Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Intelligent Decision Technologies |
| Untertitel | Proceedings of the 16th KES-IDT 2024 Conference |
| Herausgeber/-innen | Ireneusz Czarnowski, Robert J. Howlett, Lakhmi C. Jain, Lakhmi C. Jain |
| Herausgeber (Verlag) | Springer Science and Business Media Deutschland GmbH |
| Seiten | 75-85 |
| Seitenumfang | 11 |
| ISBN (elektronisch) | 978-981-97-7419-7 |
| ISBN (Print) | 9789819774180 |
| Publikationsstatus | Veröffentlicht - 7 Feb. 2025 |
| Veranstaltung | 16th International KES Conference on Intelligent Decision Technologies, KES-IDT 2024 - Madeira, Portugal Dauer: 19 Juni 2024 → 21 Juni 2024 |
Publikationsreihe
| Name | Smart Innovation, Systems and Technologies |
|---|---|
| Band | 411 SIST |
| ISSN (Print) | 2190-3018 |
| ISSN (elektronisch) | 2190-3026 |
Abstract
In the field of controlled horticulture, various methods have been studied to facilitate the environmental data retrieval. One of the great findings in this research is the attraction of insect’s behaviour towards the LED lighting of various wavelengths. Previous research shows promising results using LED based insect traps for insect population estimation in greenhouses. Therefore, an automated monitoring system is proposed as a standardization tool for environmental data gathering and estimation of pest population in controlled horticulture settings. The proposed automated monitoring system integrates object recognition models (combination of YOLOv3 and SVM) that identify and classify the pest and beneficial population density. The proposed system provides informative output via a mobile application. As a result, the proposed system functions as an integrated IoT management tool that simplifies the information retrieval process.
ASJC Scopus Sachgebiete
- Entscheidungswissenschaften (insg.)
- Allgemeine Entscheidungswissenschaften
- Informatik (insg.)
- Allgemeine Computerwissenschaft
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- BibTex
- RIS
Intelligent Decision Technologies : Proceedings of the 16th KES-IDT 2024 Conference. Hrsg. / Ireneusz Czarnowski; Robert J. Howlett; Lakhmi C. Jain; Lakhmi C. Jain. Springer Science and Business Media Deutschland GmbH, 2025. S. 75-85 (Smart Innovation, Systems and Technologies; Band 411 SIST).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - An Automated Monitoring System for Controlled Greenhouse Horticulture
AU - Becker, Matthias
AU - Yeow, Kinwoon
N1 - Publisher Copyright: © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025/2/7
Y1 - 2025/2/7
N2 - In the field of controlled horticulture, various methods have been studied to facilitate the environmental data retrieval. One of the great findings in this research is the attraction of insect’s behaviour towards the LED lighting of various wavelengths. Previous research shows promising results using LED based insect traps for insect population estimation in greenhouses. Therefore, an automated monitoring system is proposed as a standardization tool for environmental data gathering and estimation of pest population in controlled horticulture settings. The proposed automated monitoring system integrates object recognition models (combination of YOLOv3 and SVM) that identify and classify the pest and beneficial population density. The proposed system provides informative output via a mobile application. As a result, the proposed system functions as an integrated IoT management tool that simplifies the information retrieval process.
AB - In the field of controlled horticulture, various methods have been studied to facilitate the environmental data retrieval. One of the great findings in this research is the attraction of insect’s behaviour towards the LED lighting of various wavelengths. Previous research shows promising results using LED based insect traps for insect population estimation in greenhouses. Therefore, an automated monitoring system is proposed as a standardization tool for environmental data gathering and estimation of pest population in controlled horticulture settings. The proposed automated monitoring system integrates object recognition models (combination of YOLOv3 and SVM) that identify and classify the pest and beneficial population density. The proposed system provides informative output via a mobile application. As a result, the proposed system functions as an integrated IoT management tool that simplifies the information retrieval process.
KW - Decision support system
KW - Entomological analysis
KW - Model evaluation
KW - Object recognition
KW - Stochastic system
UR - http://www.scopus.com/inward/record.url?scp=85219198805&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-7419-7_7
DO - 10.1007/978-981-97-7419-7_7
M3 - Conference contribution
AN - SCOPUS:85219198805
SN - 9789819774180
T3 - Smart Innovation, Systems and Technologies
SP - 75
EP - 85
BT - Intelligent Decision Technologies
A2 - Czarnowski, Ireneusz
A2 - Howlett, Robert J.
A2 - C. Jain, Lakhmi
A2 - C. Jain, Lakhmi
PB - Springer Science and Business Media Deutschland GmbH
T2 - 16th International KES Conference on Intelligent Decision Technologies, KES-IDT 2024
Y2 - 19 June 2024 through 21 June 2024
ER -