Deep Learning for Image-Based Plant Growth Monitoring: A Review

  • Yin-Syuen Tong School of Mechanical Engineering, Universiti Sains Malaysia; TMS LITE Sendirian Berhad, Sungai Ara, Malaysia
  • Tou-Hong Lee TMS LITE Sendirian Berhad, Sungai Ara, Malaysia
  • Kin-Sam Yen School of Mechanical Engineering, Universiti Sains Malaysia, Nibong Tebal, Malaysia
Keywords: deep learning, CNN, phenotyping, plant growth monitoring


Deep learning (DL) approaches have received extensive attention in plant growth monitoring due to their ground-breaking performance in image classification; however, the approaches have yet to be fully explored. This review article, therefore, aims to provide a comprehensive overview of the work and the DL developments accomplished over the years. This work includes a brief introduction on plant growth monitoring and the image-based techniques used for phenotyping. The bottleneck in image analysis is discussed and the need of DL methods in plant growth monitoring is highlighted. A number of research works focused on DL based plant growth monitoring-related applications published since 2017 have been identified and included in this work for review. The results show that the advancement in DL approaches has driven plant growth monitoring towards more complicated schemes, from simple growth stages identification towards temporal growth information extraction. The challenges, such as resource-demanding data annotation, data-hungriness for training, and extraction of both spatial and temporal features simultaneously for accurate plant growth prediction, however, remain unsolved.


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How to Cite
Y.-S. Tong, T.-H. Lee, and K.-S. Yen, “Deep Learning for Image-Based Plant Growth Monitoring: A Review”, Int. j. eng. technol. innov., May 2022.
Special Issue: Emerging Innovations in Deep Learning