New Technologies and Their Use in Entomology

Program Chair: Julien Saguez

In the last decades, new tools and technologies have been developed to improve our day-to-day but also the understanding, the monitoring and the management of insects at different level. For examples, automated traps, computer vision, acoustic monitoring or radars, often associated with artificial intelligence, have been created and are useful to provide forecasting models and to predict insect migrations. Artificial intelligence is also used to develop new apps for mobile devices that recognize insects on pictures. In other cases, unmanned aerial vehicles such as drones, are effective tools to monitor insect pests but are also integrated in IPM programs to release beneficial insects or biopesticides to manage insect pests and vectors in large areas or in regions difficult to access. At a molecular level, using omics approaches, new tools and technologies have also been developed to shortly detect and identify species (eg. at the international borders) and can be used to validate the phylogeny of the species at taxonomical level. Here are some examples on how technologies are used in entomology and how they provide unprecedented opportunities, but this list is not exhaustive and you are invited to present other research projects associated with this topic.

Plenary Speakers

Novel approaches using genomics for the detection, identification and surveillance of insects

Genomics has revolutionized biology and provided us with new tools with very useful practical applications. The development of DNA-based diagnostics and of whole or partial genome sequencing is changing the way we can conduct crop and forest health surveys to identify the presence of pests or invasive species. DNA-based identification is powerful because it can generate an identification from any insect body part from different life stages. DNA is even present in degraded samples and frass and can be used for identification that would be otherwise impossible. Insect and pathogen identification is now regularly conducted by using the Polymerase Chain Reaction (PCR) or DNA barcoding and is routinely used in diagnostic labs. Increasingly, high-throughput and whole or targeted sequencing of genomes is providing a new layer of data that can inform our analyses of sources, pathways of spread, and identify high risk traded materials or commodities. Being able to perform these analyses in situ promises to increase uptake by making DNA-testing available to practitioners to perform rapid and accurate identification from samples collected during surveys or inspections. These approaches hold great promises, yet, there are still barriers to implementation that include cost and computing capacity. 

Richard Hamelin

Ph. D., Professor and Head, Department of Forest and Conservation Sciences, Faculty of Forestry, The University of British Columbia 

Dr. Richard Hamelin obtained a B. Sc. from McGill University (1982), a Master’s of Pest Management from Simon Fraser University (1986) and a Ph. D. from the University of Kentucky (1990). He works on pressing forest health issues such as invasive pathogens and pests and climate change. He has developed tools for pathogen detection and novel genomic approaches for pathogen surveillance. He has trained 60 graduate students, post-doctoral fellows and research staff, published over 190 peer-reviewed scientific articles and delivered more than 300 presentations around the world. He was president of the Canadian Phytopathological Society and the Quebec Society for Plant Protection. He is a Fellow of the American Phytopathological Society, and received the International Union of Forest Research Organization Scientific Achievement Award, the Queen Elizabeth II Diamond Jubilee award, the Canadian Institute of Forestry Scientific Achievement Award and Merit Awards from Natural Resources Canada, the Canadian Forest service, the Canadian Food Inspection Agency, and the Quebec Society for Plant Protection. 

Trichogramma as a tool for IPM in Brazilian Agriculture
Brazil is advancing in the utilization of Biological Control in open fields. Bioinputs are currently employed across approximately 25 million hectares in Brazilian Agriculture to manage various pests. Around 170 private companies are producing natural enemies in our country, being registered 629 products related with bioinputs (macro and micro-organisms, semiochemicals and biochemicals). Among macro-organisms, the Trichogramma species, specifically Trichogramma galloi and T. pretiosum, are the most frequently released, while T. atopovirilia exhibits promising potential for control fruit pests. The species considered are reared in a factitious host, Ephestia kuehniella, and between the 170 private companies that are producing natural enemies in our country, some of them are producing 40 kilograms of eggs of the moth (with 1 gram roughly equating to 36,000 eggs) per day. In sugarcane areas, approximately 3 to 4 million hectares are treated to control the sugarcane borer, Diatraea saccharalis, using T. galloi released via drones. Another 400,000 hectares are treated with T. pretiosum to manage pests affecting soybeans, cotton, corn, or tomatoes (Tuta absoluta), as well as pests targeting fruit trees (citrus and avocado). Recently, it was reported a new Trichogramma species, T. foersteri Takahashi 2021, which has the potential to control some of the most important Lepidoptera pests, including the Spodoptera complex that we have in Brazil (comprising 6 species, attacking different crops). Trichogramma foersteri has the capacity to parasitize 3 to 5 times more effectively than other species and promotes pseudoparasitism (a type of non-reproduction mortality).

