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

Authors

  • 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

DOI:

https://doi.org/10.46604/peti.2023.12701

Keywords:

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

Abstract

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.

References

K. Srinivasan, “Ginger Rhizomes (Zingiber Officinale): A Spice with Multiple Health Beneficial Potentials,” PharmaNutrition, vol. 5, no. 1, pp. 18-28, March 2017.

Food and Agriculture Organization of the United Nations, “FAOSTAT,” https://www.fao.org/faostat/en/#data, April 22, 2022.

Department of Statistics Malaysia, “Supply and Utilization Accounts Selected Agricultural Commodities 2016-2020,” https://www.dosm.gov.my/v1/index.php?r=column/cthemeByCat&cat=164&bul_id=cHgwanhNdU4vWXRvc3pnZU9xSjZTUT09&menu_id=Z0VTZGU1UHBUT1VJMFlpaXRRR0xpdz09, August 26, 2021.

K. P. P. Nair, The Agronomy and Economy of Turmeric and Ginger, 1st ed., Oxford: Elsevier, 2013.

D. Saravanakumar, A Guide to Good Agricultural Practices for Commercial Production of Ginger under Field Conditions in Jamaica, Kingston: Food and Agriculture Organization of the United Nations, 2021.

X. Ai, J. Song, and X. Xu, Ginger Production in Southeast Asia, 1st ed., Boca Raton: CRC Press, 2004.

Y. S. Tong, T. H. Lee, and K. S. Yen, “Deep Learning for Image-Based Plant Growth Monitoring: A Review,” International Journal of Engineering and Technology Innovation, vol. 12, no. 3, pp. 225-246, June 2022.

B. Yang and Y. Xu, “Applications of Deep-Learning Approaches in Horticultural Research: A Review,” Horticulture Research, vol. 8, article no. 123, 2021.

L. Fang, Y. Wu, Y. Li, H. Guo, H. Zhang, X. Wang, et al., “Ginger Seeding Detection and Shoot Orientation Discrimination Using an Improved YOLOv4-LITE Network,” Agronomy, vol. 11, no. 11, article no. 2328, November 2021.

M. L. McHugh, “Interrater Reliability: The Kappa Statistic,” Biochemia Medica, vol. 22, no. 3, pp. 276-282, 2012.

W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, et al., “SSD: Single Shot MultiBox Detector,” Computer Vision – ECCV 2016: 14th European Conference, Amsterdam, pp. 21-37, October 2016.

J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” IEEE Conference on Computer Vision and Pattern Recognition, pp. 779-788, June 2016.

R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation,” IEEE Conference on Computer Vision and Pattern Recognition, pp. 580-587, June 2014.

A. Abade, P. A. Ferreira, and F. de Barros Vidal, “Plant Diseases Recognition on Images Using Convolutional Neural Networks: A Systematic Review,” Computers and Electronics in Agriculture, vol. 185, article no. 106125, June 2021.

N. Genze, R. Bharti, M. Grieb, S. J. Schultheiss, and D. G. Grimm, “Accurate Machine Learning-Based Germination Detection, Prediction and Quality Assessment of Three Grain Crops,” Plant Methods, vol. 16, article no. 157, 2020.

K. M. Knausgård, A. Wiklund, T. K. Sørdalen, K. T. Halvorsen, A. R. Kleiven, L. Jiao, et al., “Temperate Fish Detection and Classification: A Deep Learning Based Approach,” Applied Intelligence, vol. 52, no. 6, pp. 6988-7001, April 2022.

C. Sandoval, E. Pirogova, and M. Lech, “Two-Stage Deep Learning Approach to the Classification of Fine-Art Paintings,” IEEE Access, vol. 7, pp. 41770-41781, March 2019.

K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask R-CNN,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 42, no. 2, pp. 386-397, June 2018.

A. Bochkovskiy, C. Y. Wang, and H. Y. M. Liao, “YOLOv4: Optimal Speed and Accuracy of Object Detection,” https://doi.org/10.48550/arXiv.2004.10934, April 23, 2020.

G. Huang, Z. Liu, L. V. D. Maaten, and K. Q. Weinberger, “Densely Connected Convolutional Networks,” IEEE Conference on Computer Vision and Pattern Recognition, pp. 2261-2269, July 2017.

M. Tan and Q. Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” International Conference on Machine Learning, PMLR, vol. 97, pp. 6105-6114, June 2019.

C. Szegedy, S. Ioffe, V. Vanhoucke, and A. Alemi, “Inception-v4, Inception-Resnet and the Impact of Residual Connections on Learning,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31, no. 1, article no. 11231, 2017.

B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, “Learning Transferable Architectures for Scalable Image Recognition,” IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8697-8710, June 2018.

F. Chollet, “Xception: Deep Learning with Depthwise Separable Convolutions,” IEEE Conference on Computer Vision and Pattern Recognition, pp. 1800-1807, July 2017.

D. Hoiem, Y. Chodpathumwan, and Q. Dai, “Diagnosing Error in Object Detectors,” Computer Vision – ECCV 2012: 12th European Conference on Computer Vision, pp. 340-353, October 2012.

R. Padilla, S. L. Netto, and E. A. B. da. Silva, “A Survey on Performance Metrics for Object-Detection Algorithms,” International Conference on Systems, Signals and Image Processing, pp. 237-242, July 2020.

R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization,” IEEE International Conference on Computer Vision, pp. 618-626, October 2017.

S. Wang, Y. Li, J. Yuan, L. Song, X. Liu, and X. Liu, “Recognition of Cotton Growth Period for Precise Spraying Based on Convolution Neural Network,” Information Processing in Agriculture, vol. 8, no. 2, pp. 219-231, June 2021.

S. Samiei, P. Rasti, J. Ly Vu, J. Buitink, and D. Rousseau, “Deep Learning-Based Detection of Seedling Development,” Plant Methods, vol. 16, article no. 103, 2020.

Downloads

Published

2023-12-18

How to Cite

[1]
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., Dec. 2023.

Issue

Section

Articles