Iraklis Rigakis

Insect Biometrics: Optoacoustic Signal Processing and Its Applications to Remote Monitoring of McPhail Type Traps

Monitoring traps are important components of integrated pest management applied against important fruit fly pests, including Bactrocera oleae (Gmelin) and Ceratitis capitata (Widemann), Diptera of the Tephritidae family, which effect a crop-loss/per year calculated in billions of euros worldwide. Pests can be controlled with ground pesticide sprays, the efficiency of which depends on knowing the time, location and extent of infestations as early as possible. Trap inspection is currently carried out manually, using the McPhail trap, and the mass spraying is decided based on a decision protocol. We introduce the term ‘insect biometrics’ in the context of entomology as a measure of a characteristic of the insect (in our case, the spectrum of its wingbeat) that allows us to identify its species and make devices to help face old enemies with modern means. We modify a McPhail type trap into becoming electronic by installing an array of photoreceptors coupled to an infrared emitter, guarding the entrance of the trap. The beating wings of insects flying in the trap intercept the light and the light fluctuation is turned to a recording. Custom-made electronics are developed that are placed as an external add-on kit, without altering the internal space of the trap. Counts from the trap are transmitted using a mobile communication network. This trap introduces a new automated remote-monitoring method different to audio and vision-based systems. We evaluate our trap in large number of insects in the laboratory by enclosing the electronic trap in insectary cages. Our experiments assess the potential of delivering reliable data that can be used to initialize reliably the spraying process at large scales but to also monitor the impact of the spraying process as it eliminates the time-lag between acquiring and delivering insect counts to a central agency.

Automated Surveillance of Fruit Flies

Insects of the Diptera order of the Tephritidae family cause costly, annual crop losses worldwide. Monitoring traps are important components of integrated pest management programs used against fruit flies. Here we report the modification of typical, low-cost plastic traps for fruit flies by adding the necessary optoelectronic sensors to monitor the entrance of the trap in order to detect, time-stamp, GPS tag, and identify the species of incoming insects from the optoacoustic spectrum analysis of their wingbeat. We propose that the incorporation of automated streaming of insect counts, environmental parameters and GPS coordinates into informative visualization of collective behavior will finally enable better decision making across spatial and temporal scales, as well as administrative levels. The device presented is at product level of maturity as it has solved many pending issues presented in a previously reported study.

The Electronic McPhail Trap

Certain insects affect cultivations in a detrimental way. A notable case is the olive fruit fly (Bactrocera oleae (Rossi)), that in Europe alone causes billions of euros in crop-loss/per year. Pests can be controlled with aerial and ground bait pesticide sprays, the efficiency of which depends on knowing the time and location of insect infestations as early as possible. The inspection of traps is currently carried out manually. Automatic monitoring traps can enhance efficient monitoring of flying pests by identifying and counting targeted pests as they enter the trap. This work deals with the hardware setup of an insect trap with an embedded optoelectronic sensor that automatically records insects as they fly in the trap. The sensor responsible for detecting the insect is an array of phototransistors receiving light from an infrared LED. The wing-beat recording is based on the interruption of the emitted light due to the partial occlusion from insects wings as they fly in the trap. We show that the recordings are of high quality paving the way for automatic recognition and transmission of insect detections from the field to a smartphone. This work emphasizes the hardware implementation of the sensor and the detection/counting module giving all necessary implementation details needed to construct it.

Automated Remote Insect Surveillance at a Global Scale and the Internet of Things

Τhe concept of remote insect surveillance at large spatial scales for many serious insect pests of agricultural and medical importance has been introduced in a series of our papers. We augment typical, low-cost plastic traps for many insect pests with the necessary optoelectronic sensors to guard the entrance of the trap to detect, time-stamp, GPS tag, and—in relevant cases—identify the species of the incoming insect from their wingbeat. For every important crop pest, there are monitoring protocols to be followed to decide when to initiate a treatment procedure before a serious infestation occurs. Monitoring protocols are mainly based on specifically designed insect traps. Traditional insect monitoring suffers in that the scope of such monitoring: is curtailed by its cost, requires intensive labor, is time consuming, and an expert is often needed for sufficient accuracy which can sometimes raise safety issues for humans. These disadvantages reduce the extent to which manual insect monitoring is applied and therefore its accuracy, which finally results in significant crop loss due to damage caused by pests. With the term ‘surveillance’ we intend to push the monitoring idea to unprecedented levels of information extraction regarding the presence, time-stamping detection events, species identification, and population density of targeted insect pests. Insect counts, as well as environmental parameters that correlate with insects’ population development, are wirelessly transmitted to the central monitoring agency in real time and are visualized and streamed to statistical methods to assist enforcement of security control to insect pests. In this work, we emphasize how the traps can be self-organized in networks that collectively report data at local, regional, country, continental, and global scales using the emerging technology of the Internet of Things (IoT). This research is necessarily interdisciplinary and falls at the intersection of entomology, optoelectronic engineering, data-science, and crop science and encompasses the design and implementation of low-cost, low-power technology to help reduce the extent of quantitative and qualitative crop losses by many of the most significant agricultural pests. We argue that smart traps communicating through IoT to report in real-time the level of the pest population from the field straight to a human controlled agency can, in the very near future, have a profound impact on the decision-making process in crop protection and will be disruptive of existing manual practices. In the present study, three cases are investigated: monitoring Rhynchophorus ferrugineus (Olivier) (Coleoptera: Curculionidae) using (a) Picusan and (b) Lindgren trap; and (c) monitoring various stored grain beetle pests using the stored-grain pitfall trap. Our approach is very accurate, reaching 98–99% accuracy on automatic counts compared with real detected numbers of insects in each type of trap.

