AI-IoT Enabled Smart Agriculture Using Sequential Hybrid Feature Selection for Intelligent Irrigation Management

Hussein Ali Abdulwahid Abdulwahid; Ghassan Zaid; Zainab Riyadh Salim Salim; Ethar Sulaiman Yaseen Yaseen

doi:10.35377/saucis...1946478

AI-IoT Enabled Smart Agriculture Using Sequential Hybrid Feature Selection for Intelligent Irrigation Management

Abstract

Efficient irrigation management is a prerequisite of sustainable agriculture, especially when considering water constraints and climate variability. Recent breakthroughs in Artificial Intelligence (AI) and Internet of Things (IoT) technology have made it possible to develop smart agricultural systems that monitor environmental conditions and automate irrigation decision-making. However, high-dimensional agricultural sensor data usually contain redundant and unnecessary features, which significantly impair machine learning model performance. This paper presents a smart agriculture system based on AI-IoT with an intelligent irrigation control using a Sequential Hybrid Feature Selection (SHFS) method. The SHFS model sequentially combines Principal Component Analysis (PCA), Particle Swarm Optimization (PSO) and L1-regularized Logistic Regression (Lasso) to minimize the dimensionality and preserve the discriminative information. The suggested framework was evaluated on 100,000 IoT-based agricultural observations consisting of ambient as well as soil-related factors. We evaluated the prediction capability of some classifiers, such as k-Nearest Neighbors (kNN), Na¨ıve Bayes, Logistic Regression and XGBoost. The experimental results show that the SHFS framework can achieve a significant improvement in classification performance compared to traditional feature selection methods. The best accuracy was 97.5%, attained by XGBoost in combination with SHFS, which also had higher precision, recall, and F1-score values than the other models. The collected findings demonstrate that the suggested framework efficiently reduces data redundancy, enhances the reliability of irrigation forecasts, and promotes sustainable applications of precision agriculture.

Keywords

Ethical Statement

It is declared that during the preparation process of this study, scientific and ethical principles were followed, and all the studies benefited from are stated in the bibliography.

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Details

Primary Language

English

Subjects

Artificial Intelligence (Other)

Journal Section

Research Article

Authors

Hussein Ali Abdulwahid Abdulwahid ^*
0009-0005-0571-9377
Iraq

Ghassan Zaid
0009-0004-2631-9527
Iraq

Zainab Riyadh Salim Salim
0009-0008-8390-8669
Iraq

Ethar Sulaiman Yaseen Yaseen
0000-0002-5259-378X
Iraq

Early Pub Date

October 27, 2026

Publication Date

October 30, 2026

Submission Date

May 7, 2026

Acceptance Date

September 7, 2026

Published in Issue

Year 2026 Volume: 9 Number: 5

DOI

https://doi.org/10.35377/saucis...1946478

IZ

https://izlik.org/JA35ST96UT

Cite

RIS / Bibtex

APA

Abdulwahid, H. A. A., Zaid, G., Salim, Z. R. S., & Yaseen, E. S. Y. (2026). AI-IoT Enabled Smart Agriculture Using Sequential Hybrid Feature Selection for Intelligent Irrigation Management. Sakarya University Journal of Computer and Information Sciences, 9(5), 1494-1503. https://doi.org/10.35377/saucis...1946478

AMA

1.Abdulwahid HAA, Zaid G, Salim ZRS, Yaseen ESY. AI-IoT Enabled Smart Agriculture Using Sequential Hybrid Feature Selection for Intelligent Irrigation Management. SAUCIS. 2026;9(5):1494-1503. doi:10.35377/saucis.1946478

Chicago

Abdulwahid, Hussein Ali Abdulwahid, Ghassan Zaid, Zainab Riyadh Salim Salim, and Ethar Sulaiman Yaseen Yaseen. 2026. “AI-IoT Enabled Smart Agriculture Using Sequential Hybrid Feature Selection for Intelligent Irrigation Management”. Sakarya University Journal of Computer and Information Sciences 9 (5): 1494-1503. https://doi.org/10.35377/saucis. 1946478.

EndNote

Abdulwahid HAA, Zaid G, Salim ZRS, Yaseen ESY (October 1, 2026) AI-IoT Enabled Smart Agriculture Using Sequential Hybrid Feature Selection for Intelligent Irrigation Management. Sakarya University Journal of Computer and Information Sciences 9 5 1494–1503.

IEEE

[1]H. A. A. Abdulwahid, G. Zaid, Z. R. S. Salim, and E. S. Y. Yaseen, “AI-IoT Enabled Smart Agriculture Using Sequential Hybrid Feature Selection for Intelligent Irrigation Management”, SAUCIS, vol. 9, no. 5, pp. 1494–1503, Oct. 2026, doi: 10.35377/saucis...1946478.

ISNAD

Abdulwahid, Hussein Ali Abdulwahid - Zaid, Ghassan - Salim, Zainab Riyadh Salim - Yaseen, Ethar Sulaiman Yaseen. “AI-IoT Enabled Smart Agriculture Using Sequential Hybrid Feature Selection for Intelligent Irrigation Management”. Sakarya University Journal of Computer and Information Sciences 9/5 (October 1, 2026): 1494-1503. https://doi.org/10.35377/saucis. 1946478.

JAMA

1.Abdulwahid HAA, Zaid G, Salim ZRS, Yaseen ESY. AI-IoT Enabled Smart Agriculture Using Sequential Hybrid Feature Selection for Intelligent Irrigation Management. SAUCIS. 2026;9:1494–1503.

MLA

Abdulwahid, Hussein Ali Abdulwahid, et al. “AI-IoT Enabled Smart Agriculture Using Sequential Hybrid Feature Selection for Intelligent Irrigation Management”. Sakarya University Journal of Computer and Information Sciences, vol. 9, no. 5, Oct. 2026, pp. 1494-03, doi:10.35377/saucis. 1946478.

Vancouver

1.Hussein Ali Abdulwahid Abdulwahid, Ghassan Zaid, Zainab Riyadh Salim Salim, Ethar Sulaiman Yaseen Yaseen. AI-IoT Enabled Smart Agriculture Using Sequential Hybrid Feature Selection for Intelligent Irrigation Management. SAUCIS. 2026 Oct. 1;9(5):1494-503. doi:10.35377/saucis. 1946478