Remote Monitoring of European Grapevine Moth, Lobesia botrana (Lepidoptera: Tortricidae) Population Using Camera-Based Pheromone Traps in Vineyards

Ünlü, Levent; Akdemïr, Bayram; Öğür, Ekrem; Sahin, Inci

doi:10.24925/turjaf.v7i4.652-657.2382

Cited by 16 publications

(11 citation statements)

References 21 publications

(36 reference statements)

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“…Trapview traps, iSCOUT traps, SightTrap traps, Z‐Trap 1, and DTN Smart Trap traps 17–20 . Similarly, a number of smart trap prototypes have been investigated for monitoring tortricid orchard pests, including codling moth, oriental fruit moth, Grapholita molesta (Busck), and European grapevine moth, Lobesia botrana (Denis and Schiffermüller) (Lepidoptera: Tortricidae) 21–23 . However, these experimental studies have only used traps baited with sex pheromone lures and captures of nontargets in these traps has not been reported to be a serious issue.…”

Section: Introductionmentioning

confidence: 99%

Remote monitoring of Cydia pomonella adults among an assemblage of nontargets in sex pheromone‐kairomone‐baited smart traps

Preti

Favaro

Knight³

et al. 2021

Pest Management Science

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BACKGROUND Captures of codling moth, Cydia pomonella (L.), in traps are used to establish action thresholds and time insecticide sprays. The need for frequent trap inspections in often remote orchards has created a niche for remote sensing smart traps. A smart trap baited with a five‐component pheromone‐kairomone blend was evaluated for codling moth monitoring among an assemblage of other nontargets in apple and pear orchards. RESULTS Codling moth captures did not differ between the smart trap and a standard trap when both were checked manually. However, the correlation between automatic and manual counts of codling moth in the smart traps was low, R2 = 0.66 ÷ 0.87. False‐negative identifications by the smart trap were infrequent <5%, but false‐positive identifications accounted for up to 67% of the count. These errors were primarily due to the misidentification of three moth species of fairly similar‐size to codling moth: apple clearwing moth Synanthedon myopaeformis (Borkhausen), oriental fruit moth Grapholita molesta (Busck), and carnation tortrix Cacoecimorpha pronubana (Hübner). Other false‐positive counts were less frequent and included the misidentifications of dipterans, other arthropods, patches of moth scales, and the double counting of some moths. CONCLUSION Codling moth was successfully monitored remotely with a smart trap baited with a nonselective sex pheromone‐kairomone lure, but automatic counts were inflated in some orchards due to mischaracterizations of primarily similar‐sized nontarget moths. Improved image‐identification algorithms are needed for smart traps baited with less‐selective lures and with lure sets targeting multiple species.

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Section: Introductionmentioning

confidence: 99%

Remote monitoring of Cydia pomonella adults among an assemblage of nontargets in sex pheromone‐kairomone‐baited smart traps

Preti

Favaro

Knight³

et al. 2021

Pest Management Science

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“…In fact, the major limitation of taking repeated shots is power consumption, which is mainly related to data transmission. Therefore, the automatic traps developed to date are typically set to take a daily picture (Guarnieri et al 2011;Ünlü et al 2019). However, multiple shots, including up to three photos per day, have been reported for experimental prototypes (Rassati et al 2016), such as those recently available for Trapview® (EFOS d.o.o, Hruševje, Slovenia) commercial automatic traps (Trapview 2020).…”

Section: Picture Acquisitionmentioning

confidence: 99%

“…In some of the case studies reported here (Guarnieri et al 2011;Rassati et al 2016;Shaked et al 2018;Ünlü et al 2019), the captured insects were checked manually by remote, and the images were observed by the human eye via computer or smartphone. This approach requires a trained observer in the control station to properly identify the insect species, and despite actually avoiding the field visit, the process is time-consuming.…”

