Arthropods, including pollinators and pests, have high positive and negative impacts on human well-being and the economy, and there is an increasing need to monitor their activity and population growth. The monitoring of arthropod species is a time-consuming and financially demanding process. Automatic detection can be a solution to this problem. Here, we describe the setup and operation mechanism of an infrared opto-electronic sensor-ring, which can be used for both small and large arthropods. The sensor-ring consists of 16 infrared (IR) photodiodes along a semicircle in front of an infrared LED. Using 3D printing, we constructed two types of sensor-ring: one with a wider sensing field for detection of large arthropods (flying, crawling, surface-living) in the size range of 2–35 mm; and another one with a narrower sensing field for soil microarthropods in the size range of 0.1–2 mm. We examined the detection accuracy and reliability of the two types of sensor-ring in the laboratory by using particles, and dead and living arthropods at two different sensitivity levels. For the wider sensor-ring, the 95% detectability level was reached with grain particles of 0.9 mm size. This result allowed us to detect all of the macroarthropods that were applied in the tests and that might be encountered in pest management. In the case of living microarthropods with different colors and shapes, when we used the narrower sensor-ring, we achieved the 95% detectability level at 1.1 mm, 0.9 mm, and 0.5 mm in the cases of F. candida, H. nitidus, and H. aculeifer, respectively. The unique potential of arthropod-detecting sensors lies in their real-time measurement system; the data are automatically forwarded to the server, and the end-user receives pest abundance data daily or even immediately. This technological innovation will allow us to make pest management more effective.

Soil microarthropods have a pivotal role in soil nitrogen cycling in that they affect microbial decomposers. A high abundance of microarthropods may increase the mobility of inorganic nitrogen ions in the soil, mainly in nitrogen‐limited habitats. However, it is difficult to study ecological processes with small‐sized, soil‐dwelling arthropods. The effects of soil microarthropods on nitrogen cycling have mainly been studied in laboratory microcosm experiments. Therefore, we face many practical issues in investigating these effects under field conditions that remain to be resolved. We developed an open‐field mesocosm setup with growing plants. In a two‐part experiment, spring wheat and grass species were grown in chernozem and sandy soils. Leached ammonium and nitrate ions were measured with percolation lysimeters. Half of the mesocosms included natural assemblages, and the other half included less abundant Acari and Collembola assemblages. The application of nitrogen fertilization assured differences in nitrogen sources. We found a large difference in ammonium and nitrate leaching between the two soil types. In chernozem soil, the leached ion concentrations were higher in mesocosms with more abundant mite and springtail assemblages. The expected patterns were less pronounced in sandy soil. Adding nitrogen fertilizer did not modify the effects of soil microarthropods. Open‐field mesocosms are promising for studying the role of soil‐dwelling mesofauna in ecological processes. We solved the problem of keeping mesofauna abundance lower in treated plots than that in control plots. Plants successfully grew in our semi‐closed systems with functioning percolation lysimeters. The use of the equipment in the experiments in this study helped reveal that the role of soil‐dwelling microarthropods in nitrogen cycling depends on the soil type and not on the application of nitrogen fertilizer.

Monitoring insect populations is essential to optimise pest control with the correct protection timing and the avoidance of unnecessary insecticide use. Modern real-time monitoring practices use automatic insect traps, which are expected to be able to estimate the population sizes of pest animals with high species specificity. There are many solutions to overcome this challenge; however, there are only a few data that consider their accuracy under field conditions. This study presents an opto-electronic device prototype (ZooLog VARL) developed by us. A pilot field study evaluated the precision and accuracy of the data filtering using an artificial neural network(ANN) and the detection accuracy of the new probes. The prototype comprises a funnel trap, sensor-ring, and data communication system. The main modification of the trap was a blow-off device that prevented the escape of flying insects from the funnel. These new prototypes were tested in the field during the summer and autumn of 2018, detecting the daily and monthly flight of six moth species (Agrotis segetum, Autographa gamma, Helicoverpa armigera, Cameraria ohridella, Grapholita funebrana, Grapholita molesta). The accuracy of ANN was always higher than 60%. In the case of species with larger body sizes, it reached 90%. The detection accuracy ranged from 84% to 92% on average. These probes detected the real-time catches of the moth species. Therefore, weekly and daily patterns of moth flight activity periods could be compared and displayed for the different species. This device solved the problem of multiple counting and gained a high detection accuracy in target species cases. ZooLog VARL probes provide the real-time, time-series data sets of each monitored pest species. Further evaluation of the catching efficiency of the probes is needed. However, the prototype allows us to follow and model pest dynamics and may make more precise forecasts of population outbreaks.

There is an increasing need to monitor activity and population growth of arthropods; however, this is a time-consuming and financially demanding process. Using sensors to detect arthropods in the field can help to follow their dynamics in time. Improving our earlier device, we developed a new camera-supported probe to detect soil microarthropods. An opto-electronic sensor ring detects the caught microarthropod individuals what activates a camera. The camera takes pictures of a specimen when it arrives in the camera chamber. A vacuum device was built into the probe which pumps up the specimen from the probe to a sample container. Here, we describe the construction and operation of the probe. We investigated the precision of the process in a laboratory experiment using living microarthropods and evaluated the accuracy of the probes in a semi-natural investigation when environmental noise was present. Under semi-natural conditions, the percentages of success, i.e., the photographed specimens compared to the caught ones, were between 60% and 70% at the investigated taxa. The automatic camera shooting helped in distinguishing insects from irrelevant detections while collecting the trapped insects allowed species-level determination. This information together serves as a basis for the automatic visual recognition of microarthropod species.