End-to-end smart agriculture sensor network & analytics platform
| dc.contributor.author | Senevirathne, I | |
| dc.contributor.author | Ambegoda, TD | |
| dc.contributor.editor | Gunawardena, S | |
| dc.date.accessioned | 2025-11-19T06:04:07Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | Agriculture is one of the main global-level industries which provides essential food supplies to the ever-increasing global population. Due to the rapidly growing demand for food, improving agricultural efficiency and productivity has become a critical goal as an industry. Traditional farming practices, reliant on intuition and manual labor, are increasingly inadequate to meet global food demands while preserving ecosystems. Smart agriculture transcends mere automation; it represents a holistic transformation of farming systems. It refers to the use of modern technology, data analytics, and innovative tools to enhance agricultural productivity, efficiency, and sustainability. The transition to modern farming methods with various sensors, actuators and analytics is a challenge to many ordinary farmers due to huge initial cost, lack of relevant knowledge and accessibility. This research focuses on proposing a cost-effective smart agriculture ecosystem including sensor network and analytics platform to improve the productivity and maximize the yield of selected crops. | |
| dc.identifier.conference | Applied Data Science & Artificial Intelligence (ADScAI) Symposium 2025 | |
| dc.identifier.department | Department of Computer Science & Engineering | |
| dc.identifier.doi | https://doi.org/10.31705/ADScAI.2025.55 | |
| dc.identifier.email | senevirathne.git21@uom.lk | |
| dc.identifier.email | thanuja@cse.mrt.ac.lk | |
| dc.identifier.faculty | Engineering | |
| dc.identifier.place | Moratuwa, Sri Lanka | |
| dc.identifier.proceeding | Proceedings of Applied Data Science & Artificial Intelligence Symposium 2025 | |
| dc.identifier.uri | https://dl.lib.uom.lk/handle/123/24395 | |
| dc.language.iso | en | |
| dc.publisher | Department of Computer Science and Engineering | |
| dc.subject | smart agriculture | |
| dc.subject | sensor analytics | |
| dc.subject | precision farming | |
| dc.subject | smart irrigation | |
| dc.subject | data-driven farming | |
| dc.title | End-to-end smart agriculture sensor network & analytics platform | |
| dc.type | Conference-Extended-Abstract |
