See the published version of this preprint at Plant Methods.
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Research
Hui Fang
College of Biosystems Engineering and Food Science, Zhejiang University
Corresponding Author
ORCiD: https://orcid.org/0000-0003-0277-677X
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Background
Surface roughness has a significant effect on leaf wettability, consequently influencing the efficiency and effectiveness of pesticide spraying application. Therefore, surface roughness measure of plant leaves is conducive to relevant researches. In order to characterize the surface roughness, present methods have to draw support from large apparatus, but they are generally high-cost and not portable enough for field measurement. Methods those instruments even have potentially inherent drawback such as absence of relation between pixel intensity and corresponding height for scanning electron microscope (SEM).
Results
An imaging system with variable object distance is set up to capture images of plant leaves and a shape from focus (SFF) based method is proposed. These space-variantly blurred images are processed with the proposed algorithm to yield surface roughness of plant leaves. The algorithm mainly improves the current SFF method in image alignment, focus distortion correction, and NaN values introducing to make it applicative for precise 3d-reconstruction and surface roughness measure in small scale.
Conclusion
Compared with method via optical three-dimensional interference microscope, the proposed method preserves the overall topography of leaf surface and meanwhile achieves superior cost performance. Experiments on standard gauge blocks revealed the RMSE of step was approximately 4.44μm. Furthermore, the focus measure operator SML was supposed to perform best according to Friedman/Nemenyi test.
Keywords
Shape from focus, 3d-reconstruction, surface roughness
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CURRENT STATUS: Published
View the published article at Plant Methods.
Version 1
Posted 05 Jan, 2021
No community comments so far
Editorial decision: Major revision
On 15 Apr, 2021
Review #2 received
Received 10 Apr, 2021
Reviewer #2 agreed
On 22 Mar, 2021
Review #1 received
Received 16 Jan, 2021
Reviewer #1 agreed
On 05 Jan, 2021
Reviewers invited
Invitations sent on 04 Jan, 2021
First submitted
On 28 Dec, 2020
Editor assigned
On 28 Dec, 2020
Submission checks complete
On 28 Dec, 2020
Editor invited
On 28 Dec, 2020
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See the published version of this preprint at Plant Methods.
This is a preprint, a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research
Hui Fang
College of Biosystems Engineering and Food Science, Zhejiang University
Corresponding Author
ORCiD: https://orcid.org/0000-0003-0277-677X
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Published
View the published article at Plant Methods.
Version 1
Posted 05 Jan, 2021
No community comments so far
Editorial decision: Major revision
On 15 Apr, 2021
Review #2 received
Received 10 Apr, 2021
Reviewer #2 agreed
On 22 Mar, 2021
Review #1 received
Received 16 Jan, 2021
Reviewer #1 agreed
On 05 Jan, 2021
Reviewers invited
Invitations sent on 04 Jan, 2021
First submitted
On 28 Dec, 2020
Editor assigned
On 28 Dec, 2020
Submission checks complete
On 28 Dec, 2020
Editor invited
On 28 Dec, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
View the published article at Plant Methods.
Background
Surface roughness has a significant effect on leaf wettability, consequently influencing the efficiency and effectiveness of pesticide spraying application. Therefore, surface roughness measure of plant leaves is conducive to relevant researches. In order to characterize the surface roughness, present methods have to draw support from large apparatus, but they are generally high-cost and not portable enough for field measurement. Methods those instruments even have potentially inherent drawback such as absence of relation between pixel intensity and corresponding height for scanning electron microscope (SEM).
Results
An imaging system with variable object distance is set up to capture images of plant leaves and a shape from focus (SFF) based method is proposed. These space-variantly blurred images are processed with the proposed algorithm to yield surface roughness of plant leaves. The algorithm mainly improves the current SFF method in image alignment, focus distortion correction, and NaN values introducing to make it applicative for precise 3d-reconstruction and surface roughness measure in small scale.
Conclusion
Compared with method via optical three-dimensional interference microscope, the proposed method preserves the overall topography of leaf surface and meanwhile achieves superior cost performance. Experiments on standard gauge blocks revealed the RMSE of step was approximately 4.44μm. Furthermore, the focus measure operator SML was supposed to perform best according to Friedman/Nemenyi test.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9
This preprint is available for download as a PDF.
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