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Vision-Based Xylem Wetness Classification in Stem Water Potential Determination

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Advances in Visual Computing (ISVC 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15047))

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Abstract

Water is often overused in irrigation, making efficient management of it crucial. Precision Agriculture emphasizes tools like stem water potential (SWP) analysis for better plant status determination. However, such tools often require labor-intensive in-situ sampling. Automation and machine learning can streamline this process and enhance outcomes. This work focused on automating stem detection and xylem wetness classification using the Scholander Pressure Chamber, a widely used but demanding method for SWP measurement. The aim was to refine stem detection and develop computer-vision-based methods to better classify water emergence at the xylem. To this end, we collected and manually annotated video data, applying vision- and learning-based methods for detection and classification. Additionally, we explored data augmentation and fine-tuned parameters to identify the most effective models. The identified best-performing models for stem detection and xylem wetness classification were evaluated end-to-end over 20 SWP measurements. Learning-based stem detection via YOLOv8n combined with ResNet50-based classification achieved a Top-1 accuracy of 80.98%, making it the best-performing approach for xylem wetness classification.

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Notes

  1. 1.

    In one instance we performed SWP measurements on two separate sessions. To keep the air compressor’s temperature within safe limits, a maximum of 30 leaves can be tested at each SWP measurement session. Hence, the rest 20 leaves were stored in a cool and dry place overnight, and measurements resumed 23 h post-excision. Images from the corresponding videos were used in stem detection; however, they were excluded from the xylem wetness classification as they were considerably drier.

  2. 2.

    We refer the reader to https://bit.ly/arcs_isvc24 for supplementary information.

  3. 3.

    https://opencv.org/.

  4. 4.

    https://doi.org/10.5281/zenodo.3908559.

  5. 5.

    https://github.com/ultralytics/ultralytics.

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Acknowledgments

We gratefully acknowledge the support of NSF # CMMI-2326309, USDA-NIFA # 2021-67022-33453, and The University of California under grant UC-MRPI M21PR3417. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agencies.

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Authors and Affiliations

  1. University of California, Riverside, Riverside, CA, 92521, USA

    Pamodya Peiris, Aritra Samanta, Caio Mucchiani, Cody Simons, Amit Roy-Chowdhury & Konstantinos Karydis

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  1. Pamodya Peiris
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Corresponding author

Correspondence to Konstantinos Karydis .

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Editors and Affiliations

  1. University of Nevada Reno, Reno, NV, USA

    George Bebis

  2. Johns Hopkins University, Baltimore, MD, USA

    Vishal Patel

  3. Chinese University of Hong Kong, Shatin, Hong Kong

    Jinwei Gu

  4. University of California, Davis, CA, USA

    Julian Panetta

  5. George Mason University, Fairfax, VA, USA

    Yotam Gingold

  6. University of Georgia, Athens, GA, USA

    Kyle Johnsen

  7. Colorado State University, Fort Collins, CO, USA

    Mohammed Safayet Arefin

  8. Indian Institute of Technology, Kanpur, Uttar Pradesh, India

    Soumya Dutta

  9. Los Alamos National Lab., Los Alamos, NM, USA

    Ayan Biswas

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Peiris, P., Samanta, A., Mucchiani, C., Simons, C., Roy-Chowdhury, A., Karydis, K. (2025). Vision-Based Xylem Wetness Classification in Stem Water Potential Determination. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2024. Lecture Notes in Computer Science, vol 15047. Springer, Cham. https://doi.org/10.1007/978-3-031-77389-1_10

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  • DOI: https://doi.org/10.1007/978-3-031-77389-1_10

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