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Thimar: An AI-driven solution for hydroponic farms

  • Ajman University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Hydroponic farming faces challenges in nutrient management and early disease detection, leading to significant crop losses. This paper introduces Thimar, an AI-driven system that integrates real-Time sensor data and deep learning-based image analysis to optimize hydroponic crop monitoring. While relative studies focus on plant diagnosis or nutrition management in isolation, Thimar concurrently evaluates both factors, offering actionable insights for farmers. By leveraging IoT-enabled sensors and convolutional neural networks (CNNs), our approach advances precision agriculture, contributing to sustainable and data-driven farming practices.

Original languageEnglish
Title of host publicationProceedings of the 16th Student Research Conference on Applied Computing
Subtitle of host publicationAI Innovations for a Better Tomorrow, SRC 2025
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798331578718
DOIs
StatePublished - 2025
Event16th Student Research Conference on Applied Computing, SRC 2025 - Dubai, United Arab Emirates
Duration: 24 Sep 202525 Sep 2025

Publication series

NameProceedings of the 16th Student Research Conference on Applied Computing: AI Innovations for a Better Tomorrow, SRC 2025

Conference

Conference16th Student Research Conference on Applied Computing, SRC 2025
Country/TerritoryUnited Arab Emirates
CityDubai
Period24/09/2525/09/25

Keywords

  • Deep learning
  • Hydroponic farming
  • IoT-enabled sensors
  • Plant disease detection
  • Precision agriculture

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