Section: Insect Pest Identification and Countmentioning

confidence: 99%

Insect pest monitoring with camera-equipped traps: strengths and limitations

2020

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Integrated pest management relies on insect pest monitoring to support the decision of counteracting a given level of infestation and to select the adequate control method. The classic monitoring approach of insect pests is based on placing in single infested areas a series of traps that are checked by human operators on a temporal basis. This strategy requires high labor cost and provides poor spatial and temporal resolution achievable by single operators. The adoption of image sensors to monitor insect pests can result in several practical advantages. The purpose of this review is to summarize the progress made on automatic traps with a particular focus on camera-equipped traps. The use of software and image recognition algorithms can support automatic trap usage to identify and/or count insect species from pictures. Considering the high image resolution achievable and the opportunity to exploit data transfer systems through wireless technology, it is possible to have remote control of insect captures, limiting field visits. The availability of real-time and on-line pest monitoring systems from a distant location opens the opportunity for measuring insect population dynamics constantly and simultaneously in a large number of traps with a limited human labor requirement. The actual limitations are the high cost, the low power autonomy and the low picture quality of some prototypes together with the need for further improvements in fully automated pest detection. Limits and benefits resulting from several case studies are examined with a perspective for the future development of technology-driven insect pest monitoring and management.

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“…The use of automated image capture technology to monitor insect pest populations in applied research experiments [5][6][7] and commercial settings [8][9][10][11][12] has proven effective for timely pest management in various cultivated crop systems, including vineyards. Many of these image capture technologies facilitate automated image acquisition of traps that are set at a fixed distance and within optimal focus.…”

Section: Related Workmentioning

confidence: 99%

“…Some of these automated insect trap setups ensure that the acquired images are free from shadows or specular reflections to enable remote insect identification and automate pest enumeration [7,13]. However, some automated trap setups still rely on static systems that require proprietary traps or enclosures, where the sticky trap must be assembled [5,6].…”

Section: Related Workmentioning

confidence: 99%

AI-Powered Mobile Image Acquisition of Vineyard Insect Traps with Automatic Quality and Adequacy Assessment

Faria

Nogueira²,

Ferreira

et al. 2021

Agronomy

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The increasing alarming impacts of climate change are already apparent in viticulture, with unexpected pest outbreaks as one of the most concerning consequences. The monitoring of pests is currently done by deploying chromotropic and delta traps, which attracts insects present in the production environment, and then allows human operators to identify and count them. While the monitoring of these traps is still mostly done through visual inspection by the winegrowers, smartphone image acquisition of those traps is starting to play a key role in assessing the pests’ evolution, as well as enabling the remote monitoring by taxonomy specialists in better assessing the onset outbreaks. This paper presents a new methodology that embeds artificial intelligence into mobile devices to establish the use of hand-held image capture of insect traps for pest detection deployed in vineyards. Our methodology combines different computer vision approaches that improve several aspects of image capture quality and adequacy, namely: (i) image focus validation; (ii) shadows and reflections validation; (iii) trap type detection; (iv) trap segmentation; and (v) perspective correction. A total of 516 images were collected, divided into three different datasets and manually annotated, in order to support the development and validation of the different functionalities. By following this approach, we achieved an accuracy of 84% for focus detection, an accuracy of 80% and 96% for shadows/reflections detection (for delta and chromotropic traps, respectively), as well as mean Jaccard index of 97% for the trap’s segmentation.

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Remote Monitoring of European Grapevine Moth, Lobesia botrana (Lepidoptera: Tortricidae) Population Using Camera-Based Pheromone Traps in Vineyards

Cited by 16 publications

References 21 publications

Remote monitoring of Cydia pomonella adults among an assemblage of nontargets in sex pheromone‐kairomone‐baited smart traps

Remote monitoring of Cydia pomonella adults among an assemblage of nontargets in sex pheromone‐kairomone‐baited smart traps

Insect pest monitoring with camera-equipped traps: strengths and limitations

AI-Powered Mobile Image Acquisition of Vineyard Insect Traps with Automatic Quality and Adequacy Assessment

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