IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v10y2020i10p462-d425078.html
   My bibliography  Save this article

Research Status and Prospects on Plant Canopy Structure Measurement Using Visual Sensors Based on Three-Dimensional Reconstruction

Author

Listed:
  • Jizhang Wang

    (Key Laboratory of Modern Agricultural Equipment and Technology, Ministry of Education and Jiangsu Province, School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Yun Zhang

    (Institute of Field Management Equipment, School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China)

  • Rongrong Gu

    (Shanghai Research Institute for Intelligent Autonomous Systems, School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China)

Abstract

Three-dimensional (3D) plant canopy structure analysis is an important part of plant phenotype studies. To promote the development of plant canopy structure measurement based on 3D reconstruction, we reviewed the latest research progress achieved using visual sensors to measure the 3D plant canopy structure from four aspects, including the principles of 3D plant measurement technologies, the corresponding instruments and specifications of different visual sensors, the methods of plant canopy structure extraction based on 3D reconstruction, and the conclusion and promise of plant canopy measurement technology. In the current research phase on 3D structural plant canopy measurement techniques, the leading algorithms of every step for plant canopy structure measurement based on 3D reconstruction are introduced. Finally, future prospects for a standard phenotypical analytical method, rapid reconstruction, and precision optimization are described.

Suggested Citation

  • Jizhang Wang & Yun Zhang & Rongrong Gu, 2020. "Research Status and Prospects on Plant Canopy Structure Measurement Using Visual Sensors Based on Three-Dimensional Reconstruction," Agriculture, MDPI, vol. 10(10), pages 1-27, October.
    • Handle: RePEc:gam:jagris:v:10:y:2020:i:10:p:462-:d:425078
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/10/10/462/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/10/10/462/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Douglas M. Hawkins, 1980. "Critical Values for Identifying Outliers," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 29(1), pages 95-96, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yawei Wang & Yifei Chen & Xiangnan Zhang & Wenwen Gong, 2021. "Research on Measurement Method of Leaf Length and Width Based on Point Cloud," Agriculture, MDPI, vol. 11(1), pages 1-13, January.
    2. Daniel Queirós da Silva & André Silva Aguiar & Filipe Neves dos Santos & Armando Jorge Sousa & Danilo Rabino & Marcella Biddoccu & Giorgia Bagagiolo & Marco Delmastro, 2021. "Measuring Canopy Geometric Structure Using Optical Sensors Mounted on Terrestrial Vehicles: A Case Study in Vineyards," Agriculture, MDPI, vol. 11(3), pages 1-19, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Karol Pilot & Alicja Ganczarek-Gamrot & Krzysztof Kania, 2024. "Dealing with Anomalies in Day-Ahead Market Prediction Using Machine Learning Hybrid Model," Energies, MDPI, vol. 17(17), pages 1-20, September.
    2. Damian Przekop, 2020. "Feature Engineering for Anti-Fraud Models Based on Anomaly Detection," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 12(3), pages 301-316, September.
    3. Francesca Ieva & Anna Maria Paganoni, 2020. "Component-wise outlier detection methods for robustifying multivariate functional samples," Statistical Papers, Springer, vol. 61(2), pages 595-614, April.
    4. Gaucher, Solenne & Klopp, Olga & Robin, Geneviève, 2021. "Outlier detection in networks with missing links," Computational Statistics & Data Analysis, Elsevier, vol. 164(C).
    5. Andrzej Chmielowiec, 2021. "Algorithm for error-free determination of the variance of all contiguous subsequences and fixed-length contiguous subsequences for a sequence of industrial measurement data," Computational Statistics, Springer, vol. 36(4), pages 2813-2840, December.
    6. Marc Chataigner & Stéphane Crépey & Jiang Pu, 2020. "Nowcasting Networks," Post-Print hal-03910123, HAL.
    7. Greco, Salvatore & Ishizaka, Alessio & Tasiou, Menelaos & Torrisi, Gianpiero, 2019. "Sigma-Mu efficiency analysis: A methodology for evaluating units through composite indicators," European Journal of Operational Research, Elsevier, vol. 278(3), pages 942-960.
    8. David Juárez-Varón & Victoria Tur-Viñes & Alejandro Rabasa-Dolado & Kristina Polotskaya, 2020. "An Adaptive Machine Learning Methodology Applied to Neuromarketing Analysis: Prediction of Consumer Behaviour Regarding the Key Elements of the Packaging Design of an Educational Toy," Social Sciences, MDPI, vol. 9(9), pages 1-23, September.
    9. Stéphane Crépey & Lehdili Noureddine & Nisrine Madhar & Maud Thomas, 2022. "Anomaly Detection on Financial Time Series by Principal Component Analysis and Neural Networks," Working Papers hal-03777995, HAL.
    10. Zhongqiu Wang & Guan Yuan & Haoran Pei & Yanmei Zhang & Xiao Liu, 2020. "Unsupervised learning trajectory anomaly detection algorithm based on deep representation," International Journal of Distributed Sensor Networks, , vol. 16(12), pages 15501477209, December.
    11. Arata, Linda & Fabrizi, Enrico & Sckokai, Paolo, 2020. "A worldwide analysis of trend in crop yields and yield variability: Evidence from FAO data," Economic Modelling, Elsevier, vol. 90(C), pages 190-208.
    12. Wentao Yang & Huaxi He & Dongsheng Wei & Hao Chen, 2022. "Generating pseudo-absence samples of invasive species based on outlier detection in the geographical characteristic space," Journal of Geographical Systems, Springer, vol. 24(2), pages 261-279, April.
    13. Fournier, Nicholas PhD & Farid, Yashar Zeinali PhD & Patire, Anthony David PhD, 2021. "Potential Erroneous Degradation of High Occupancy Vehicle (HOV) Facilities," Institute of Transportation Studies, Research Reports, Working Papers, Proceedings qt3z76r7tj, Institute of Transportation Studies, UC Berkeley.
    14. Richter, Lucas & Lehna, Malte & Marchand, Sophie & Scholz, Christoph & Dreher, Alexander & Klaiber, Stefan & Lenk, Steve, 2022. "Artificial Intelligence for Electricity Supply Chain automation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    15. Tommaso Barbariol & Enrico Feltresi & Gian Antonio Susto, 2020. "Self-Diagnosis of Multiphase Flow Meters through Machine Learning-Based Anomaly Detection," Energies, MDPI, vol. 13(12), pages 1-24, June.
    16. Puteri Paramita & Zuduo Zheng & Md Mazharul Haque & Simon Washington & Paul Hyland, 2018. "User satisfaction with train fares: A comparative analysis in five Australian cities," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-26, June.
    17. Liqun Diao & Grace Y. Yi, 2023. "Classification Trees with Mismeasured Responses," Journal of Classification, Springer;The Classification Society, vol. 40(1), pages 168-191, April.
    18. Durgesh Samariya & Amit Thakkar, 2023. "A Comprehensive Survey of Anomaly Detection Algorithms," Annals of Data Science, Springer, vol. 10(3), pages 829-850, June.
    19. Sisman, S. & Aydinoglu, A.C., 2022. "Improving performance of mass real estate valuation through application of the dataset optimization and Spatially Constrained Multivariate Clustering Analysis," Land Use Policy, Elsevier, vol. 119(C).
    20. Gasser, Patrick, 2020. "A review on energy security indices to compare country performances," Energy Policy, Elsevier, vol. 139(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jagris:v:10:y:2020:i:10:p:462-:d:425078. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.