Deep learning for automated detection of <i>Drosophila suzukii</i>: potential for <scp>UAV</scp>‐based monitoring

Roosjen, P.P.J.; Kellenberger, Benjamin; Kooistra, Lammert; Green, David R.; Fahrentrapp, Johannes

doi:10.1002/ps.5845

Cited by 60 publications

(36 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Roosjen et al [ 21 ] present an automated monitoring system of fruit flies in crops for pest management. The system uses image-based object detection through deep learning to identify the spotted wing Drosophila ( Drosophila suzukii ).…”

Section: Introductionmentioning

confidence: 99%

An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning

Bjerge

Nielsen

Sepstrup

et al. 2021

Sensors

View full text Add to dashboard Cite

Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.

show abstract

Section: Introductionmentioning

confidence: 99%

An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning

Bjerge

Nielsen

Sepstrup

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…Several recent studies extended these machine learning approaches to deal with basic and applied issues in insect ecology (Høye et al, 2021). DNN-based software for insect identification in images has been developed in the applicative context of agricultural pest control, aiming to support farmers and extension workers in identifying insect pests (e.g., Cheng et al, 2017;Nieuwenhuizen et al, 2018;Zhong et al, 2018;Liu et al, 2019;Chudzik et al, 2020;Roosjen et al, 2020). Other researchers developed deep learning models to assist insect identification for biodiversity monitoring projects (Hansen et al, 2020 for beetles;Buschbacher et al, 2020 for bees).…”

Section: State Of the Art: Machine Learning For Insect Ecoinformaticsmentioning

confidence: 99%

“…However, identifying and counting the trapped specimens is labor-intensive, requires taxonomical expertise, and limits the scale of monitoring projects. A few recent projects combined insect trapping with machine learning methods to reduce the scouting workload required when monitoring crop pests (Nieuwenhuizen et al, 2018;Zhong et al, 2018;Liu et al, 2019;Chudzik et al, 2020;Roosjen et al, 2020). These approaches have not yet been extended to insect monitoring in non-agricultural settings, because they often require sophisticated high-cost sensors that are incompatible with large-scale ecological studies, and are designed to identify specific taxa.…”

Section: A Rate-limiting Step: Acquisition Of Entomological Big Datamentioning

confidence: 99%

High Throughput Data Acquisition and Deep Learning for Insect Ecoinformatics

et al. 2021

View full text Add to dashboard Cite

Ecology documents and interprets the abundance and distribution of organisms. Ecoinformatics addresses this challenge by analyzing databases of observational data. Ecoinformatics of insects has high scientific and applied importance, as insects are abundant, speciose, and involved in many ecosystem functions. They also crucially impact human well-being, and human activities dramatically affect insect demography and phenology. Hazards, such as pollinator declines, outbreaks of agricultural pests and the spread insect-borne diseases, raise an urgent need to develop ecoinformatics strategies for their study. Yet, insect databases are mostly focused on a small number of pest species, as data acquisition is labor-intensive and requires taxonomical expertise. Thus, despite decades of research, we have only a qualitative notion regarding fundamental questions of insect ecology, and only limited knowledge about the spatio-temporal distribution of insects. We describe a novel high throughput cost-effective approach for monitoring flying insects as an enabling step toward “big data” entomology. The proposed approach combines “high tech” deep learning with “low tech” sticky traps that sample flying insects in diverse locations. As a proof of concept we considered three recent insect invaders of Israel’s forest ecosystem: two hemipteran pests of eucalypts and a parasitoid wasp that attacks one of them. We developed software, based on deep learning, to identify the three species in images of sticky traps from Eucalyptus forests. These image processing tasks are quite difficult as the insects are small (<5 mm) and stick to the traps in random poses. The resulting deep learning model discriminated the three focal organisms from one another, as well as from other elements such as leaves and other insects, with high precision. We used the model to compare the abundances of these species among six sites, and validated the results by manually counting insects on the traps. Having demonstrated the power of the proposed approach, we started a more ambitious study that monitors these insects at larger spatial and temporal scales. We aim at building an ecoinformatics repository for trap images and generating data-driven models of the populations’ dynamics and morphological traits.

show abstract

“…Since the trap captures and retains the individuals, counting the individuals would be a relatively trivial task compared to tracking live moving insects. (Roosjen et al, 2020) presents an automated monitoring system of fruit flies in crops for pest management. The system uses image-based object detection through deep learning to identify the fruit fly Drosophilla suzukii, or spotted wing drosophila.…”

Section: Related Workmentioning

confidence: 99%

“…(Roosjen et al, 2020) presents an automated monitoring system of fruit flies in crops for pest management. The system uses image-based object detection through deep learning to identify the fruit fly Drosophilla suzukii, or spotted wing drosophila.…”

Section: Related Workmentioning

confidence: 99%

An automated light trap to monitor moths (Lepidoptera) using computer vision-based tracking and deep learning

Bjerge¹,

Nielsen²,

Sepstrup³

et al. 2020

Preprint

View full text Add to dashboard Cite

This paper presents a portable computer vision system, that is able to attract and detect live insects. More specifically, the paper proposes detection and classification of species by recording images of live moths captured by a light trap. A light trap with multiple illuminations and a camera was designed to attract and monitor live insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting, based on deep learning analysis of the captured images then tracked and counted the number of insects and identified moth species. This paper presents the design and the algorithm that were used to determine and identify the moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5,675 images per night. A customized convolutional neural network was trained on 864 labelled images of live moths, which were divided in to eight different species, achieving a high validation F 1 -score of 0.96. The algorithm measured an average classification and tracking F 1 -score of 0.83 and a tracking detection rate of 0.79. This result was based on an estimate of 83 individual moths observed during one night with insect activity in 122 minutes collecting 6,000 images. Overall, the proposed computer vision system and algorithm showed promising results in nondestructive and automatic monitoring of moths as well as classification of species. The system provides a cost-effective alternative to traditional methods, which require time-consuming manual identification and typically provides coarse temporal solution to capturing data. In addition, the system avoids depleting moth populations in the monitoring process, which is a problem in traditional traps that kill individual moths. As image libraries grow and become more complete, the images captured by the trapping system can be processed automatically and allow users with limited experience to collect data on insect abundance, biomass, and diversity. (Kim Bjerge) measures. For many taxa, long-term population census data are also non-existing and more extensive monitoring. Thus, more especially of species rich taxa, is critically needed.More than half of all described species on Earth are insects. With more than 160,000 described species, the insect order Lepidoptera, which consists of moths (95%) and butterflies (5%), is one of the largest. Moths are important as pollinators, herbivores and prey for e.g. birds and bats. Reports of declining moth populations have come from Great Britain Fox et al. (2013) Sweden Franzén andJohannesson (2007) and the Netherlands Groe-

show abstract

Deep learning for automated detection of Drosophila suzukii: potential for UAV‐based monitoring

Cited by 60 publications

References 33 publications

An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning

An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning

High Throughput Data Acquisition and Deep Learning for Insect Ecoinformatics

An automated light trap to monitor moths (Lepidoptera) using computer vision-based tracking and deep learning

Contact Info

Product

Resources

About