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A Systematic Review of AI-Based Classifications Used in Agricultural Monitoring in the Context of Achieving the Sustainable Development Goals

Author

Listed:
  • Vasile Adrian Nan

    (Faculty of Geodesy, Technical University of Civil Engineering, 020396 Bucharest, Romania)

  • Gheorghe Badea

    (Faculty of Geodesy, Technical University of Civil Engineering, 020396 Bucharest, Romania)

  • Ana Cornelia Badea

    (Faculty of Geodesy, Technical University of Civil Engineering, 020396 Bucharest, Romania)

  • Anca Patricia Grădinaru

    (Faculty of Geodesy, Technical University of Civil Engineering, 020396 Bucharest, Romania)

Abstract

The integration of Artificial Intelligence (AI) into remote sensing data classification has revolutionized agriculture and environmental monitoring. AI is one of the main technologies used in smart farming that enhances and optimizes the sustainability of agricultural production. The use of AI in agriculture can involve land use mapping and crop detection, crop yield monitoring, flood-prone area detection, pest disease monitoring, droughts prediction, soil content analysis and soil production capacity detection, and for monitoring the evolution of forests and vegetation. This review examines recent advancements in AI-driven classification techniques for various applications regarding agriculture and environmental monitoring to answer the following research questions: (1) What are the main problems that can be solved through incorporating AI-driven classification techniques into the field of smart agriculture and environmental monitoring? (2) What are the main methods and strategies used in this technology? (3) What type of data can be used in this regard? For this study, a systematic literature review approach was adopted, analyzing publications from Scopus and WoS (Web of Science) between 1 January 2020 and 31 December 2024. By synthesizing recent developments, this review provides valuable insights for researchers, highlighting the current trends, challenges and future research directions, in the context of achieving the Sustainable Development Goals.

Suggested Citation

  • Vasile Adrian Nan & Gheorghe Badea & Ana Cornelia Badea & Anca Patricia Grădinaru, 2025. "A Systematic Review of AI-Based Classifications Used in Agricultural Monitoring in the Context of Achieving the Sustainable Development Goals," Sustainability, MDPI, vol. 17(19), pages 1-40, September.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:19:p:8526-:d:1756130
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    References listed on IDEAS

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    1. Liu, Quanshan & Wu, Zongjun & Cui, Ningbo & Zheng, Shunsheng & Zhu, Shidan & Jiang, Shouzheng & Wang, Zhihui & Gong, Daozhi & Wang, Yaosheng & Zhao, Lu, 2024. "Soil moisture content estimation of drip-irrigated citrus orchard based on UAV images and machine learning algorithm in Southwest China," Agricultural Water Management, Elsevier, vol. 303(C).
    2. Rehab Mahmoud & Mohamed Hassanin & Haytham Al Feel & Rasha M. Badry, 2023. "Machine Learning-Based Land Use and Land Cover Mapping Using Multi-Spectral Satellite Imagery: A Case Study in Egypt," Sustainability, MDPI, vol. 15(12), pages 1-21, June.
    3. Julia Rodrigues & Mauricio Araújo Dias & Rogério Negri & Sardar Muhammad Hussain & Wallace Casaca, 2024. "A Robust Dual-Mode Machine Learning Framework for Classifying Deforestation Patterns in Amazon Native Lands," Land, MDPI, vol. 13(9), pages 1-19, September.
    4. Parra-López, Carlos & Ben Abdallah, Saker & Garcia-Garcia, Guillermo & Hassoun, Abdo & Trollman, Hana & Jagtap, Sandeep & Gupta, Sumit & Aït-Kaddour, Abderrahmane & Makmuang, Sureerat & Carmona-Torres, 2025. "Digital technologies for water use and management in agriculture: Recent applications and future outlook," Agricultural Water Management, Elsevier, vol. 309(C).
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