information to the IoT. The device can be configured to control actuators of various auxiliary devices and equipment including external systems used for ventilation, heating, and air conditioning. The prototype is fully operational. The exemplary results of IE monitoring were shown.