Wing Interferential Patterns (WIPs) and machine learning, a step toward automatized tsetse (Glossina spp.) identification

Abstract

A simple method for accurately identifying Glossina spp in the field is a challenge to sustain the future elimination of Human African Trypanosomiasis (HAT) as a public health scourge, as well as for the sustainable management of African Animal Trypanosomiasis (AAT). Current methods for Glossina species identification heavily rely on a few well-trained experts. Methodologies that rely on molecular methodologies like DNA barcoding or mass spectrometry protein profiling (MALDI TOFF) haven’t been thoroughly investigated for Glossina sp. Nevertheless, because they are destructive, costly, time-consuming, and expensive in infrastructure and materials, they might not be well adapted for the survey of arthropod vectors involved in the transmission of pathogens responsible for Neglected Tropical Diseases, like HAT. This study demonstrates a new type of methodology to classify Glossina species. In conjunction with a deep learning architecture, a database of Wing Interference Patterns (WIPs) representative of the Glossina species involved in the transmission of HAT and AAT was used. This database has 1766 pictures representing 23 Glossina species. This cost-effective methodology, which requires mounting wings on slides and using a commercially available microscope, demonstrates that WIPs are an excellent medium to automatically recognize Glossina species with very high accuracy.

Wing Interferential Patterns (WIPs) and machine learning for the classification of some Aedes species of medical interest

“…To this aim, we have explored the accuracy and reliability of Wing Interferential Patterns (WIPs) generated on the surface of insect wings to accurately identify Aedes specimens and classify them using a deep learning (DL) procedure. We have already validated the robustness in the classi cation accuracy of this methodology on insects belonging to the Glossinidae Theobald, 1903 family 7 .…”

“…The procedures applied to capture WIPs were the same as those described for Glossina sp. WIPs acquisition 7 . Wings were dissected and deposited on a glass slide, adding a small cover slide.…”

“…All pictures were enlarged to a maximal occupancy, making the size of the wing not a discriminating parameter for insect species identi cation. The numerical parameters settled are the same as previously described and detailed in the publication on Glossina spp 7 .…”

“…The whole data set is available at gshare: https:// gshare.com/s/597f4e2f2d8206e09603. The methodology used was initially developed and tested on Tetse ies 7 . Brie y, the original CNN architecture MobileNet 9 , ResNet 10 , and YOLOv2 11 architecture were deemed for automatically classifying Aedes sp.…”

WingSegment: A Computer Vision‐Based Hybrid Approach for Insect Wing Image Segmentation and 3D Printing

“…Beyond entomology, scientists can harness WingSegment for feature extraction in machine learning and deep learning applications, enabling the development of automated identification, modeling, and analysis methods for insect wings. [57][58][59][60] Notably, WingSegment's versatility extends beyond insect wings, as it can extract features from 2D images of other cellular structures such as leaves, skins, and foam-like formations [61] like that illustrated in Figure 3. Additionally, as depicted in Figure 3b,c, it can contribute to the investigation of soil and the development of clay cracks.…”