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Smart Farming Revolution: A Cutting-Edge Review of Deep Learning and IoT Innovations in Agriculture

Author

Listed:
  • J. Siva Prashanth

    (Anurag University)

  • G. Bala Krishna

    (Anurag University)

  • A. V. Krishna Prasad

    (Maturi Venkata Subba Rao Engineering College)

  • P. Ravinder Rao

    (Anurag University)

Abstract

The increasing adoption of IoT-based Smart Farming (SF) has transformed agriculture, enhancing productivity and efficiency. However, challenges such as cybersecurity risks, rural connectivity issues, and device interoperability hinder its full potential. This review systematically examines 88 studies published since 2019, focusing on key aspects such as application domains, privacy and security, communication protocols, sensors, and devices. Evaluations using datasets like CropDeep, regional agricultural data, and precision agriculture datasets demonstrate the effectiveness of deep learning (DL) and machine learning (ML) approaches in crop recommendation, disease detection, and yield prediction. Efficient MobileNet (EffiMob-Net) achieves a 99.92% accuracy rate in disease identification, while hybrid optimization algorithms improve IoT node placement by 18%, enhancing energy efficiency and coverage. The comparative analysis highlights DL models outperforming ML, with CNN achieving 99.81% accuracy in plant disease detection. Additionally, AI-based solutions, including forest cover change detection and DL-driven yield prediction, show improvements of up to 22% in forecasting accuracy. Scientific mapping identifies gaps in IoT security, dataset quality, and standardization. To address existing research limitations, a 5G-based SF framework is proposed, aiming to improve connectivity, real-time data processing, and automation in SF. This review advocates for robust security measures, high-quality datasets, and AI-driven innovations to drive future advancements in precision agriculture.

Suggested Citation

  • J. Siva Prashanth & G. Bala Krishna & A. V. Krishna Prasad & P. Ravinder Rao, 2025. "Smart Farming Revolution: A Cutting-Edge Review of Deep Learning and IoT Innovations in Agriculture," SN Operations Research Forum, Springer, vol. 6(1), pages 1-39, March.
    • Handle: RePEc:spr:snopef:v:6:y:2025:i:1:d:10.1007_s43069-025-00434-z
    DOI: 10.1007/s43069-025-00434-z
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    References listed on IDEAS

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    1. Zewen Xie & Zhenyu Ke & Kuigeng Chen & Yinglin Wang & Yadong Tang & Wenlong Wang, 2024. "A Lightweight Deep Learning Semantic Segmentation Model for Optical-Image-Based Post-Harvest Fruit Ripeness Analysis of Sugar Apples ( Annona squamosa )," Agriculture, MDPI, vol. 14(4), pages 1-22, April.
    2. Muthumanickam Dhanaraju & Poongodi Chenniappan & Kumaraperumal Ramalingam & Sellaperumal Pazhanivelan & Ragunath Kaliaperumal, 2022. "Smart Farming: Internet of Things (IoT)-Based Sustainable Agriculture," Agriculture, MDPI, vol. 12(10), pages 1-26, October.
    3. Christine Musanase & Anthony Vodacek & Damien Hanyurwimfura & Alfred Uwitonze & Innocent Kabandana, 2023. "Data-Driven Analysis and Machine Learning-Based Crop and Fertilizer Recommendation System for Revolutionizing Farming Practices," Agriculture, MDPI, vol. 13(11), pages 1-23, November.
    4. Zahid Ullah & Najah Alsubaie & Mona Jamjoom & Samah H. Alajmani & Farrukh Saleem, 2023. "EffiMob-Net: A Deep Learning-Based Hybrid Model for Detection and Identification of Tomato Diseases Using Leaf Images," Agriculture, MDPI, vol. 13(3), pages 1-13, March.
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