10.57647/ijamad.2025.1504.20

Transformation of Agriculture through Machine Learning, Deep Learning, and the Internet of Things: Insights from Iran – Challenges and Opportunities

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  • Gholamreza Farrokhi*1,
  • Mahboobeh Gapeleh,
  1. Faculty member
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Published in Issue 31-12-2025

Copyright (c) 2026 Gholamreza Farrokhi, Mahboobeh Gapeleh (Author)

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This work is licensed under a Creative Commons Attribution 4.0 International License.

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Farrokhi, G., & Gapeleh, M. (2025). Transformation of Agriculture through Machine Learning, Deep Learning, and the Internet of Things: Insights from Iran – Challenges and Opportunities. International Journal of Agricultural Management and Development, 15(4). https://doi.org/10.57647/ijamad.2025.1504.20
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Abstract

Agriculture is a fundamental pillar of livelihood, food security, and employment in Iran, especially in rural areas. Challenges such as population growth, water scarcity, climate change, and weak resource management impact this sector. Recent advances in Machine Learning (ML), Deep Learning (DL), and the Internet of Things (IoT) offer promising opportunities to enhance productivity, sustainability, and smart management in Iranian agriculture. This paper examines the integration of ML, DL, and IoT in agriculture, enabling data-driven decisions on crop management, soil, irrigation, and weather forecasting. IoT systems support real-time monitoring, intelligent control, and resource loss reduction. Despite challenges like poor data quality, limited communication infrastructure, and insufficient farmer training, targeted policies, government support, and investment can help Iran achieve smart agriculture, improve food security, and promote sustainable development. By reviewing the current status, challenges, and future prospects, this study highlights the transformative potential of ML, DL, and IoT in shaping Iran’s agricultural future.

Keywords

  • Smart Agriculture, Machine Learning, Deep Learning, Internet of Things (IoT), Water and Soil Resource Management
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