Large Aperture Optoelectronic Devices to Record and Time-Stamp Insects’ Wingbeats

Recording and analysis of wildlife sounds with regard to monitoring biodiversity are a developing trend in ecology. Automatic audio-based units are commonly used to record field vocalizations of birds, bats, cetaceans, and amphibians. The wingbeat of insects produces audible but feeble tones. Practical automatic recording units for the wingbeat of insects are still pending. In this paper, we present a complete system to record the wingbeat of insects based on the large aperture optical sensors that turn the light fluctuations (caused by the partial occlusion of light from the wings) into sound. Wide apertures are useful when tracking the movement of fast flying insects and the full motion of the beating wing in the case of tethered insects. The system detects a wingbeat event, auto-triggers the recording process, time-stamps the event, and stores the permanent record in situ. When the sensor is inserted in an insectary, it effortlessly produces massive datasets of wingbeat recordings. We discuss the implications for novel studies that are impractical to carry out manually, as they involve large numbers of insects. We also suggest potential applications such as smart insect traps that count, recognize, and alert for the presence of insects of economic and public health importance.

Power line network automation over IP

Internet Protocol (IP) suite and Ethernet physical layer are the current trend in home and industrial systems communication protocols. Unfortunately, in the field of power line automation protocols that are in wide use in home automation applications, the lack of IP convergence leads to difficulties, because of the variety and high costs of systems and solutions that need to be integrated. This paper presents a simple and reliable convergence mechanism of open power line Home Automation Protocols to IP (HAPoIP), which is implemented and tested for X10 protocol, on a low cost platform. The novelty and the achievement of the proposed system is the integration of heterogeneous automation networks through IP. In addition, the system was built in terms of software and hardware based on a new protocol.

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

We present a novel bimodal optoelectronic sensor based on Fresnel lenses and the associated stereo-recording device that records the wingbeat event of an insect in flight as backscattered and extinction light. We investigate the complementary information of these two sources of biometric evidence and we finally embed part of this technology in an electronic e-trap for fruit flies. The e-trap examines the spectral content of the wingbeat of the insect flying in and reports wirelessly counts and species identity. We design our devices so that they are optimized in terms of detection accuracy and power consumption, but above all, we ensure that they are affordable. Our aim is to make more widespread the use of electronic insect traps that report in virtually real time the level of the pest population from the field straight to a human controlled agency. We have the vision to establish remote automated monitoring for all insects of economic and hygienic importance at large spatial scales, using their wingbeat as biometric evidence. To this end, we provide open access to the implementation details, recordings, and classification code we developed.

Real time visualization of dynamic magnetic fields with a nanomagnetic ferrolens

Abstract Due to advancements in nanomagnetism and latest nanomagnetic materials and devices, a new potential field has been opened up for research and applications which was not possible before. We herein propose a new research field and application for nanomagnetism for the visualization of dynamic magnetic fields in real-time. In short, Nano Magnetic Vision. A new methodology, technique and apparatus were invented and prototyped in order to demonstrate and test this new application. As an application example the visualization of the dynamic magnetic field on a transmitting antenna was chosen. Never seen before high-resolution, photos and real-time color video revealing the actual dynamic magnetic field inside a transmitting radio antenna rod has been captured for the first time. The antenna rod is fed with six hundred volts, orthogonal pulses. This unipolar signal is in the very low frequency (i.e. VLF) range. The signal combined with an extremely short electrical length of the rod, ensures the generation of a relatively strong fluctuating magnetic field, analogue to the signal transmitted, along and inside the antenna. This field is induced into a ferrolens and becomes visible in real-time within the normal human eyes frequency spectrum. The name we have given to the new observation apparatus is, SPIONs Superparamagnetic Ferrolens Microscope (SSFM), a powerful passive scientific observation tool with many other potential applications in the near future.