A Smart Pest Control System Based on Automated Reverse Aerodynamics and Suction Technology

Many diseases (including malaria, one of the deadliest diseases especially to those living in developing nations which kills hundreds on daily basis) are transmitted by household insects. Mosquitoes, one of these insects, bites humans and sucks their blood and are pathogens of several diseases (especially malaria) which causes ill health and many times lead to death if it is not well treated. This necessitates the need to smartly eliminate them without chemicals, which may be harmful to human health. This research aims at the design and development of a smart insect trap which attracts and contains household insects and pests automatically. The device incorporates a dual power supply that powers and charges the system, sensors which detects the presence of insects and triggers the vacuum suction mechanism automatically, a charge controller for controlling the charging of the installed battery, an ultraviolet light emitting section that lures the insects to the device, a chamber for containment and dehydration of the insect, and a smart microcontroller based control system. The developed design is smart, environmentally safe, portable and highly cost effective yet very efficient in eliminating household pathogen-transmitting insects and pests Keywords: reverse aerodynamics, pest control, smart systems, insect trap, vacuum suction, mosquitoes.

Vegetable Pest Monitoring Using Insect Trap Lights

The results of improving the design of autonomous LED insect trap lights developed by the Federal Research Center for Plant Biological Protection are provided. The flying dynamics of the summer Helicoverpa armigera is shown. It was found that the insect trap light attracted 2.6 times more phytophagous specimens than pheromon traps. A separating element of a insect trap light been developed, which makes it possible to reduce 50 times the number of captured representatives of useful and indifferent entomofauna. The efficiency of battery charging has been increased by 43%.

No Evidence of SARS-CoV-2 Among Flies or Cockroaches in COVID-19 Positive Households

Background:The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in a pandemic of coronavirus disease (COVID-19), which continues to cause infections and mortality worldwide. SARS-CoV-2 is transmitted primarily via the respiratory route and has experimentally been found to be stable on surfaces for multiple days. Flies (Diptera) and other arthropods mechanically transmit several pathogens, including turkey coronavirus. A previous experimental study demonstrated house flies, Musca domestica, can mechanically transmit SARS-CoV-2, but the ability of flies in general to acquire and deposit this virus in natural settings has not been explored. The purpose of this study was to explore the possibility of mechanical transmission of SARS-CoV-2 by peridomestic insects and their potential as a xenosurveillance tool for detection of the virus.Methods:In order to optimize chances of viral detection, flies were trapped in homes where at least one confirmed human COVID-19 case(s) resided. Sticky and liquid baited fly traps were deployed inside and outside of the homes of SARS-CoV-2 human cases in Brazos, Bell, and Montgomery Counties, from June to September 2020. Flies from sticky traps were identified, pooled by taxa, homogenized, and tested for the presence of SARS-CoV-2 RNA using qRT-PCR. Liquid traps were drained, and the collected fluid similarly tested after RNA concentration. Experimental viral detection pipeline and viral inactivation were confirmed in a Biosafety Level 3 lab. As part of a separate ongoing study, companion animals in the home were sampled and tested for SARS-CoV-2 on the same day of insect trap deployment.Results:We processed the contents of 133 insect traps from 44 homes, which contained over 1,345 individual insects of 11 different Diptera families and Blattodea.These individuals were grouped into 243 pools, and all tested negative for SARS-CoV-2 RNA. Dead flies exposed to SARS-CoV-2 in a BSL3 lab were processed using the same methods and viral RNA was detected by RT-PCR. Fourteen traps in seven homes were deployed on the day that cat or dog samples tested positive for SARS-CoV-2 RNA by nasal, oral, body, or rectal samples.Conclusions:This study presents evidence that biting and non-biting flies are not likely to contribute to mechanical transmission of SARS-CoV-2 or be useful in xenosurveillance for SARS-CoV-2.

Developing a Phototactic Electrostatic Insect Trap Targeting Whiteflies, Leafminers, and Thrips in Greenhouses

Our aim was to develop an electrostatic apparatus to lure and capture silverleaf whiteflies (Bemisia tabaci), vegetable leafminers (Liriomyza sativae), and western flower thrips (Frankliniella occidentalis) that invade tomato greenhouses. A double-charged dipolar electric field producer (DD-EFP) was constructed by filling water in two identical transparent soft polyvinyl chloride tubes arrayed in parallel with fixed separation, and then, inserting the probes of grounded negative and positive voltage generators into the water of the two tubes to generate negatively and positively charged waters, respectively. These charged waters electrified the outer surfaces of the opposite tubes via dielectric polarization. An electric field formed between the oppositely charged tubes. To lure these phototactic insects, the water was colored yellow using watercolor paste, then introduced into the transparent insulator tubes to construct the yellow-colored DD-EFP. This apparatus lured insects in a manner similar to commercially available yellow sticky traps. The yellow-colored DD-EFP was easily placed as a movable upright screen along the plants, such that invading pests were preferentially attracted to the trap before reaching the plants. Furthermore, pests settling on the plants were attracted to the apparatus, which used a plant-tapping method to drive them off the plants. Our study provided an experimental basis for developing an electrostatic device to attract and capture insects that enter greenhouses.

