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Jiaxin Wang
(王家新, in Chinese)
Ph.D. | Postdoctoral Associate at Virginia Tech | Forestry, Biofuel, Populus, Phenotyping | AI, CNN, Deep Learning | MSU, Mississippi State University
About me
I did tree physiological research for my PhD, specifically tree growth, high-throughput phenotyping, physiology of stomata, photosynthesis, water use efficiency, and their relationships and interaction with environmental factors.
My Specific Research Interests:
Environmental controls on physiological plant function
Structure/function relationships in plants
Tree and forest water use
Short rotation bioenergy crops
I am now a Postdoctoral Associate at Virginia Tech working on the National Park Service National Capital Region Network (NCRN) program.
I will be joining Dr Yun Yang's Lab at Cornell University in 2026.
Research Progress at Virginia Tech
Invasive plant species threaten ecosystem integrity in the USA
Invasive plant species threaten ecosystem integrity in the USA
I observed varied correlations between invasive species coverage and Shannon diversity index across different ecoregions in the USA.
12/09/2024
Spatiotemporal disbribution of native and invasive species in national capital region of USA
Spatiotemporal disbribution of native and invasive species in national capital region of USA
Draft revision.
03/24/2025
Manuscript in preparation.
02/12/2025
Native species, like white oak, are spatially and temporally narrowed, while invasive species, such as Japanese honeysuckle, are expanded. Figure shows the comparison of white oak and Japanese honeysuckle's distribution across the national capital region of USA in 2013 and 2023.
08/21/2024
SapFlower: An automated tool for sap flow data preprocessing, gap filling, and analyzing using deep learning
SapFlower: An automated tool for sap flow data preprocessing, gap filling, and analyzing using deep learning
Published: New Phytologist.
04/08/2025
Accepted in New Phytologist.
03/07/2025
SapFlower 1.0.2 version is available now, with enhanced features including more non-linear models and functionality to handle SAPFLUXNET data!
01/20/2025
In Revision.
01/13/2025
In Review.
11/20/2024
Full Standalone app is available online at Zenodo: https://doi.org/10.5281/zenodo.13665919
Open-source codes are available: https://github.com/JiaxinWang123/SapFlower
Video Tutorial: https://drive.google.com/file/d/1cskIYdHHHBYsw1U-L66W-yFqgLAlNwx7/view?usp=sharing
09/13/2024
Research Progress at MSU
SapFlower: An automated tool for sap flow data preprocessing, gap filling, and analyzing using deep learning
SapFlower: An automated tool for sap flow data preprocessing, gap filling, and analyzing using deep learning
Full Standalone app is under consconstruction.
Several functions open-source codes are available: https://github.com/JiaxinWang123/SapFlower
04/20/2024
Leaf gas exchange, isotope, woody isotope, and tree-level sap flow measurements do not tell the same stories
Leaf gas exchange, isotope, woody isotope, and tree-level sap flow measurements do not tell the same stories
Finished the first draft.
04/18/2024
Manuscript in Preparation
04/02/2024
Understanding phenological variability and management impacts on Populus: Implications for growth, susceptibility, and productivity
Understanding phenological variability and management impacts on Populus: Implications for growth, susceptibility, and productivity
05/19/2025
In Revision. Forest Ecosystems.
03/24/2025
In Review.
10/14/2024
Ready to be submitted.
07/21/2024
Finished the first draft.
03/15/2024
Manuscript in Preparation to be submitted to Global Change Biology
03/02/2024
StoManager1 v.1.0.0: Integrated with training your own model with custom dataset
StoManager1 v.1.0.0: Integrated with training your own model with custom dataset
The latest version is available at Zenodo: https://doi.org/10.5281/zenodo.7686022; and Figshare at https://doi.org/10.6084/m9.figshare.22205020
Our recent Research Article relevant to StoManager1 has been accepted by Plant Physiology
01/09/2024
Has been published on 11/15/2023
11/15/2023
Early spring warming, cold spells, and followed high VPD threaten bioenergy sustainability using Populus
Early spring warming, cold spells, and followed high VPD threaten bioenergy sustainability using Populus
Combined with Phenology Paper, ready to submit.
