research-article

Identification of Plant Stomata Based on YOLO v5 Deep Learning Model

Authors:

Fan ZhangAuthors Info & Claims

CSAI '21: Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence

Pages 78 - 83

https://doi.org/10.1145/3507548.3507560

Published: 09 March 2022 Publication History

Abstract

Stomata is an important structure in all terrestrial plants and is very vital in controlling plant photosynthesis and transpiration flow. Precise detection of plant stomata is the basis for studying stomata characteristics. Traditional detection methods are mostly manual operations, which is a tedious and inefficient process. Manually extracting features requires high image quality. Choosing appropriate features depends on certain prior knowledge, especially for the object with large morphological changes such as plant stomata. With the widespread use of deep learning technology, efficient solutions to this task have become possible. This article combines the characteristics of the corn leaf stomatal data sets to improve the latest object detection model YOLO v5)You Only Look Once(. By introducing the attention mechanism, that is, adding the SE module to the backbone network, the precision and recall of stoma detection are improved. At the same time, The loss function has been improved from to for avoiding some problems that may occur when selecting the best prediction box. Experimental results show that the precision and recall rates of the improved model on the corn leaf stomata data sets have reached 94.8% and 98.7% respectively, lay the foundation for the measurement of stomatal parameters. In addition, this paper also can help agriculturists and botanists to build their own data sets for stomatal research by explaining the methods of acquiring, pre-processing, and annotating data sets.

References

[1]

Millstead, L., Jayakody, H.,Patel, H., Kaura, V., and Whitty, M. (2020), “Accelerating automated stomata analysis through simplified sample collection and imaging techniques.” Frontiers in Plant Science, 11:1493-1502.

[2]

Fanourakis, D., Aliniaeifard, S., Sellin, A., Giday, H., Korner, O., Nejad, AR., Delis, C., Bouranis, D., Koubouris, G., and Kambourakis, E. (2020). “Stomatal behavior following mid- or long-term exposure to high relative air humidity: A review.” Plant Physiol. Biochem. 153:92–105.

[3]

Fanourakis, D., Nikoloudakis, N., Pappi, P., Markakis, E., Doupis, G., Charova, SN., Delis, C., and Tsaniklidis, G. (2020), “The role of proteases in determining stomatal development and tuning pore aperture: A review.” Plants. 9:340-354.

[4]

Fanourakis, D., Bouranis, D., Tsaniklidis, G., Nejad, AR., Ottosen, CO., and Woltering, EJ. (2020). “Genotypic and phenotypic differences in fresh weight partitioning of cut rose stems: Implications for water loss.” Acta Physiol.Plant. 42, 1–10.

[5]

Sorensen, HK., Fanourakis, D., Tsaniklidis, G., Bouranis, D., Nejad, AR., and Ottosen, CO. (2020). “Using artificial lighting based on electricity price without a negative impact on growth, visual quality or stomatal closing response in passiflora.” Sci. Hortic. 267, 109354.

[6]

Wu, G, Liu, H, and Hua, L. (2018). “Differential Responses of Stomata and Photosynthesis to Elevated Temperature in Two Co-occurring Subtropical Forest Tree Species.” Frontiers in Plant Science, 9:467-479.

[7]

Melotto, M, Underwood, W, and Koczan, J. (2006). “Plant Stomata Function in Innate Immunity against Bacterial Invasion.” Cell, 126(5):969-980.

[8]

Zhang, L., Liu, L., Zhao, H., Jiang, Z., and Cai, J. (2020). “Differences in near isohydric and anisohydric behavior of contrasting poplar hybrids (i-101 (populus alba l.) × 84k (populus alba l. × populus glandulosa uyeki))under drought-rehydration treatments.” Forests, 11:402-415.

[9]

Fetter, KC., Eberhardt, S., Barclay, RS., Wing, S., and Keller, SR. (2019).“Stomatacounter: A neural network for automatic stomata identification and counting.” New Phytol. 223:1671–1681.

