Automatic Detection of Moths (Lepidoptera) with a Funnel Trap Prototype

SIMPLE SUMMARY: Modern pest control is based on correct timing protection and the avoidance of unnecessary insecticide use. Therefore, we must know the exact time of pest gradation and activity. Using automatic insect traps allows insect activity detection without considerable human intervention. The proper use of automatic catching, counting, and data forwarding in the field has not been fully resolved yet. This study presents a modified trap prototype used for automatically catching and counting flying insects, mostly pest moths, in the field. Here, we present the modifications to the construction of our trap design. During the pilot field tests, the new probe prototypes provided real-time, time-series data sets for each of the six pest moth species monitored. Environmental noise was reduced and filtered out. Detected data were forwarded to a web interface where end-users could further process or download the data. With this new device, moths’ daily and seasonal flight patterns could be followed and described. This knowledge may provide an opportunity for more precise forecasts of population outbreaks. ABSTRACT: 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.