Artificial neural networks in online semiautomated pest discriminability: an applied case with 2 Thrips species

Fedor, Peter; Peña‐Méndez, Eladia María; Kucharczyk, Halina; Vaňhara, Jaromír; Havel, Josef; Doričová, Martina; Prokop, Pavol

doi:10.3906/tar-1305-8

Cited by 13 publications

(7 citation statements)

References 40 publications

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“…Similarly, an ANN has been developed that can identify 101 European thrips species with 95% reliability [ 216 ]. A three-layer ANN using seventeen morphological and 15 quantitative morphometric variables can discriminate two similar thrips species, T. sambuci Heeger and T. fuscipennis Haliday, with 100% accuracy [ 217 ].…”

Section: Pcr-based Identification Of Thrips Using Molecular Markersmentioning

confidence: 99%

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Frontiers Approaches to the Diagnosis of Thrips (Thysanoptera): How Effective Are the Molecular and Electronic Detection Platforms?

Ghosh

Jangra

Dietzgen

et al. 2021

Insects

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Thrips are insect pests of economically important agricultural, horticultural, and forest crops. They cause damage by sucking plant sap and by transmitting several tospoviruses, ilarviruses, carmoviruses, sobemoviruses, and machlomoviruses. Accurate and timely identification is the key to successful management of thrips species. However, their small size, cryptic nature, presence of color and reproductive morphs, and intraspecies genetic variability make the identification of thrips species challenging. The use of molecular and electronic detection platforms has made thrips identification rapid, precise, sensitive, high throughput, and independent of developmental stages. Multi-locus phylogeny based on mitochondrial, nuclear, and other markers has resolved ambiguities in morphologically indistinguishable thrips species. Microsatellite, RFLP, RAPD, AFLP, and CAPS markers have helped to explain population structure, gene flow, and intraspecies heterogeneity. Recent techniques such as LAMP and RPA have been employed for sensitive and on-site identification of thrips. Artificial neural networks and high throughput diagnostics facilitate automated identification. This review also discusses the potential of pyrosequencing, microarrays, high throughput sequencing, and electronic sensors in delimiting thrips species.

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Section: Pcr-based Identification Of Thrips Using Molecular Markersmentioning

confidence: 99%

“…The implementation of ANN shows potential to discriminate thrips species with high accuracy [ 216 , 217 ]. SVM-based image processing can be used for high-throughput diagnosis of thrips species [ 223 ].…”

Section: Strengths Of the Present-day Molecular And Electronic Platforms And Future Potentialmentioning

confidence: 99%

Frontiers Approaches to the Diagnosis of Thrips (Thysanoptera): How Effective Are the Molecular and Electronic Detection Platforms?

Ghosh

Jangra

Dietzgen

et al. 2021

Insects

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“…Furthermore, according to P. Fedor et al, the artificial neural networks have proven effective in accurately distinguishing between two closely related species, T. sambuci and T. fuscipennis , despite their similar morphology. This demonstrates the approach’s reliability and speed in species identification, making it a valuable tool for online, semi-automated pest identification [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Detection and Classification of Scirtothrips dorsalis on Fruit Crops with Smartphone-Based Deep Learning System: Preliminary Results

Niyigena,

Lee,

Kwon

et al. 2023

Insects

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This study proposes a deep-learning-based system for detecting and classifying Scirtothrips dorsalis Hood, a highly invasive insect pest that causes significant economic losses to fruit crops worldwide. The system uses yellow sticky traps and a deep learning model to detect the presence of thrips in real time, allowing farmers to take prompt action to prevent the spread of the pest. To achieve this, several deep learning models are evaluated, including YOLOv5, Faster R-CNN, SSD MobileNetV2, and EfficientDet-D0. EfficientDet-D0 was integrated into the proposed smartphone application for mobility and usage in the absence of Internet coverage because of its smaller model size, fast inference time, and reasonable performance on the relevant dataset. This model was tested on two datasets, in which thrips and non-thrips insects were captured under different lighting conditions. The system installation took up 13.5 MB of the device’s internal memory and achieved an inference time of 76 ms with an accuracy of 93.3%. Additionally, this study investigated the impact of lighting conditions on the performance of the model, which led to the development of a transmittance lighting setup to improve the accuracy of the detection system. The proposed system is a cost-effective and efficient alternative to traditional detection methods and provides significant benefits to fruit farmers and the related ecosystem.

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“…Since the turn of the century, the need for a more complex approach to research on thrips has es-tablished a new generation of thysanopterologists dealing with all aspects of Thysanoptera diversity, including the faunistics (Fedor 2003(Fedor , 2004(Fedor , 2005, taxonomy (Fedor et al 2008(Fedor et al , 2009(Fedor et al , 2014, ecology (Pelikán et al 2002;Varga et al 2010;Fedor et al 2011;Zvaríková et al 2016;Masarovič et al 2017b) and the global change consequences (Varga 2007;Varga & Fedor 2008;Masarovič et al 2017a;Zvaríková et al 2017;Fedor et al 2018), publishing 41 first records (Figure 1 and 2).…”

mentioning

confidence: 99%

An updated checklist of thrips from Slovakia with emphasis on economic species

Zvaríková¹,

Masarovič²,

Prokop³

et al. 2020

Plant Prot. Sci.

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Almost sixty years after the first published plea for more systematic research on thrips in Slovakia, the checklist undisputedly requires an appropriate revision with a special emphasis on the economic consequences of climate change and biological commodity trade globalisation synergic effects, followed by the dynamic and significant changes in the native biodiversity due to alien species introduction. The updated checklist contains 189 species ecorded from the area of Slovakia, from three families: Aeolothripidae Uzel, 1895 (15 species), Thripidae Stephens, 1829 (113 species) and Phlaeothripidae Uzel, 1895 (61 species), including 7 beneficiary and 35 economic pest elements, such as one A2 EPPO quarantine pest (Frankliniella occidentalis) and five potential transmitters of tospoviruses (F. occidentalis, F. intonsa, F. fusca, Thrips tabaci, Dictyothrips betae). Several species (e.g., Hercinothrips femoralis, Microcephalothrips abdominalis, F. occidentalis, T. flavus, T. tabaci, Limothrips cerealium, L. denticornis, etc.) may possess a heavy introduction and invasion potential with well-developed mechanisms for successful dispersion.

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Artificial neural networks in online semiautomated pest discriminability: an applied case with 2 Thrips species

Cited by 13 publications

References 40 publications

Frontiers Approaches to the Diagnosis of Thrips (Thysanoptera): How Effective Are the Molecular and Electronic Detection Platforms?

Frontiers Approaches to the Diagnosis of Thrips (Thysanoptera): How Effective Are the Molecular and Electronic Detection Platforms?

Real-Time Detection and Classification of Scirtothrips dorsalis on Fruit Crops with Smartphone-Based Deep Learning System: Preliminary Results

An updated checklist of thrips from Slovakia with emphasis on economic species

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