A GSM-based Field Monitoring System for Spodoptera litura (Fabricius)

Shieh, Jiann-Cherng; Wang, Jiing-Yi; Lin, Ta‐Te; Lin, Chi‐Hung; Yang, En‐Cheng; Tsai, Yu-Shiuan; Tsai, Hsien-Tsung; Chiou, Ming-Tzu; Lu, Faming; Jiang, Joe-Air

doi:10.1016/s1881-8366(11)80016-9

Cited by 19 publications

(10 citation statements)

References 7 publications

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“…; Shieh et al . ). To reduce power consumption required for image analysis, Selby, Gage & Whalon () combined a CCD camera with an IR sensor to study a weevil (plum curculio, Conotrachelus nenuphar ).…”

Section: Discussionmentioning

confidence: 97%

“…Efforts to detect insects in the field have been successful for arthropods larger than a centimetre mostly on pests (Arbogast et al 2000;Jiang et al 2008;Shieh et al 2011). To reduce power consumption required for image analysis, Selby, Gage & Whalon (2014) combined a CCD camera with an IR sensor to study a weevil (plum curculio, Conotrachelus nenuphar).…”

Section: Discussionmentioning

confidence: 99%

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EDAPHOLOG monitoring system: automatic, real‐time detection of soil microarthropods

et al. 2016

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Summary Soil microarthropods as organic matter decomposers play an important role in soil functioning thus providing ecosystem services. However, ecosystem scale investigations on their abundance and dynamics are scarce because their high spatio‐temporal heterogeneity requires huge sample size. Processing and identifying large number of individuals are extremely labour‐intensive. We prototyped a device called EDAPHOLOG monitoring system that consists of (1) a probe that catches and detects microarthropods and estimates their body size; (2) a data logger transmitting data to a central data base; and (3) a Java application for retrieving data. We tested the device in three ways. First, we tested the precision and accuracy of detection and body size estimation of the device in the laboratory using microarthropods of five morphotypes: euedaphic Collembola, haired Collembola, scaled Collembola, Acari and Oribatida. Secondly, we compared the number of individuals collected by EDAPHOLOG traps, pitfall traps and soil extraction method in an alfalfa field. Thirdly, we deployed a total of 100 EDAPHOLOG probes in nine different habitats for over 3 months to demonstrate the applicability of the monitoring system. In the laboratory, EDAPHOLOG detected 95·6% of individuals; even the smallest morphotype (Oribatida, body size (mean ± SE): 0·58 ± 0·04 mm) was detected in 87·5% of cases. For body length estimation, we established a quadratic relationship between the estimated and measured body lengths; however, the R2 of the quadratic model was only 0·32. By comparing the three different sampling methods (EDAPHOLOG, pitfall traps and soil extraction), we concluded that EDAPHOLOG traps better select for soil microarthropods compared to classical pitfall traps, since the latter ones caught also many other arthropod species. Furthermore, the EDAPHOLOG traps caught more epigeic microarthropods and almost the same number of soil‐dwelling euedaphic microarthropods as the numbers collected by soil extraction. During the 3‐month‐long field test, the total numbers of detected and captured individuals agreed very well, although the device tended to overestimate the number probably due to counting also some soil particles falling into the probe. This trend was the same regardless of the total number caught. Surface‐dwelling epigeic and litter‐dwelling hemiedaphic microarthropods dominated the samples although soil‐dwelling euedaphic microarthropods were also caught. EDAPHOLOG is a novel device that consumes little power, rugged enough to operate in the field for extended periods of time, and can be remotely controlled. It detects surface‐ and soil‐dwelling microarthropods real‐time, and with high accuracy; however, it is less accurate to estimate body size. The system is especially suitable in field research focusing on temporal activity of microarthropods. Because it is non‐invasive, studies requiring long‐term monitoring, such as soil remediation or ecosystem restoration projects, will also find EDAPHOLOG useful.

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Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

EDAPHOLOG monitoring system: automatic, real‐time detection of soil microarthropods

et al. 2016

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“…Only a few remote pest monitoring systems, based on wireless communication technology, have been evaluated in the past, with varying accuracy. 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.…”

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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“…The accuracy of our system in detecting adult beetle catches is also shown by the very high (r > 0.99 in all cases) correlation between the generated signals and actual numbers of insects caught in the trap. 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.…”

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 GSM-based Field Monitoring System for Spodoptera litura (Fabricius)

Cited by 19 publications

References 7 publications

EDAPHOLOG monitoring system: automatic, real‐time detection of soil microarthropods

EDAPHOLOG monitoring system: automatic, real‐time detection of soil microarthropods

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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