Automatic Irrigation and Crop Monitoring using Machine Learning and Internet of Things

Kaur, Inderpreet and Patil, Snehal Pradeep and Shankar, Bhukya and Beenu, G H Kerinab and Kumar, G. Suresh and Reddy, D. V. Lokeswar and Ramachandran, G. (2024) Automatic Irrigation and Crop Monitoring using Machine Learning and Internet of Things. In: UNSPECIFIED.

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Abstract

The Crop environmental monitoring system with intelligence multidimensional and multi-angle crop environment monitoring system is created to satisfy the demands of contemporary society for agricultural crop environmental monitoring. The system gathers, processes, and forwards data to the network module by connecting the appropriate data gathering sensors. Ultimately, the data is sent to the information processing center for accurate processing and analysis before being visually displayed. Ultimately, the system's ability to achieve the desired result, have a high application value, and effectively increase agricultural environment surveillance efficiency is fully demonstrated by the testing and verification of its operation. Low-cost intelligent modules for smart irrigation systems powered by IoT and AI In India, agriculture is the primary source of income for the majority of people. It is essential to use cutting edge technologies to safeguard priceless water supplies. In addition to serving as Industry 4.0's cornerstone, IoT improves smart agriculture. The goal of the research is to develop a cutting-edge, affordable system for intelligent irrigation. Self-talk and inter-device networking are made possible by IoT. © 2025 Elsevier B.V., All rights reserved.

Item Type: Conference or Workshop Item (Paper)
Subjects: Agricultural and Biological Sciences > Agricultural Sciences
Divisions: Engineering and Technology > Vinayaka Mission's Kirupananda Variyar Engineering College, Salem > Electronics & Communication Engineering
Depositing User: Unnamed user with email techsupport@mosys.org
Last Modified: 27 Nov 2025 06:43
URI: https://vmuir.mosys.org/id/eprint/1735

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