A multi-target trapping and tracking algorithm for Bactrocera Dorsalis based on cost model

Xiao, Di; Yang, Qiumei; Junqian, Fu; Deng, Xiaohui; Feng, Jiali; Ye, Yaowen; Lu, Yongyue

doi:10.1016/j.compag.2016.03.001

Cited by 11 publications

(7 citation statements)

References 9 publications

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“…A recent McPhail trap for FF based on images and other advanced sensors technologies has also been, recently, developed in Australia (rapid‐aim.com/home/), and a McPhail trap based on sensors detecting differences in light transmission resulting from entering insects into the trap was also developed for B. oleae (Potamitis, Rigakis, & Tatlas, ). An additional e‐trap for B. dorsalis employs a species‐specific attractant and an infrared interruption sensor to count attracted male flies passing through the electronic funnel (Deqin et al., ; Jiang et al., ).…”

Section: Introductionmentioning

confidence: 99%

Electronic traps for detection and population monitoring of adult fruit flies (Diptera: Tephritidae)

Shaked

Amore²,

Ioannou

et al. 2017

J Applied Entomology

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During the last decades, the economic importance of tephritid fruit flies (FF) has increased worldwide because of recurrent invasions and expansions into new areas, and reduced control capabilities of current control systems. Efficient monitoring systems, thus, are required to provide fast information to act promptly. With this aim in mind, we developed two electronic trap (e‐trap) versions for adult FF: one with specific volatiles for male and female adult Ceratitis capitata, and the second, based on the attraction of adult FF to yellow colour, targeting Dacus ciliatus, Rhagoletis cerasi and Bactrocera oleae. In the case of B. oleae, the female pheromone and ammonium bicarbonate were added as synergists. In the two versions, attracted FF were retained in the trap on glued surfaces. Real‐time images of the surfaces were automatically taken and transmitted to a server. We tested the two e‐trap versions in insect‐proof cages, where flies were released and recaptured, and in commercial orchards throughout the Mediterranean: C. capitata in peach orchards in Italy; R. cerasi in cherry orchards in Greece; B. oleae in olive orchards in Spain and in Greece; and D. ciliatus in melons in plastic tunnels in Israel. The e‐trap showed excellent abilities to transmit real‐time images of trapped FF and a high specificity for trapping different FF species. The ability of the entomologist to correctly classify FF from images in the office was >88%. In addition, average number of flies/trap in e‐trap grids did not differ from numbers reported on grids of conventional traps that were operating simultaneously. The e‐traps developed and tested in this study provide the basis for the real‐time monitoring of FF were no olfactory attractants are available, and for the surveillance of alien FF incursions where generic, but not specific, olfactory attractants exists.

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Section: Introductionmentioning

confidence: 99%

Electronic traps for detection and population monitoring of adult fruit flies (Diptera: Tephritidae)

Shaked

Amore²,

Ioannou

et al. 2017

J Applied Entomology

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“…The oriental leafworm moth Spodoptera litura (Fabricius) (Lepidoptera: Noctuidae) was effectively monitored by an ecological monitoring system combining GSM transmission technologies with mechatronics with accuracy ranging from 71% to 100% [38]. Average accuracies of 78.1% [25], 96.3% [39], and 94.9% [40] were demonstrated by automatic monitoring systems counting the catches of the oriental fruit fly B. dorsalis. Other automated systems with image analysis technology also proved to be reliable in detecting mainly whiteflies and moths, with accuracies ranging from 70% to 100% [41][42][43][44][45].…”

Section: Resultsmentioning

confidence: 99%

Automated Remote Insect Surveillance at a Global Scale and the Internet of Things

