An Opto-Electronic Sensor for Detecting Soil Microarthropods and Estimating Their Size in Field Conditions

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

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.

Automated Surveillance of Lepidopteran Pests with Smart Optoelectronic Sensor Traps

Several lepidopterans are pests in horticulture and pose biosecurity risks to trading countries worldwide. Efficient species-specific semiochemical lures are available for some of these pests, facilitating the implementation of surveillance programmes via trapping networks. These networks have a long history of success in detecting incursions of invasive species; however, their reliance on manual trap inspections makes these surveillance programmes expensive to run. Novel smart traps integrating sensor technology are being developed to detect insects automatically but are so far limited to expensive camera-based sensors or optoelectronic sensors for fast-moving insects. Here, we present the development of an optoelectronic sensor adapted to a delta-type trap to record the low wing-beat frequencies of Lepidoptera, and remotely send real-time digital detection via wireless communication. These new smart traps, combined with machine-learning algorithms, can further facilitate diagnostics via species identification through biometrics. Our laboratory and field trials have shown that moths flying in/out of the trap can be detected automatically before visual trap catch, thus improving early detection. The deployment of smart sensor traps for biosecurity will significantly reduce the cost of labour by directing trap visits to the locations of insect detection, thereby supporting a sustainable and low-carbon surveillance system.

Automated Applications of Acoustics for Stored Product Insect Detection, Monitoring, and Management

Simple Summary

A variety of different acoustic devices has been commercialized for detection of hidden insect infestations in stored products, trees, and soil, including a recently introduced device demonstrated in this report to successfully detect rice weevil immatures and adults in grain. Several of the systems have incorporated digital signal processing and statistical analyses such as neural networks and machine learning to distinguish targeted pests from each other and from background noise, enabling automated monitoring of the abundance and distribution of pest insects in stored products, and potentially reducing the need for chemical control. Current and previously available devices are reviewed in the context of the extensive research in stored product insect acoustic detection since 2011. It is expected that further development of acoustic technology for detection and management of stored product insect pests will continue, facilitating automation and decreasing detection and management costs.

Abstract

Acoustic technology provides information difficult to obtain about stored insect behavior, physiology, abundance, and distribution. For example, acoustic detection of immature insects feeding hidden within grain is helpful for accurate monitoring because they can be more abundant than adults and be present in samples without adults. Modern engineering and acoustics have been incorporated into decision support systems for stored product insect management, but with somewhat limited use due to device costs and the skills needed to interpret the data collected. However, inexpensive modern tools may facilitate further incorporation of acoustic technology into the mainstream of pest management and precision agriculture. One such system was tested herein to describe Sitophilus oryzae (Coleoptera: Curculionidae) adult and larval movement and feeding in stored grain. Development of improved methods to identify sounds of targeted pest insects, distinguishing them from each other and from background noise, is an active area of current research. The most powerful of the new methods may be machine learning. The methods have different strengths and weaknesses depending on the types of background noise and the signal characteristic of target insect sounds. It is likely that they will facilitate automation of detection and decrease costs of managing stored product insects in the future.

Detecting Soil Microarthropods with a Camera-Supported Trap

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.

An Opto-electronic Sensor-ring to Detect Arthropods of Significantly Different Body Sizes

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.

In-Vivo Vibroacoustic Surveillance of Trees in the Context of the IoT

This work introduces a device for long term systematic monitoring of trees against borers. A widely applied way to detect wood-boring insects is to insert a piezoelectric probe with an uncoated waveguide in the tree trunk and listen for locomotion or feeding sounds through headphones. This approach has several shortcomings: (a) frequent manual inspection of trees is costly and impractical to scale to hundreds or thousands of trees, (b) the larvae could be present but inactive during the inspection time and, (c) when the trees are in urban environments the background noise can be significant and can mask the feeble sounds of wood-boring insects even with the use of specialized headphones. We introduce a remotely controlled device that records and wirelessly transmits on a scheduled basis short recordings of the internal vibrations of a tree to a server. The user can listen remotely or process the recording automatically to infer the infestation state of the tree with wood-boring insects that feed or move inside the tree. When integrated within the IoT framework this device can scale up to automatically monitoring the trees of the entire city. The proposed approach led to detection results in field trials of the pests Xylotrechus chinensis (Chevrolat) (Cerambycidae) and Rhynchophorus ferrugineus Olivier (Coleoptera: Curculionidae).