[10]

Mott, KA. (2020). “Opinion:Stomatal responses to light and CO2 depend on the mesophyll.” Plant Cell & Environment, 32(11):1479-1486.

[11]

Rueden, CT., Schindelin, J., and Hiner, MC. (2017). “Image J2: ImageJ for the next generation of scientific image data.” BMC Bioinformatics 18, 529. https://doi.org/10.1186/s12859-017-1934-z.

[12]

Oliveira, M., Silva, N. (2014). “Automatic counting of stomata in epidermis microscopic images.” In: Workshop de Visao Computacional, WVC’14, pp. 253–257

[13]

Liu, S., Tang, J., and Petrie, P. (2016). “A fast method to measure stomatal aperture by MSER on smart mobile phone.” In: Conference on Imaging and Applied Optics, AIO’16, page AIW2B.2.

[14]

Duarte, K.T.N., Carvalho, M.A.G., and Martins, P.S. (2017). Segmenting high-quality digital images of stomata using the wavelet spot detection and the watershed transform. In:International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISAPP’17, pp. 540–547. https://doi.org/10.5220/0006168105400547.

[15]

Vialet-Chabrand, S. and Brendel, O. (2014). “Automatic measurement of stomatal density from microphotographs.” Trees 28 (6), 1859–1865. https://doi.org/10.1007/s00468-014-1063-5.

[16]

Jayakody, H., Liu, S., and Whitty, M. (2017). “Microscope image based fully automated stomata detection and pore measurement method for grapevines.” Plant Methods 13(94):1–12. https://doi.org/10.1186/s13007-017-0244-9.

[17]

Toda, Y., Toh, S., and Bourdais, G. (2018). DeepStomata: Facial Recognition Technology for Automated Stomatal Aperture Measurement. bioRXiv, 2018.

[18]

Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, SE.,Fu, C., and Berg, AC. (2016). “SSD: single shot multibox detector,” in European Conference on Computer Vision, ECCV, pp. 21–37.

[19]

Ren, S., He, K., and Girshick, R., (2017). “Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks.” IEEE Transactions on Pattern Analysis & Machine Intelligence, 39(6):1137-1149.

Digital Library

[20]

Bhugra, S., Mishra, D., and Anupama, A. (2018). “Deep convolutional neural networks based framework for estimation of stomata density and structure from microscopic images.” In: European Conference on Computer Vision, volume 11134 of ECCV’18, pp.412–423. https://doi.org/10.1007/978-3-030-11024-6_31.

Digital Library

[21]

Li, K., Huang, J., and Song, W. (2019). “Automatic segmentation and measurement methods of living stomata of plants based on the CV model.” Plant Methods 15(67):1–12. https://doi.org/10.1186/s13007-019-0453-5.

[22]

Sakoda, K., Watanabe, T., and Sukemura, S. (2019). Genetic diversity in stomatal density among soybeans elucidated using high-throughput technique based on an algorithm for object detection. Sci. Rep. 9 (7610), 1–9. https://doi.org/10.1038/s41598-019-44127-0.

[23]

Redmon, J. and Farhadi, A. (2018). “YOLOv3: An Incremental Improvement.” arXiv e-prints.

[24]

Casado-García, A., Del-Canto, A., Sanz-Saez, A., Pérez-López, U., and Heras, J. (2020) “Computers and Electronics in Agriculture LabelStoma: A tool for stomata detection based on the YOLO algorithm.” Computers and Electronics in Agriculture.178:1689-1699. https://doi.org/10.1016/j.compag.2020.105751.

[25]

Ryo Hasegawa, Yutaro Iwamoto, and Yen-Wei Chen, "Robust Japanese Road Sign Detection and Recognition in Complex Scenes Using Convolutional Neural Networks," Journal of Image and Graphics, Vol. 8, No. 3, pp. 59-66, September 2020.

[26]

Fitri Utaminingrum, Renaldi P. Prasetya, and Rizdania, "Combining Multiple Feature for Robust Traffic Sign Detection," Journal of Image and Graphics, Vol. 8, No. 2, pp. 53-58, June 2020.

