Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018 |
| Seiten | 381-385 |
| Seitenumfang | 5 |
| ISBN (elektronisch) | 9781538667866 |
| Publikationsstatus | Veröffentlicht - 2 Juli 2018 |
| Veranstaltung | 2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018 - Bangkok, Thailand Dauer: 16 Dez. 2018 → 19 Dez. 2018 |
Publikationsreihe
| Name | IEEE International Conference on Industrial Engineering and Engineering Management |
|---|---|
| Band | 2019-December |
| ISSN (Print) | 2157-3611 |
| ISSN (elektronisch) | 2157-362X |
Abstract
Pest population prognosis helps the growers in the greenhouse to keep the pest population below the threshold efficiently. INSIM is one of the recognized pest population simulators. However, the implementation of the INSIM simulation faces some difficulties to be executed as a web service. Thus, we propose a Java-based web application using the mathematical model used in INSIM. Additionally to the known model, our implementation is able to give prognosis boundaries based on uncertainty of the temperature development and pest count. The proposed JIS gives lower and upper estimation of the pest population with the mean accuracy of 66.67% against our experimental validation data. Furthermore, the relationship between the area coverage for each yellow sticky trap and its accuracy percentage is investigated.
ASJC Scopus Sachgebiete
- Betriebswirtschaft, Management und Rechnungswesen (insg.)
- Betriebswirtschaft, Management und Rechnungswesen (sonstige)
- Ingenieurwesen (insg.)
- Wirtschaftsingenieurwesen und Fertigungstechnik
- Ingenieurwesen (insg.)
- Sicherheit, Risiko, Zuverlässigkeit und Qualität
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2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018. 2018. S. 381-385 8607724 (IEEE International Conference on Industrial Engineering and Engineering Management; Band 2019-December).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - JIS
T2 - 2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018
AU - Yeow, Kin Woon
AU - Becker, Matthias
N1 - Funding information: The project is supported (was supported) by funds of the Federal Ministry of Food and Agriculture (BMEL) based on a decision of the Parliament of the Federal Republic of Germany via the Federal Office for Agriculture and Food (BLE) under the innovation support programme.
PY - 2018/7/2
Y1 - 2018/7/2
N2 - Pest population prognosis helps the growers in the greenhouse to keep the pest population below the threshold efficiently. INSIM is one of the recognized pest population simulators. However, the implementation of the INSIM simulation faces some difficulties to be executed as a web service. Thus, we propose a Java-based web application using the mathematical model used in INSIM. Additionally to the known model, our implementation is able to give prognosis boundaries based on uncertainty of the temperature development and pest count. The proposed JIS gives lower and upper estimation of the pest population with the mean accuracy of 66.67% against our experimental validation data. Furthermore, the relationship between the area coverage for each yellow sticky trap and its accuracy percentage is investigated.
AB - Pest population prognosis helps the growers in the greenhouse to keep the pest population below the threshold efficiently. INSIM is one of the recognized pest population simulators. However, the implementation of the INSIM simulation faces some difficulties to be executed as a web service. Thus, we propose a Java-based web application using the mathematical model used in INSIM. Additionally to the known model, our implementation is able to give prognosis boundaries based on uncertainty of the temperature development and pest count. The proposed JIS gives lower and upper estimation of the pest population with the mean accuracy of 66.67% against our experimental validation data. Furthermore, the relationship between the area coverage for each yellow sticky trap and its accuracy percentage is investigated.
KW - Decision Support System
KW - Model Evaluation
KW - Population Prognosis
KW - System Analysis
UR - http://www.scopus.com/inward/record.url?scp=85061842261&partnerID=8YFLogxK
U2 - 10.1109/IEEM.2018.8607724
DO - 10.1109/IEEM.2018.8607724
M3 - Conference contribution
AN - SCOPUS:85061842261
SN - 978-1-5386-6787-3
T3 - IEEE International Conference on Industrial Engineering and Engineering Management
SP - 381
EP - 385
BT - 2018 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2018
Y2 - 16 December 2018 through 19 December 2018
ER -