José Roberto Postali Parra

University of São Paulo, Brazil

José Roberto Postali Parra is graduated in Agronomic Engineer at São Paulo University (ESALQ/USP), a Master and PhD degree in Entomology by ESALQ, Post-Doctorate degree by University of Illinois (Urban Champaign). He was Director of ESALQ/USP (from 2003 to 2006) and is currently Senior Professor in the Department of Entomology and Acarology at ESALQ/USP. Professor Parra is member of the Brazilian Academy of Science and Academy of Sciences for the Developing World (TWAS) and member of the Brazilian Academy of Agronomic Sciences (ABCA). He has received the Commendation of the National Order of Scientific Merit and earned the honor of Grã-Cruz (Grand Cross) of the National Order of Scientific Merit. He was President of Ethics Committee from USP (2006). Member of CERT, representing the interior for 3 managements. Adjunct Coordinator of Agrarian Sciences from FAPESP (2007-2021). He has supervised 115 post-graduation students and 14 post-doctorates. He has published 372 scientific papers, written 25 books and 87 chapters of books.

From identification to forecasting: the potential of image recognition and artificial intelligence for aphid pest monitoring
In this presentation, we will give a brief overview of recent advances in the field of artificial intelligence (AI) that enable the development of automated solutions for image-based identification and classification of insects. The efficient data processing of these systems offers fast analysis of large data sets and enables rapid response in time-critical applications such as pest control. In addition, large-scale data evaluation can be performed in cases where there is a lack of appropriate human expertise for manual examination. We focus on aphids as a model organism to evaluate the potential of image recognition and artificial intelligence in agricultural pest monitoring by investigating current insect monitoring methods and species identification capabilities, and show how image recognition could support the identification process in large sample sizes from mass catches.
As a concrete application, we will discuss and demonstrate research on the automatic identification of aphids from mass catches using a research project carried out at our institutions. Here, the goal is to identify 30 different aphid species relevant to agriculture, some of which have a high morphological similarity. The performance evaluation shows that the system can identify aphids with an accuracy comparable to that of a human expert. Finally, we show how this system can be used in practice as an integrated software-hardware solution that includes the entire examination process from the acquisition of the sample tray to the visualisation of the identification results.

Dr. Torsten Will

Julius Kuehn Institute - Federal Research Centre for Cultivated Plants, Institute for Resistance Research and Stress Tolerance

Torsten Will studied biology at the Justus Liebig University in Giessen, where he also completed his doctorate at the Botanical Department in the field of plant cell biology on the topic of aphid-plant interaction. After a group leader position in the field of insect biotechnology, he moved to the Julius Kühn Institute in 2016, where he heads a research group at the institute for Resistence Research and Stress Tolerance that conducts research on plant resistance to plant viruses and insect pests.

Dr. Christoph Joachim
Christoph Joachim studied biology at the University of Hohenheim and completed his doctorate at the Technical University of Munich in the field of chemical ecology. He now works as a group leader in the Entomology Department at the Institute for Plant Protection in Field Crops and Grassland at the Julius Kühn Institute. Among other things, he deals with the determination of infestations and the assessment of treatment strategies using the example of aphids and sugar beet. The monitoring of insect pests is also an integral part of his applied research focus, which in the case of aphids involves an intensive examination of their population development, distribution and identification in the field.

Sebastian Thiel
Sebastian Thiel graduated with a degree in Technical computer science from Technical University of Berlin with distinction. He worked as research assistant at TU Berlin and University of Edinburgh in Artificial Intelligence and co-founded ALM, a startup company which develops and implements AI solutions for industrial as well as scientific applications.


Philipp Batz 
Philipp Batz graduated with a degree in Statistics from Humboldt University of Berlin. He worked as research assistant at Technical University of Berlin in Artificial Intelligence and co-founded ALM, a startup company which develops and implements AI solutions for industrial as well as scientific applications.

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