How to Count Bugs: A Method to Estimate the Most Probable Absolute Population Density and Its Statistical Bounds from a Single Trap Catch

Knowledge of insect population density is crucial for establishing management and conservation tactics and evaluating treatment efficacies. Here, we propose a simple and universal method for estimating the most probable absolute population density and its statistical bounds. The method is based on a novel relationship between experimentally measurable characteristics of insect trap systems and the probability to catch an insect located a given distance away from the trap. The generality of the proposed relationship is tested using 10 distinct trapping datasets collected for insects from 5 different orders and using major trapping methods, i.e., chemical-baited traps and light. For all datasets, the relationship faithfully (R=0.91) describes the experiment. The proposed approach will take insect detection and monitoring to a new, rigorously quantitative level. It will improve conservation and management, while driving future basic and applied research in population and chemical ecology.

Comparison of Biological Activity of Field Isolates of Steinernema feltiae with a Commercial S. feltiae Biopesticide Product

Insect trap studies were carried out to determine the presence of entomopathogenic nematodes (EPN) from the family Steinernematidae in the soils of Poland and to compare the biological activities of field nematode isolates with nematodes from commercial biopesticide. The fauna of these organisms in central Poland is poorly studied in both taxonomic and biological terms. Tilled soils representative of this region were sampled from cultivated fields. EPN were isolated from soil samples under laboratory conditions and identified using a key for species identification and molecular analysis. Basic morphometric parameters of infective juveniles and adult males of the first generation were determined. The research showed that males and infective juveniles Steinernema feltiae from Łoniów were the largest. The smallest infective juveniles were found in the isolate from Oblasy, and the smallest males in the isolate from Danków. In Poland, new field isolates showed close genetic similarity to other S. feltiae isolates. The research showed that the field isolates from Poland had greater infectivity and rate of reproduction compared with nematodes from the commercial biopesticide. The findings indicate the potential use of field S. feltiae isolates from Poland (iso1Lon, iso1Dan and iso1Obl) to develop new biopesticide products.

Sticky Pi, an AI-powered smart insect trap for community chronoecology

Circadian clocks are paramount to insect survival and drive many aspects of their physiology and behaviour. While insect circadian behaviours have been extensively studied in the laboratory, their circadian activity within natural settings is poorly understood. The study of circadian activity necessitates measuring biological variables (e.g., locomotion) at high frequency (i.e., at least several times per hour) over multiple days, which has mostly confined insect chronobiology to the laboratory. In order to study insect circadian biology in the field, we developed the Sticky Pi, a novel, autonomous, open-source, insect trap that acquires images of sticky cards every twenty minutes. Using custom deep-learning algorithms, we automatically and accurately scored where, when and which insects were captured. First, we validated our device in controlled laboratory conditions with a classic chronobiological model organism, Drosophila melanogaster. Then, we deployed an array of Sticky Pis to the field to characterise the daily activity of an agricultural pest, Drosophila suzukii, and its parasitoid wasps. Finally, we demonstrate the wide scope of our smart trap by describing the sympatric arrangement of insect temporal niches in a community, without targeting particular taxa a priori. Together, the automatic identification and high sampling rate of our tool provide biologists with unique data that impacts research far beyond chronobiology; with applications to biodiversity monitoring and pest control as well as fundamental implications for phenology, behavioural ecology, and ecophysiology. We released the Sticky Pi project as an open community resource on https://doc.sticky-pi.com.

Embedded System-Based Sticky Paper Trap with Deep Learning-Based Insect-Counting Algorithm

Flying insect detection, identification, and counting are the key components of agricultural pest management. Insect identification is also one of the most challenging tasks in agricultural image processing. With the aid of machine vision and machine learning, traditional (manual) identification and counting can be automated. To achieve this goal, a particular data acquisition device and an accurate insect recognition algorithm (model) is necessary. In this work, we propose a new embedded system-based insect trap with an OpenMV Cam H7 microcontroller board, which can be used anywhere in the field without any restrictions (AC power supply, WIFI coverage, human interaction, etc.). In addition, we also propose a deep learning-based insect-counting method where we offer solutions for problems such as the “lack of data” and “false insect detection”. By means of the proposed trap and insect-counting method, spraying (pest swarming) could then be accurately scheduled.