[27]

Shiqi Huang, Yiting Wang, and Peifeng Su, "A New Synthetical Method of Feature Enhancement and Detection for SAR Image Targets," Journal of Image and Graphics, Vol. 4, No. 2, pp. 73-77, December 2016.

[28]

Edisanter Lo, "Target Detection Algorithms in Hyperspectral Imaging Based on Discriminant Analysis," Journal of Image and Graphics, Vol. 7, No. 4, pp. 140-144, December 2019.

[29]

Bochkovskiy, A., Wang, CY., and Liao, H. (2020). “YOLOv4: Optimal Speed and Accuracy of Object Detection.” arXiv .1-17.

[30]

Jocher G.(2020).”Yolov5”. Code repository https://github.com/ultralytics/yolov5.

[31]

Yang, N, Lu, Y, Yang, X, and Huang, J. (2014).”Genome Wide Association Studies Using a New Nonparametric Model Reveal the Genetic Architecture of 17 Agronomic Traits in an Enlarged Maize Association Panel.” Plos Genetics, 10(9):e1004573.

[32]

Zhu, JY., Xu, CY., Wu, J. (2018). “Rapid measurement method of stomatal density and stomatal area based on ecology.” Journal of Beijing Forestry University, 40(05):41-49.

[33]

Karayaneva and Diana Hintea, "Object Recognition in Python and MNIST Dataset Modification and Recognition with Five Machine Learning Classifiers," Journal of Image and Graphics, Vol. 6, No. 1, pp. 10-20 June 2018.

[34]

Casado, N., Dominguez, C., and Garcia, M. (2019). “CLoDSA: a tool for augmentation in classification, localization, detection, semantic segmentation and instance segmentationtasks.” BMCBioinformatics,20(1),https://doi.org/10.1186/s12859-019-2931-1.

[35]

Lin, TY., Dollar, P., and Girshick, R. (2017). “Feature Pyramid Networks for Object Detection[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).” IEEE Computer Society.

[36]

Zheng, Z., Wang, P., and Liu, W. (2019). “Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression”. arXiv.

Cited By

Mauladany MFatkhurrozi BWibowo R(2024)Deteksi Penyakit Daun Durian dengan Algoritma YOLO (You Only Look Once)AVITEC10.28989/avitec.v6i1.20676:1(73)Online publication date: 16-Feb-2024
https://doi.org/10.28989/avitec.v6i1.2067
Jyothsna DChandra G(2024)Multi-Object Detection in 3D Point Cloud's Range Image Using Deep-Learning Technique2024 14th International Conference on Cloud Computing, Data Science & Engineering (Confluence)10.1109/Confluence60223.2024.10463402(401-406)Online publication date: 18-Jan-2024
https://doi.org/10.1109/Confluence60223.2024.10463402
Wang JRenninger HMa Q(2024)Labeled temperate hardwood tree stomatal image datasets from seven taxa of Populus and 17 hardwood speciesScientific Data10.1038/s41597-023-02657-311:1Online publication date: 2-Jan-2024
https://doi.org/10.1038/s41597-023-02657-3
Show More Cited By

Index Terms

Identification of Plant Stomata Based on YOLO v5 Deep Learning Model
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
  2. Machine learning
    1. Learning paradigms
    2. Machine learning approaches
      1. Neural networks

Index terms have been assigned to the content through auto-classification.

Recommendations

Detection and Identification of Peach Leaf Diseases based on YOLO v5 Improved Model
ICCCV '22: Proceedings of the 5th International Conference on Control and Computer Vision

