[email protected] +91 9840521421 Scholarly Open Access, Peer-reviewed, Refereed Journal e-ISSN 2582-8746 Since 2020
  1. Home
  2. Archives
  3. 2025
  4. Vol. 6 Issue 6
  5. Article
Original Article

Agri Sense: ARIMA-Based Smart Irrigation with Pesticide Detection

  • Paper ID 1012
  • Volume 6, Issue 6
  • Pages 89-93
  • Published Online: November-December 2025
  • 3 Views
Open Access Peer Reviewed Crossref DOI

Authors

  • T Auntin Jose1
  • Mukunda T2
  • Sujan P3
  • M Disha4
  • N Bhavana5

1 Professor, Department. of CSE, Raja Rajeswari College of Engineering, Bengaluru, Karnataka, India. 2 3 4 5 Department of CSE, Raja Rajeswari College of Engineering, Bengaluru, Karnataka, India.

Article Information

Published Online
November-December 2025
Pages
89-93
Article Type
Original Article
Check for updates
check update

Abstract

View PDF

Agriculture continues to be the backbone of many economies, yet traditional irrigation and pesticide management practices often rely on manual observation, resulting in either over-irrigation, water scarcity, or excessive pesticide use. With advancements in the Internet of Things (IoT) and predictive analytics, smart agriculture solutions have gained popularity due to their ability to automate decision- making and improve resource utilization. In this study, we propose AgriSense, an IoT-enabled smart irrigation system that combines ARIMA-based time series forecasting with real-time pesticide detection. ARIMA models are well-suited for soil moisture prediction due to their ability to capture temporal dependencies. Pesticide detection ensures safe food production and prevents harmful effects of chemical residues. Integrating these features enables farmers to monitor field conditions, automate irrigation, and ensure crop safety using a unified system

APC from ₹1200 + 18% GST

Peer-reviewed open access₹1200 Indian (₹1400 with DOI)$80 USD internationalpaid after acceptance Peer-reviewed open access₹1200 Indian$80 USD international ₹1200 Indian · $80 intl

  • CrossRef DOI
  • ISSN 2582-8746
  • Open Access
  • Peer Reviewed
  • Plagiarism Checked
  • Google Scholar
Submit Paper