Recognition of Ginger Seed Growth Stages Using a Two-Stage Deep Learning Approach


  • Yin-Syuen Tong School of Mechanical Engineering, Universiti Sains Malaysia, Nibong Tebal, 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



ginger seed germination, growth monitoring, deep learning, instance segmentation


Monitoring the growth of ginger seed relies on human experts due to the lack of salient features for effective recognition. In this study, a region-based convolutional neural network (R-CNN) hybrid detector-classifier model is developed to address the natural variations in ginger sprouts, enabling automatic recognition into three growth stages. Out of 1,746 images containing 2,277 sprout instances, the model predictions revealed significant confusion between growth stages, aligning with the human perception in data annotation, as indicated by Cohen’s Kappa scores. The developed hybrid detector-classifier model achieved an 85.50% mean average precision (mAP) at 0.5 intersections over union (IoU), tested with 402 images containing 561 sprout instances, with an inference time of 0.383 seconds per image. The results confirm the potential of the hybrid model as an alternative to current manual operations. This study serves as a practical case, for extensions to other applications within plant phenotyping communities.


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How to Cite

Yin-Syuen Tong, Tou-Hong Lee, and Kin-Sam Yen, “Recognition of Ginger Seed Growth Stages Using a Two-Stage Deep Learning Approach”, Proc. eng. technol. innov., vol. 26, pp. 01–17, Feb. 2024.