IoT Based Agriculture Monitoring and Prediction of Paddy Growth using Enhanced Conquer Based Transitive Clustering

Authors

  • C. Muruganandam Research Scholar, Department of Computer Science, A.V.V.M Sri Pushpam College (Autonomous), Poondi, Thanjavur (Dt) Affiliated to Bharathidasan University, Tiruchirappalli, Tamil Nadu, India
  • V. Maniraj Research Advisor and Coordinator, Department of Computer Science, A.V.V.M Sri Pushpam College (Autonomous), Poondi, Thanjavur (Dt) Affiliated to Barathidasan University, Tiruchirappalli, Tamil Nadu, India

Keywords:

Agriculture Monitoring, IoT, Enhanced Conquer, Paddy Growth Prediction, Transitive Clustering

Abstract

Agriculture is widely recognized as a fundamental pillar of our civilization, and it is currently undergoing a significant transition with the emergence of the IoT. This research investigates the field of IoT-based agriculture monitoring, with a specific emphasis on forecasting paddy growth. The introduction establishes the context by emphasizing the pivotal significance of agriculture and the promise of the IoT in enhancing farming methodologies. The problem statement highlights the necessity for a more advanced and precise system to monitor and forecast the growth of paddy, by identifying a gap in current research. Conventional approaches frequently prove inadequate in delivering timely and comprehensive insights, so neglecting to fully exploit the capabilities of IoT technology. The Enhanced Conquer based Transitive Clustering methodology combines conquer-based methodologies with transitive clustering, providing a resilient framework for the study and prediction of data. By harnessing the capabilities of IoT devices, real-time data pertaining to many parameters, including soil moisture, temperature, and humidity, is gathered. The study findings demonstrate the effectiveness of the Enhanced Conquer based Transitive Clustering algorithm in properly forecasting paddy growth stages. The system possesses the capability to not only monitor the prevailing agricultural circumstances but also forecast forthcoming developments, thereby empowering farmers to make well-informed decisions. The model accuracy and effectiveness highlight its potential for extensive implementation in contemporary agricultural practices.

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Published

23.02.2024

How to Cite

Muruganandam, C. ., & Maniraj, V. . (2024). IoT Based Agriculture Monitoring and Prediction of Paddy Growth using Enhanced Conquer Based Transitive Clustering. International Journal of Intelligent Systems and Applications in Engineering, 12(17s), 283–293. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4874

Issue

Vol. 12 No. 17s (2024)

Section

Research Article

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