Article Sidebar

Download
PDF
Statistic
Read Counter : 94 Download : 101

Main Article Content

Abstract

Potato plants have a very high nutritional value, making them widely cultivated in Indonesia. To ensure the cultivation of potatoes has good quality, many individuals, ranging from farmers to researchers and plant breeders, strive to explore and understand the characteristics of plant resistance sources, one of which is through the role of trichomes. Trichomes are fine hairs that coat the outer surface of plant leaves, serving as a physical barrier and regulating plant temperature. Identification and quantification of trichomes are commonly conducted manually by researchers, which consumes much time and is inefficient. Therefore, a system that can automatically detect and quantify trichomes is crucial to avoid manual identification and quantification, allowing these processes to be carried out more quickly. This study utilized a deep learning approach to train a model capable of detecting and quantifying trichome objects. The model architecture used was YOLOv8. From the training process, the resulting mean average precision (mAP) at a confidence threshold of 50 was 0.816, while the mAP at a confidence threshold of 90 was 0.38. This model is expected to assist experts or researchers in the field of agriculture in identifying trichomes, thereby optimizing crop yields.

Keywords

glandular trichomes potato deep learning object detection YOLOv8

Article Details

License

Authors who publish with this journal agree to the following terms:

  1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
  2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
  3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
How to Cite
Azhari, M. F., Rohmatul Fajriyah, Izzati Muhimmah, Dan Jeric Arcega Rustia, Smulders, M. J., & Gracianna Devi, M. (2024). Detection and Quantification of Glandular Trichomes (Bulbous) on Potato Plant Leaf Images Using Deep Learning. Enthusiastic : International Journal of Applied Statistics and Data Science, 4(2), 96–108. https://doi.org/10.20885/enthusiastic.vol4.iss2.art2
Download Citation
Endnote/Zotero/Mendeley (RIS)
BibTeX