Aiming at the problem of peach tree leaf diseases in modern orchards, this work proposes a lightweight identification method based on the combination of improved YOLO v5 and ShuffleNet to achieve accurate identification of peach tree leaf diseases in ...
A deep learning approach incorporating YOLO v5 and attention mechanisms for field real-time detection of the invasive weed Solanum rostratum Dunal seedlings
Highlights
- A deep learning method incorporating YOLO v5 and attention mechanism is proposed.
- A deep learning method for real-time detection of Solanum rostratum Dunal seedlings.
- A method of slicing the high-resolution images by calculating ...
Abstract
Solanum rostratum Dunal is a common invasive alien weed that can damage native ecosystems and biodiversity. Detecting Solanum rostratum Dunal at an early stage of growth will make it possible to treat it before it causes serious damage. ...
Deep learning model for detection of brown spot rice leaf disease with smart agriculture
Abstract
Given that it provides nourishment for more than half of humanity, rice is regarded as one of the most significant plants in the world in agriculture. The quantity and quality of the product may be impacted by diseases that can damage rice plants ...
Graphical abstract
Proposed CNN-VGG19 model for detection of rice leaf disease.

Display Omitted
Highlights
- The CNN-VGG19 model is developed for the recognition of brown spot rice leaf diseases.
- The CNN-VGG19 is based on transfer learning for the precise identification of the brown spot leaf disease.
- The highest accuracy, precision, and ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

CSAI '21: Proceedings of the 2021 5th International Conference on Computer Science and Artificial Intelligence

December 2021

437 pages

ISBN:9781450384155

DOI:10.1145/3507548

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 09 March 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

CSAI 2021

CSAI 2021: 2021 5th International Conference on Computer Science and Artificial Intelligence

December 4 - 6, 2021

Beijing, China

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
184
Total Downloads

Downloads (Last 12 months)41
Downloads (Last 6 weeks)1

Reflects downloads up to 24 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Mauladany MFatkhurrozi BWibowo R(2024)Deteksi Penyakit Daun Durian dengan Algoritma YOLO (You Only Look Once)AVITEC10.28989/avitec.v6i1.20676:1(73)Online publication date: 16-Feb-2024
https://doi.org/10.28989/avitec.v6i1.2067
Jyothsna DChandra G(2024)Multi-Object Detection in 3D Point Cloud's Range Image Using Deep-Learning Technique2024 14th International Conference on Cloud Computing, Data Science & Engineering (Confluence)10.1109/Confluence60223.2024.10463402(401-406)Online publication date: 18-Jan-2024
https://doi.org/10.1109/Confluence60223.2024.10463402
Wang JRenninger HMa Q(2024)Labeled temperate hardwood tree stomatal image datasets from seven taxa of Populus and 17 hardwood speciesScientific Data10.1038/s41597-023-02657-311:1Online publication date: 2-Jan-2024
https://doi.org/10.1038/s41597-023-02657-3
Chen PXie F(2023)A Machine Learning Approach for Automated Detection of Critical PCB Flaws in Optical Sensing SystemsPhotonics10.3390/photonics1009098410:9(984)Online publication date: 29-Aug-2023
https://doi.org/10.3390/photonics10090984
Valdez DAliac CFeliscuzo L(2023)Detecting Medicinal Plants Using YOLOv5: A Mobile Vision Approach2023 International Conference on Inventive Computation Technologies (ICICT)10.1109/ICICT57646.2023.10134246(533-538)Online publication date: 26-Apr-2023
https://doi.org/10.1109/ICICT57646.2023.10134246
Li KCong SDai TZhang JLiu J(2023)Microscopy image recognition method of stomatal open and closed states in living leaves based on improved YOLO-XTheoretical and Experimental Plant Physiology10.1007/s40626-023-00296-y35:4(395-406)Online publication date: 13-Oct-2023
https://doi.org/10.1007/s40626-023-00296-y
Fu XLi AMeng ZYin XZhang CZhang WQi L(2022)A Dynamic Detection Method for Phenotyping Pods in a Soybean Population Based on an Improved YOLO-v5 NetworkAgronomy10.3390/agronomy1212320912:12(3209)Online publication date: 17-Dec-2022
https://doi.org/10.3390/agronomy12123209
Li XGuo SGong LLan Y(2022)An automatic plant leaf stoma detection method based on YOLOv5IET Image Processing10.1049/ipr2.1261717:1(67-76)Online publication date: 29-Aug-2022
https://doi.org/10.1049/ipr2.12617

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Figures

Tables

Media

View Table of Conten