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INTERNATIONAL JOURNAL OF RECENT TRENDS IN TECHNOLOGY AND ENGINEERING (IJRTTE)

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Predictive Irrigation Scheduling in Precision Agriculture Using Remote Sensing and Deep Neural Networks

Purushotham Endla
Department of Physics, School of Sciences and Humanities, SR University, India.
L. Kalaiselvi
Assistant Professor, Department of Electronics and Communication Engineering, Surya Engineering College, India.
P. Mathiyalagan
Professor, Department of Mechanical Engineering, J.J. College of Engineering and Technology, India.

Keywords: Predictive irrigation, precision agriculture, remote sensing, deep neural networks, water-use efficiency.

Abstract

A deep learning agrotechnical advising framework which can be used in precision agriculture is described in this paper, that combines satellite remote sensing information together with environmental and crop specific factors. Overcoming drawbacks of previous models such as poor generizability, the need for clear-sky images and the lack of real-time adaptabilty this work uses deep neural networks to provide dynamic and more reliable irrigation advice. This frame strengthens water-use efficiency, reduces crop water stress, and promotes the sustainable management of resources. Experimental evidence in various agro climatic zones reveals that over traditional approach, the framework performs better, thereby suggesting its potential in scalable, automated and climate-resilient agriculture decisions.
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Published
2025-06-27
Pages
1-10
Issue
Vol. 4 No. 2 (2025): IJRTTE - 04 - 02
Section
Articles

References

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