Affordable Bimodal Optical Sensors to Spread the Use of Automated Insect Monitoring

Potamitis, Ilyas; Rigakis, Iraklis; Vidakis, Nectarios; Petousis, Markos; Weber, Michael

doi:10.1155/2018/3949415

Cited by 57 publications

(42 citation statements)

References 20 publications

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“…Concerning flying insects, mostly fruitflies were detected by using opto-electronic sensors in McPhail traps. They had an evolution from the simpler infrared beams to the more sophisticated ones (see in Table 1: Potamitis, Rigakis, Vidakis, Petousis and Weber [20]), which was based on the attributes of arthropods' wingbeats [21,28,29]. The species selectivity is a very big step in automatic monitoring of arthropods, since both practical IPM experts and field ecologists are interested in target pest species or the population dynamics of a particular species.…”

Section: Discussionmentioning

confidence: 99%

An Opto-Electronic Sensor-Ring to Detect Arthropods of Significantly Different Body Sizes

Balla

Flórián

Gergócs

et al. 2020

Sensors

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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.

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Section: Discussionmentioning

confidence: 99%

An Opto-Electronic Sensor-Ring to Detect Arthropods of Significantly Different Body Sizes

Balla

Flórián

Gergócs

et al. 2020

Sensors

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“…Generally, there is a massive advantage of detecting different scattering from 0.1% to 1% as opposed to detecting an extinction from 99.9% down to 99%. In point monitors and laboratory setups, multiple angular modes of side scatter and extinction can easily be added . In lidar, extinction can be estimated by the reduction in the post‐echo compared to the static value .…”

Section: Detection and Light Interactions With Aquatic‐ And Aerofaunamentioning

confidence: 99%

Photonic Monitoring of Atmospheric and Aquatic Fauna

Brydegaard

Svanberg

2018

Laser & Photonics Reviews

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Flying insects are of utmost importance in ecology and for human living conditions. Certain species serve as indispensable pollinators to ensure the availability of food stuffs, while others are dangerous vectors for spreading deadly diseases, such as malaria. Agricultural pests reduce the yield of crops, and their abatement through pesticides cause many additional problems. Birds and bats are frequently carriers of diseases, which, especially for long‐distance migrants, cause serious consequences. Clearly, there is high motivation to be able to effectively identify and quantify flying fauna. Here, the emerging field of optics and laser‐based monitoring of flying fauna is reviewed with an emphasis on remote sensing based on pulsed and contineous wave (CW) lidar systems, and how they complement existing radar techniques. Furthermore, ground‐truth laboratory studies are covered. Wing‐beat and overtone spectra as well as reflectance, depolarization and fluorescence properties are studied. The aquatic environment is for many reasons less accessible for optical studies, but is clearly also of great importance. Phytoplankton constitute the start of the aquatic food chain, followed by zooplankton and a long chain of higher animals, including fish, an important part of the human food supply. Finally, how optical monitoring can complement sonar and sampling techniques is discussed.

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“…However, solutions for sensor-based monitoring of insects and other invertebrates in their natural environment are emerging (34). The innovation and development is primarily driven by agricultural research to predict occurrence and abundance of beneficial and pest insect species of economic importance (35)(36)(37), to provide more efficient screening of natural products for invasive insect species (38), or to monitor disease vectors such as mosquitos (39,40). The most commonly used sensors are cameras, radar, and microphones.…”

Section: Sensor-based Insect Monitoringmentioning

confidence: 99%

Deep learning and computer vision will transform entomology

Høye

Ärje

Bjerge

et al. 2020

Preprint

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ABSTRACTMost animal species on Earth are insects, and recent reports suggest that their abundance is in drastic decline. Although these reports come from a wide range of insect taxa and regions, the evidence to assess the extent of the phenomenon is still sparse. Insect populations are challenging to study and most monitoring methods are labour intensive and inefficient. Advances in computer vision and deep learning provide potential new solutions to this global challenge. Cameras and other sensors that can effectively, continuously, and non-invasively perform entomological observations throughout diurnal and seasonal cycles. The physical appearance of specimens can also be captured by automated imaging in the lab. When trained on these data, deep learning models can provide estimates of insect abundance, biomass, and diversity. Further, deep learning models can quantify variation in phenotypic traits, behaviour, and interactions. Here, we connect recent developments in deep learning and computer vision to the urgent demand for more cost-efficient monitoring of insects and other invertebrates. We present examples of sensor-based monitoring of insects. We show how deep learning tools can be applied to the big data outputs to derive ecological information and discuss the challenges that lie ahead for the implementation of such solutions in entomology. We identify four focal areas, which will facilitate this transformation: 1) Validation of image-based taxonomic identification, 2) generation of sufficient training data, 3) development of public, curated reference databases, and 4) solutions to integrate deep learning and molecular tools.Significance statementInsect populations are challenging to study, but computer vision and deep learning provide opportunities for continuous and non-invasive monitoring of biodiversity around the clock and over entire seasons. These tools can also facilitate the processing of samples in a laboratory setting. Automated imaging in particular can provide an effective way of identifying and counting specimens to measure abundance. We present examples of sensors and devices of relevance to entomology and show how deep learning tools can convert the big data streams into ecological information. We discuss the challenges that lie ahead and identify four focal areas to make deep learning and computer vision game changers for entomology.

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Affordable Bimodal Optical Sensors to Spread the Use of Automated Insect Monitoring

Cited by 57 publications

References 20 publications

An Opto-Electronic Sensor-Ring to Detect Arthropods of Significantly Different Body Sizes

An Opto-Electronic Sensor-Ring to Detect Arthropods of Significantly Different Body Sizes

Photonic Monitoring of Atmospheric and Aquatic Fauna

Deep learning and computer vision will transform entomology

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