IOAT in Agricultural Research: Continuous Monitoring and Analysis of Demographic Data to Assess Cotton Crop Potential in Paddy Fields

Authors

  • S. Sandeepkumar Research Scholar, Department of Information Technology, Annamalai University, Chidambaram, Tamilnadu, India
  • K. Jagan Mohan Associate Professor, Department of Information Technology, Annamalai University, Chidambaram, Tamilnadu, India
  • K. Amarendra Professor, Department of Computer Science & Engineering, Koneru Lakshmaiah Educational Foundation, Guntur, Andhra Pradesh, India

Keywords:

Internet of Agriculture Things, Prediction, Cotton Diseases, Data Visualization, Predictive algorithms

Abstract

The escalating global demand for agricultural and horticultural products, driven by rapid population growth and exacerbated greenhouse effects, underscores the urgent need for technological advancements in these sectors. Notably, the horticulture and agriculture fields grapple with many diseases, particularly pronounced in vital crops like cotton, often called "white gold." Despite its widespread utility, cotton is susceptible to significant losses once afflicted by diseases. This research paper addresses this challenge by predicting conditions based on soil mineral deficiencies. We introduce an innovative system under the Internet of Agriculture Things (IoAT). This system continuously monitors essential soil parameters, including pH, humidity, Temperature, Nitrogen, phosphorus, and potassium levels in cotton paddy fields. The acquired data is processed and stored in a cloud database. Advanced data prediction techniques are then employed to forecast potential cotton diseases. Furthermore, data visualization techniques provide a comprehensive assessment, equipping farmers with insights to optimize soil conditions and enhance cotton yield. Through this integrated approach, the research offers a proactive solution to mitigate disease-related losses in cotton crops, emphasizing the pivotal role of technology in modern agriculture.

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References

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Published

30.11.2023

How to Cite

Sandeepkumar, S. ., Mohan, K. J. ., & Amarendra, K. . (2023). IOAT in Agricultural Research: Continuous Monitoring and Analysis of Demographic Data to Assess Cotton Crop Potential in Paddy Fields. International Journal of Intelligent Systems and Applications in Engineering, 12(6s), 130–138. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/3965

Issue

Vol. 12 No. 6s (2024)

Section

Research Article

License

Copyright (c) 2023 S. Sandeepkumar, K. Jagan Mohan, K. Amarendra

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