Discrimination of Hover Fly Species and Sexes by Wing Interference Signals DOI Creative Commons
Meng Li, Anna Runemark,

Julio Hernandez

et al.

Advanced Science, Journal Year: 2023, Volume and Issue: 10(34)

Published: Oct. 17, 2023

Abstract Remote automated surveillance of insect abundance and diversity is poised to revolutionize decline studies. The study reveals spectral analysis thin‐film wing interference signals (WISs) can discriminate free‐flying insects beyond what be accomplished by machine vision. Detectable photonic sensors, WISs are robust indicators enabling species sex identification. first quantitative survey thickness modulation through shortwave‐infrared hyperspectral imaging 600 wings from 30 hover fly presented. Fringy reflectance WIS explained four optical parameters, including membrane thickness. Using a Naïve Bayes Classifier with five parameters that retrieved remotely, 91% achieved accuracy in identification sexes. WIS‐based therefore potent tool for remote surveillance.

Language: Английский

AInsectID Version 1.1: An Insect Species Identification Software Based on the Transfer Learning of Deep Convolutional Neural Networks DOI Creative Commons
Haleema Sadia, Parvez Alam

Published: March 25, 2025

AInsectID Version 1.1 is a Graphical User Interface (GUI)‐operable open‐source insect species identification, color processing, and image analysis software. The software has current database of 150 insects integrates artificial intelligence approaches to streamline the process with focus on addressing prediction challenges posed by mimics. This paper presents methods algorithmic development, coupled rigorous machine training used enable high levels validation accuracy. Our work transfer learning prominent convolutional neural network (CNN) architectures, including VGG16, GoogLeNet, InceptionV3, MobileNetV2, ResNet50, ResNet101. Here, we employ both fine tuning hyperparameter optimization improve performance. After extensive computational experimentation, ResNet101 evidenced as being most effective CNN model, achieving accuracy 99.65%. dataset utilized for sourced from National Museum Scotland, Natural History London, open source datasets Zenodo (CERN's Data Center), ensuring diverse comprehensive collection species.

Language: Английский

Citations

0
Tiny deep learning model for insect segmentation and counting on resource-constrained devices DOI Creative Commons
Amin Kargar, Dimitrios Zorbas, Michael Gaffney

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 236, P. 110378 - 110378

Published: April 18, 2025

Language: Английский

Citations

0
Towards edge processing of images from insect camera traps DOI Creative Commons
Kim Bjerge, Henrik Karstoft, Toke T. Høye

et al.

Remote Sensing in Ecology and Conservation, Journal Year: 2025, Volume and Issue: unknown

Published: April 17, 2025

Abstract Insects represent nearly half of all known multicellular species, but knowledge about them lags behind for most vertebrate species. In part this reason, they are often neglected in biodiversity conservation policies and practice. Computer vision tools, such as insect camera traps, automated monitoring have the potential to revolutionize study conservation. To further advance trapping analysis their image data, effective processing pipelines needed. paper, we present a flexible fast pipeline designed analyse these recordings by detecting, tracking classifying nocturnal insects broad taxonomy 15 classes resolution individual moth A classifier with anomaly detection is proposed filter dark, blurred or partially visible that will be uncertain classify correctly. simple track‐by‐detection algorithm track classified incorporating feature embeddings, distance area cost. We evaluated computational speed power performance different edge computing devices (Raspberry Pi's NVIDIA Jetson Nano) compared various time‐lapse (TL) strategies tracking. The minimum difference detections was found 2‐min TL intervals 0.5 frames per second; however, fewer than one night, Pearson correlation decreases. Shifting from would reduce number recorded images allow real‐time on trap Raspberry Pi. Nano energy‐efficient solution, capable at fps. Our applied more 5.7 million second 12 light traps during two full seasons located diverse habitats, including bogs, heaths forests. results thus show scalability traps.

Language: Английский

Citations

0
A transfer learning approach to classify insect diversity based on explainable AI DOI Creative Commons
Md. Mahmudul Hasan,

SM Shaqib,

Sharmin Akter

et al.

Discover Life, Journal Year: 2025, Volume and Issue: 55(1)

Published: April 17, 2025

Language: Английский

Citations

0
Discrimination of Hover Fly Species and Sexes by Wing Interference Signals DOI Creative Commons
Meng Li, Anna Runemark,

Julio Hernandez

et al.

Advanced Science, Journal Year: 2023, Volume and Issue: 10(34)

Published: Oct. 17, 2023

Abstract Remote automated surveillance of insect abundance and diversity is poised to revolutionize decline studies. The study reveals spectral analysis thin‐film wing interference signals (WISs) can discriminate free‐flying insects beyond what be accomplished by machine vision. Detectable photonic sensors, WISs are robust indicators enabling species sex identification. first quantitative survey thickness modulation through shortwave‐infrared hyperspectral imaging 600 wings from 30 hover fly presented. Fringy reflectance WIS explained four optical parameters, including membrane thickness. Using a Naïve Bayes Classifier with five parameters that retrieved remotely, 91% achieved accuracy in identification sexes. WIS‐based therefore potent tool for remote surveillance.

Language: Английский

Citations

9