04/23/2024
Manuscript in Preparation
10/14/2023
Elevated CO2 increases Populus sensitivity to temperature change
Elevated CO2 increases Populus sensitivity to temperature change
In Review.
11/11/2024
Ready to submit
12/25/2023
Manuscript in Preparation
10/11/2023
Easily measurable leaf and stomatal traits explain Populus intrinsic water use efficiency and productivity
Easily measurable leaf and stomatal traits explain Populus intrinsic water use efficiency and productivity
In Revision
08/06/2025
Submitted
04/01/2025
Ready to submit
10/31/2023
Manuscript in Preparation
09/29/2023
Enhanced StoManager1: Automated Segementation and Measurement of Leaf Stomatal Guard Cell Width, Length, and Area
Enhanced StoManager1: Automated Segementation and Measurement of Leaf Stomatal Guard Cell Width, Length, and Area
With over 30 metrics measured using geometric algorithms!!
With over 30 metrics measured using geometric algorithms!!
New version is available at Zenodo: https://doi.org/10.5281/zenodo.7686022; and Figshare at https://doi.org/10.6084/m9.figshare.22205020
03/04/2023
Research has indicated the potential of using machine learning algorithms to detect and measure stomata automatically. However, the current limitation for further improving and fine-tuning machine learning-based stomatal study methods is due to the small, inconsistent, and monotypic nature of stomatal datasets, which are also not easily accessible. To address this issue, our collection comprises more than 11,000 unique images of hardwood leaf stomata gathered from projects conducted between 2015 and 2020-2022. The dataset includes over 7,000 images of 17 frequently encountered hardwood species, including oak, maple, ash, elm, and hickory, as well as over 3,000 images of 55 genotypes from seven Populus taxa (as detailed in Table 1). Each image has been labeled as either stomata (stomatal aperture only) or whole_stomata (stomatal aperture and guard cells) and has a corresponding YOLO label file that can be transformed to other annotation formats. These images and labels are publicly available, making it easier to train machine-learning models and examine leaf stomatal traits. By utilizing our dataset, users can (1) use state-of-the-art machine learning models to identify, count, and quantify leaf stomata; (2) investigate the diverse range of stomatal characteristics across different types of hardwood trees; and (3) create new indices for measuring stomata.
Submitted to Scientific Data
Current status: Accepted
09/06/2023
The characteristics of stomata on leaves are crucial for the performance of plants and their impact on global water and carbon cycling. However, manually counting stomata can be time-consuming, prone to bias, and limited to small scales and sample sizes. We have created StoManager1, a high-throughput tool that automates detecting, counting, and measuring stomata to address this issue. StoManager1 uses convolutional neural networks to estimate parameters such as stomatal density, area, orientation, and variance. Our results show that StoManager1 is highly precise and has an excellent recall for the stomatal characterizing leaves from various species. This tool can automate measuring leaf stomata, making it easier to explore how leaf stomata control and regulate plant growth and adaptation to environmental stress and climate change. An online demonstration of StoManager1 is available on GitHub at https://github.com/JiaxinWang123/StoManager1. We have also developed a standalone, user-friendly Windows application for StoManager1 that does not require any programming or coding experience.
Lanched on February 28, 2023
Latest Preprint is available: here
02/28/2023
Spatiotemporal characterization of heavy metal and antibiotics in the Pearl River Basin and pollutants removal assessment using invasive species-derived biochars
Spatiotemporal characterization of heavy metal and antibiotics in the Pearl River Basin and pollutants removal assessment using invasive species-derived biochars
This work was conducted when I worked as a research assistant at South China Agricultural University from 2020-2021.
Submitted to Journal of Hazardous Materials
Current status: Published
02/11/2023
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