Artificial intelligence-powered IoT-Based irrigation system for precision farming
Agriculture irrigation is an essential agricultural practice that ensures crops' healthy growth. Nonetheless, irrigation is labor-intensive and difficult to manage, particularly in large-scale farming operations. Smart irrigation systems are promising solution to the agricultural sector's...
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2024
|
| Online Access: | http://psasir.upm.edu.my/id/eprint/118457/ http://psasir.upm.edu.my/id/eprint/118457/1/118457.pdf |
| _version_ | 1848867520628391936 |
|---|---|
| author | Leao, Hen-Rin Lee, Chu-Liang Soon, Gregory How Thien Lee, It-Ee Chung, Gwo-Chin Pang, Wai-Leong Ng, Zi-Neng Tan, Kar-Ban Chan, Kah-Yoong |
| author_facet | Leao, Hen-Rin Lee, Chu-Liang Soon, Gregory How Thien Lee, It-Ee Chung, Gwo-Chin Pang, Wai-Leong Ng, Zi-Neng Tan, Kar-Ban Chan, Kah-Yoong |
| author_sort | Leao, Hen-Rin |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Agriculture irrigation is an essential agricultural practice that ensures crops' healthy growth. Nonetheless, irrigation is labor-intensive and difficult to manage, particularly in large-scale farming operations. Smart irrigation systems are promising solution to the agricultural sector's challenges. These systems use sensors and other Internet of Things (IoT) technologies to monitor and control irrigation without much human intervention. Hence, several benefits are achievable, including lower labor costs, improved water efficiency, and increased crop yields. This study proposed a smart irrigation system using sensors to detect soil moisture levels and irrigate crops based on the detected moisture level. The proposed system also utilized a cloud-based platform to collect and store sensor data, which monitored the irrigation process and identified potential farming concerns. Besides, the system optimized the irrigation schedule to retain the soil moisture level from 80 to 90%. Therefore, the proposed smart irrigation system is a cost-effective and scalable solution that can improve irrigation efficiency in the agricultural sector. |
| first_indexed | 2025-11-15T14:37:48Z |
| format | Article |
| id | upm-118457 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:37:48Z |
| publishDate | 2024 |
| publisher | Elsevier |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1184572025-07-11T00:22:25Z http://psasir.upm.edu.my/id/eprint/118457/ Artificial intelligence-powered IoT-Based irrigation system for precision farming Leao, Hen-Rin Lee, Chu-Liang Soon, Gregory How Thien Lee, It-Ee Chung, Gwo-Chin Pang, Wai-Leong Ng, Zi-Neng Tan, Kar-Ban Chan, Kah-Yoong Agriculture irrigation is an essential agricultural practice that ensures crops' healthy growth. Nonetheless, irrigation is labor-intensive and difficult to manage, particularly in large-scale farming operations. Smart irrigation systems are promising solution to the agricultural sector's challenges. These systems use sensors and other Internet of Things (IoT) technologies to monitor and control irrigation without much human intervention. Hence, several benefits are achievable, including lower labor costs, improved water efficiency, and increased crop yields. This study proposed a smart irrigation system using sensors to detect soil moisture levels and irrigate crops based on the detected moisture level. The proposed system also utilized a cloud-based platform to collect and store sensor data, which monitored the irrigation process and identified potential farming concerns. Besides, the system optimized the irrigation schedule to retain the soil moisture level from 80 to 90%. Therefore, the proposed smart irrigation system is a cost-effective and scalable solution that can improve irrigation efficiency in the agricultural sector. Elsevier 2024 Article PeerReviewed text en cc_by_sa_4 http://psasir.upm.edu.my/id/eprint/118457/1/118457.pdf Leao, Hen-Rin and Lee, Chu-Liang and Soon, Gregory How Thien and Lee, It-Ee and Chung, Gwo-Chin and Pang, Wai-Leong and Ng, Zi-Neng and Tan, Kar-Ban and Chan, Kah-Yoong (2024) Artificial intelligence-powered IoT-Based irrigation system for precision farming. International Journal of Intelligent Systems and Applications in Engineering, 12 (19s). pp. 329-335. ISSN 2147-6799 https://www.ijisae.org/index.php/IJISAE/article/view/5070 |
| spellingShingle | Leao, Hen-Rin Lee, Chu-Liang Soon, Gregory How Thien Lee, It-Ee Chung, Gwo-Chin Pang, Wai-Leong Ng, Zi-Neng Tan, Kar-Ban Chan, Kah-Yoong Artificial intelligence-powered IoT-Based irrigation system for precision farming |
| title | Artificial intelligence-powered IoT-Based irrigation system for precision farming |
| title_full | Artificial intelligence-powered IoT-Based irrigation system for precision farming |
| title_fullStr | Artificial intelligence-powered IoT-Based irrigation system for precision farming |
| title_full_unstemmed | Artificial intelligence-powered IoT-Based irrigation system for precision farming |
| title_short | Artificial intelligence-powered IoT-Based irrigation system for precision farming |
| title_sort | artificial intelligence-powered iot-based irrigation system for precision farming |
| url | http://psasir.upm.edu.my/id/eprint/118457/ http://psasir.upm.edu.my/id/eprint/118457/ http://psasir.upm.edu.my/id/eprint/118457/1/118457.pdf |