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A Survey on Deep Learning and Its Impact on Agriculture: Challenges and Opportunities

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  • Marwan Albahar

    (College of Computer Science in Al-Leith, Umm Al Qura University, Mekkah 21955, Saudi Arabia)

Abstract

The objective of this study was to provide a comprehensive overview of the recent advancements in the use of deep learning (DL) in the agricultural sector. The author conducted a review of studies published between 2016 and 2022 to highlight the various applications of DL in agriculture, which include counting fruits, managing water, crop management, soil management, weed detection, seed classification, yield prediction, disease detection, and harvesting. The author found that DL’s ability to learn from large datasets has great promise for the transformation of the agriculture industry, but there are challenges, such as the difficulty of compiling datasets, the cost of computational power, and the shortage of DL experts. The author aimed to address these challenges by presenting his survey as a resource for future research and development regarding the use of DL in agriculture.

Suggested Citation

  • Marwan Albahar, 2023. "A Survey on Deep Learning and Its Impact on Agriculture: Challenges and Opportunities," Agriculture, MDPI, vol. 13(3), pages 1-22, February.
    • Handle: RePEc:gam:jagris:v:13:y:2023:i:3:p:540-:d:1078334
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    References listed on IDEAS

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    1. 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.
    2. Chen, Huazhou & Chen, An & Xu, Lili & Xie, Hai & Qiao, Hanli & Lin, Qinyong & Cai, Ken, 2020. "A deep learning CNN architecture applied in smart near-infrared analysis of water pollution for agricultural irrigation resources," Agricultural Water Management, Elsevier, vol. 240(C).
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    2. Abdullah Addas & Muhammad Tahir & Najma Ismat, 2023. "Enhancing Precision of Crop Farming towards Smart Cities: An Application of Artificial Intelligence," Sustainability, MDPI, vol. 16(1), pages 1-18, December.
    3. Jingyu Wang & Miaomiao Li & Chen Han & Xindong Guo, 2024. "YOLOv8-RCAA: A Lightweight and High-Performance Network for Tea Leaf Disease Detection," Agriculture, MDPI, vol. 14(8), pages 1-20, July.
    4. Shouwei Wang & Lijian Yao & Lijun Xu & Dong Hu & Jiawei Zhou & Yexin Chen, 2024. "An Improved YOLOv7-Tiny Method for the Segmentation of Images of Vegetable Fields," Agriculture, MDPI, vol. 14(6), pages 1-16, May.

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