2017

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Abstract:The concept of remote insect surveillance at large spatial scales for many serious insect pests of agricultural and medical importance has been introduced in a series of our papers. We augment typical, low-cost plastic traps for many insect pests with the necessary optoelectronic sensors to guard the entrance of the trap to detect, time-stamp, GPS tag, and-in relevant cases-identify the species of the incoming insect from their wingbeat. For every important crop pest, there are monitoring protocols to be followed to decide when to initiate a treatment procedure before a serious infestation occurs. Monitoring protocols are mainly based on specifically designed insect traps. Traditional insect monitoring suffers in that the scope of such monitoring: is curtailed by its cost, requires intensive labor, is time consuming, and an expert is often needed for sufficient accuracy which can sometimes raise safety issues for humans. These disadvantages reduce the extent to which manual insect monitoring is applied and therefore its accuracy, which finally results in significant crop loss due to damage caused by pests. With the term 'surveillance' we intend to push the monitoring idea to unprecedented levels of information extraction regarding the presence, time-stamping detection events, species identification, and population density of targeted insect pests. Insect counts, as well as environmental parameters that correlate with insects' population development, are wirelessly transmitted to the central monitoring agency in real time and are visualized and streamed to statistical methods to assist enforcement of security control to insect pests. In this work, we emphasize how the traps can be self-organized in networks that collectively report data at local, regional, country, continental, and global scales using the emerging technology of the Internet of Things (IoT). This research is necessarily interdisciplinary and falls at the intersection of entomology, optoelectronic engineering, data-science, and crop science and encompasses the design and implementation of low-cost, low-power technology to help reduce the extent of quantitative and qualitative crop losses by many of the most significant agricultural pests. We argue that smart traps communicating through IoT to report in real-time the level of the pest population from the field straight to a human controlled agency can, in the very near future, have a profound impact on the decision-making process in crop protection and will be disruptive of existing manual practices. In the present study, three cases are investigated: monitoring Rhynchophorus ferrugineus (Olivier) (Coleoptera: Curculionidae) using (a) Picusan and (b) Lindgren trap; and (c) monitoring various stored grain beetle pests using the stored-grain pitfall trap. Our approach is very accurate, reaching 98-99% accuracy on automatic counts compared with real detected numbers of insects in each type of trap.

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“…moth Spodoptera litura (Fabricius) (Lepidoptera: Noctuidae) was effectively monitored by an ecological monitoring system combining GSM transmission technologies with mechatronics with accuracy ranging from 71 to 100 [38]. Average accuracies of 78.1% [25], 96.3% [39] and 94.9 % [40] were demonstrated by automatic monitoring systems counting the catches of the oriental fruit fly B. dorsalis. Other automated systems with image analysis technology also proved to be reliable in detecting mainly whiteflies and moths, with accuracies ranging from 70 to 100% [41][42][43][44][45].…”

Section: Resultsmentioning

confidence: 99%

Automated Remote Insect surveillance at a Global Scale and the Internet of Things

Potamitis¹,

Eliopoulos²,

Rigakis³

2017

Preprint

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17Τhe concept of remote insect surveillance at large spatial scales for a number of serious insect 18 pests of agricultural and medical importance is introduced in a series of our papers. We consuming, an expert is often needed for sufficient accuracy and can sometimes raise safety 27 issues for humans. These disadvantages reduce the extent to which manual insect monitoring 28 is applied and therefore its accuracy, which finally results in significant crop loss due to damage 29 caused by pests. With the term 'surveillance' we intend to push the monitoring idea to and qualitative crop losses by many the most significant agricultural pests. 42We argue that smart traps communicating through IoT to report in real-time the level of the 43 pest population from the field straight to a human controlled agency can, in the very near future, 44have a profound impact on the decision making process in crop protection and will be 45 disruptive of existing manual practices. In the present study, three cases are investigated :

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A multi-target trapping and tracking algorithm for Bactrocera Dorsalis based on cost model

Cited by 11 publications

References 9 publications

Electronic traps for detection and population monitoring of adult fruit flies (Diptera: Tephritidae)

Electronic traps for detection and population monitoring of adult fruit flies (Diptera: Tephritidae)

Automated Remote Insect Surveillance at a Global Scale and the Internet of Things

Automated Remote Insect surveillance at a Global Scale and the Internet of Things

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