Proceedings of the International Conference on Sustainable Economics and Finance in the Digital Business Transformation (INCOSEF 2025)

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Optimizing Irrigation with AI: A Sensor-Driven Machine Learning Framework for Sustainable Agriculture

Authors
Arafat Islam1, Abdul Quddus Mozumder2, Anseena Anees Sabeena3, *, Anupom Debnath4, Syeda Kamari Noor3, Barna Biswas5, 6, Mohammad Shahidullah4, Farhana Akter7
1College of Graduate and Professional Studies, Trine University, Angola, IN, 46703, USA
2Department of Information System management, Stanton University, Anaheim, CA, 92802, USA
3College of Business, Westcliff University, Irvine, CA, 92614, USA
4Department of Business Administration, International American University, Los Angeles, CA, 90010, USA
5College of Technology and Engineering, Westcliff University, Irvine, CA, 92614, USA
6Koppelman School of Business, Brooklyn College, Brooklyn, NY, 11210, USA
7St. Francis Graduate School of Advanced Studies, St. Francis College, Brooklyn, NY, 11201, USA
*Corresponding author. Email: anseenaaneessabeena@gmail.com
Corresponding Author
Anseena Anees Sabeena
Available Online 6 April 2026.
DOI
10.2991/978-94-6239-624-1_10How to use a DOI?
Keywords
Smart Irrigation; Machine Learning; Artificial Intelligence (AI); Sensor Data; AdaBoost; Water Efficiency; IoT; Weather Forecast Integration
Abstract

An efficient use of water is crucial in farming sustainability, in areas that have limited water. Fixed schedules or manual judgment, which is the basis of the traditional irrigation systems, usually lead to an over-or under-irrigation that influences the yields and waste resources. This paper suggests a smart irrigation system that can be performed through a machine learning system with the help of sensor data to make ideal irrigation choices. Six monitored algorithms, logistic regression, random forest, gradient boosting, AdaBoost, support vector machine, K-nearest neighbors machine learning related 2000 samples (irrigation_machine.csv) measured over three agricultural plots with 20 sensor characteristics and irrigation answers of either 0 or 1. Normalization, one-hot encodings and train-test split 70-30 were employed in preprocessing. The accuracy, precision, recall, and F1-score were used to measure performance. The best results were achieved by AdaBoost where 99.45 was recorded as the accuracy, 99.45 was the precision, 99.47 was the recall, and 99.52 was the F1-score. The ability to get feature relevance and class balance insight by using correlation heatmaps was observed. This could be done in addition to making accuracy and efficiency more enhanced although there was no weather data. The suggested AI-based system effectively provides a high level of predictive performance, which minimizes the amount of unused water and promotes efficient precision irrigation at any scale and in any agricultural environment.

Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International Conference on Sustainable Economics and Finance in the Digital Business Transformation (INCOSEF 2025)
Series
Advances in Economics, Business and Management Research
Publication Date
6 April 2026
ISBN
978-94-6239-624-1
ISSN
2352-5428
DOI
10.2991/978-94-6239-624-1_10How to use a DOI?
Copyright
© 2026 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Arafat Islam
AU  - Abdul Quddus Mozumder
AU  - Anseena Anees Sabeena
AU  - Anupom Debnath
AU  - Syeda Kamari Noor
AU  - Barna Biswas
AU  - Mohammad Shahidullah
AU  - Farhana Akter
PY  - 2026
DA  - 2026/04/06
TI  - Optimizing Irrigation with AI: A Sensor-Driven Machine Learning Framework for Sustainable Agriculture
BT  - Proceedings of the International Conference on Sustainable Economics and Finance in the Digital Business Transformation (INCOSEF 2025)
PB  - Atlantis Press
SP  - 129
EP  - 141
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-624-1_10
DO  - 10.2991/978-94-6239-624-1_10
ID  - Islam2026
ER  -