References

  1. Y. Zhang et al., “The Roles of Different Types of Trichomes in Tomato,” Agronomy, Vol. 10, No. 3, , 2020, Art. n. 411, doi: 10.3390/agronomy10030411.
  2. N. Bergau, S. Bennewitz, F. Syrowatka, G. Hause, and A. Tissier, “The Development of Type VI Glandular Trichomes in the Cultivated Tomato Solanum Lycopersicum and a Related Wild Species S. Habrochaites,” BMC Plant Biology, Vol. 15, No. 1, pp. 1–15, 2015, doi: 10.1186/s12870-015-0678-z.
  3. J.J. Glas, B.C.J. Schimmel, J.M. Alba, R. Escobar-Bravo, R.C. Schuurink, and M.R. Kant, “Plant glandular trichomes as targets for breeding or engineering of resistance to herbivores,” International Journal of Molecular Sciences Vol. 13, No. 12, pp. 17077–17103, 2012, doi: 10.3390/ijms131217077.
  4. A.F. Lucatti, A.W. van Heusden, R.C.H. de Vos, RG.F. Visser, and B. Vosman, “Differences in Insect Resistance Between Tomato Species Endemic to the Galapagos Islands,” BMC Evolutionary Biology, Vol. 13, No. 1, 2013, Art. No. 175, doi: 10.1186/1471-2148-13-175.
  5. X. Ni, C. Li, H. Jiang, and F. Takeda, “Deep Learning Image Segmentation and Extraction of Blueberry Fruit Traits Associated with Harvestability and Yield,” Horticulture Research, Vol. 7, 2020, Art. No. 110, doi: 10.1038/s41438-020-0323-3.
  6. S.M. Narkhede et al., “Machine Learning Identifies Digital Phenotyping Measures Most Relevant to Negative Symptoms in Psychotic Disorders: Implications for Clinical Trials,” Schizophrenia Bulletin, Vol. 48, No. 2, pp. 425–436, 2022, doi: 10.1093/schbul/sbab134.
  7. B. Vosman et al., “QTL Mapping of Insect Resistance Components of Solanum Galapagense,” Theoretical and Applied Genetics, Vol. 132, No. 2, pp. 531–541, 2019, doi: 10.1007/s00122-018-3239-7.
  8. T. Diwan, G. Anirudh, and J.V. Tembhurne, “Object Detection Using YOLO: Challenges, Architectural Successors, Datasets and Applications,” Multimedia Tools and Applications, Vol. 82, No. 6, pp. 9243–9275, 2023, doi: 10.1007/s11042-022-13644-y.
  9. I. Denata, T. Rismawan, and I. Ruslianto, “Implementation of Deep Learning for Classification Type of Orange Using The Method Convolutional Neural Network,” Telematika, Vol. 18, No. 3, pp. 297–307, 2021, doi: 10.31315/telematika.v18i3.5541.
  10. S. Indolia, A.K. Goswami, S.P. Mishra, and P. Asopa, “Conceptual Understanding of Convolutional Neural Network-A Deep Learning Approach,” Procedia Computer Science, Vol. 132, pp. 679–688, 2018, doi: 10.1016/j.procs.2018.05.069.
  11. J.P. Onnela, “Opportunities and Challenges in the Collection and Analysis of Digital Phenotyping Data,” Neuropsychopharmacology, Vol. 46, No. 1, pp. 45–54, 2021, doi: 10.1038/s41386-020-0771-3.
  12. M.H. Saleem, J. Potgieter, and K.M. Arif, “Plant Disease Classification: A Comparative Evaluation of Convolutional Neural Networks and Deep Learning Optimizers,” Plants, Vol. 9, No. 10, pp. 1–17, 2020, doi: 10.3390/plants9101319.
  13. R. Kaur and S. Singh, “A Comprehensive Review of Object Detection With Deep Learning,” Digital Signal Processing, Vol. 132, 2022, doi: 10.1016/j.dsp.2022.103812.
  14. J. Liu and X. Wang, “Plant Diseases and Pests Detection Based on Deep Learning: A Review,” Plant Methods, Vol. 17, No. 1, pp. 1–18, 2021, doi: 10.1186/s13007-021-00722-9.
  15. W. Albattah, M. Nawaz, A. Javed, M. Masood, and S. Albahli, “A Novel Deep Learning Method for Detection and Classification of Plant Diseases,” Complex & Intelligent Systems, Vol. 8, no. 1, pp. 507–524, 2022, doi: 10.1007/s40747-021-00536-1.
  16. S.S. Harakannanavar, J.M. Rudagi, V.I. Puranikmath, A. Siddiqua, and R. Pramodhini, “Plant Leaf Disease Detection Using Computer Vision and Machine Learning Algorithms,” Global Transitions Proceedings, Vol. 3, No. 1, pp. 305–310, 2022, doi: 10.1016/j.gltp.2022.03.016.
  17. L. Li, S. Zhang, and B. Wang, “Plant Disease Detection and Classification by Deep Learning - A Review,” IEEE Access, Vol. 9, pp. 56683–56698, 2021, doi: 10.1109/ACCESS.2021.3069646.
  18. C. Chen et al., “YOLO-Based UAV Technology: A Review of the Research and Its Applications,” Drones, Vol. 7, No. 3, 2023, doi: 10.3390/drones7030190.
  19. J. Huixian, “The Analysis of Plants Image Recognition Based on Deep Learning and Artificial Neural Network,” IEEE Access, Vol. 8, pp. 68828–68841, 2020, doi: 10.1109/ACCESS.2020.2986946.
  20. L. Kang, Z. Lu, L. Meng, and Z. Gao, “YOLO-FA: Type-1 fuzzy attention based YOLO detector for vehicle detection,” Expert Systems with Applications, Vol. 237, 2024, doi: 10.1016/j.eswa.2023.121209.
  21. J.W. Wu, W. Cai, S.M. Yu, Z.L. Xu, and X. Y. He, “Optimized Visual Recognition Algorithm in Service Robots,” International Journal of Advanced Robotic Systems, Vol. 17, No. 3, pp. 1–11, 2020, doi: 10.1177/1729881420925308.