Progress 01/01/23 to 12/31/23
Outputs
Target Audience:The target audience for our hyperspectral minirhizotron system, algorithm development, plant physiology and biogeochemical studies are soil and plant scientists/researchers carrying out field experiments and in situ data collection over time. The target audience for our education and outreach activities are graduate students and postdoctoral scientists. Changes/Problems:We have had unexpected turnover in the post-doctoral role supporting the sensor development portions of this effort. As such, the work in this area has been delayed. We will adjust to focus on breaking up components of this to be moved forward by graduate students on the team or individuals we can bring into the team. What opportunities for training and professional development has the project provided?This year, postdoctoral associate Yangyang Song was supported for travel to the tri-societies meeting in St. Louis, Missouri, where had the opportunity to present findings from this study (and his lead-authored in-review manuscript) at the largest annual scientific meeting for crop and soil scientists. This contributed significantly to Dr. Song's professional development in science communication, and the development of his professional network. Students are incorporated into all project planning and communication and being provided opportunities to direct and present work. How have the results been disseminated to communities of interest? An oral presentation by Dr. Song at the 2023 tri-societies meeting in St. Louis, Missouri. A manuscript was submitted to Plant, Cell, & Environment titled "Hyperspectral signals in the soil: plant-soil hydraulic connections as mechanisms of drought tolerance and rapid recovery" which is presently in peer-review. A poster presentation by SJ Chang in the CVPPA workshop at ICCV 2023, which took place in Paris, France. A manuscript titled "HyperPRI: A Dataset of Hyperspectral Images for Underground Plant Root Study" was submitted to Computers and Electronics in Agriculture. It is currently in peer-review. The HyperPRI dataset was posted on Harvard Dataverse and is now freely available online. What do you plan to do during the next reporting period to accomplish the goals? Physiology Physiology experimental objectives have now all been completed in this reporting year, and a manuscript submitted to Plant, Cell, & Environment with postdoctoral researcher Yangyang Song as first author. In the upcoming year, remaining physiology personnel (co-PI Hammond) will provide support and guidance to other project teams in the completion of remaining grant aims. Algorithm and Computational Development of a model for predicting water content from the hyperspectral signature of soil. The Physiology team's collection of soil data in a controlled environment will prove valuable for constructing a regression model. This model will subsequently be transferred from the controlled environment data to enable predictions at the rhizobox level. Modeling Analyze root injection experiments in conjunction with the microbial rhizosphere model, to determine the movement and microbial uptake of root exudates. Modeling analyses will help to create new hypotheses regarding the effects of root exudation on the spatial distribution of microbes in the rhizosphere and its impact on soil organic matter decomposition (priming). Analyze hyperspectral images from the root injection experiment and compare data to carbon content according to distance from the root to determine if carbon signals can be picked up in the wavelengths. Prepare a manuscript detailing the results.?
Impacts
What was accomplished under these goals?
Physiology: This year, the physiology team completed data analysis for the hyperspectral root, shoot, and soil water status aims of the research project. These findings were prepared for peer-reviewed publication and submitted in January 2024 to Plant, Cell, & Environment, a peer-reviewed journal, where it is presently in review. Major highlights of completing the project included developing models for predicting soil water status, root water status, shoot water status, and using hyperspectral imaging of the soil to determine the point of pre-dawn disequilibrium between the plant and soil. All of these models provided an opportunity to identify important wavelengths for inclusion in the mini-rhizotron multispectral camera in development by the sensor construction team. Successful completion of these aims also relied upon the developed HYPER-PRI dataset and models for hyperspectral root segmentation developed by the algorithm and computational team. Modeling Accomplishments The root injection experiment and hyperspectral imaging procedures were completed, resulting in a library of time series' hyperspectral images over the course of the root injection experiment. A soil sampling device was created, and 3D printed to harvest soil samples in the rhizoboxes according to distance to the root. The collected samples were analyzed to determine the movement of 13C. This data was prepared for model implementation. A corresponding predictive model that tracks root exudates and its microbial processing was built and is used to compare against the 13C data samples. Model results show that root exudate products should remain concentrated closer to the root surface than observed. This suggests that commonly used parameters of the microbial model are biased towards high microbial processing efficiency. Algorithm and Computational Accomplishments Three image segmentation algorithms were compared using the HyperPRI dataset: a red-green-blue (RGB) algorithm focused on relationships between neighboring image pixels, a pixel-wise algorithm focused on each pixel's hyperspectral features, and an algorithm that combined pixel neighborhoods and hyperspectral features. The last method demonstrated better overall performance, fewer false positive segmentations, and exemplified an added benefit of using hyperspectral features to supplement current RGB root image segmentation methods.
Publications
- Type:
Journal Articles
Status:
Under Review
Year Published:
2024
Citation:
Yangyang Song, Gerard Sapes, Spencer Chang, Ritesh Chowdhry, Tomas Mejia, Anna Hampton, Shelby Kucharski, TM Sazzad, Yuxuan Zhang, Barry L. Tillman, M�rcio F R Resende Jr, Sanjeev Koppal, Chris Wilson, Stefan Gerber, Alina Zare, and William M. Hammond. "Hyperspectral signals in the soil: plant-soil hydraulic connections as mechanisms of drought tolerance and rapid recovery. Manuscript submitted to Plant, Cell, & Environment for peer review in January 2024.
- Type:
Journal Articles
Status:
Under Review
Year Published:
2024
Citation:
Spencer J. Chang, Ritesh Chowdhry, Yangyang Song, Tomas Mejia, Anna Hampton, Shelby Kucharski, TM Sazzad, Yuxuan Zhang, Sanjeev J. Koppal, Chris H. Wilson, Stefan Gerber, Barry Tillman, Marcio F. R. Resende, William M. Hammond, Alina Zare. HyperPRI: A Dataset of Hyperspectral Images for Underground Plant Root Study . Manuscript submitted to Computers and Electronics in Agriculture for peer review in October 2023. Preprint posted on bioRxiv.
S. J. Chang et al., HyperPRI: A Dataset of Hyperspectral Images for Underground Plant Root Study, bioRxiv, p. 2023.09.29.559614, Oct. 15, 2023. doi: 10.1101/2023.09.29.559614.
- Type:
Conference Papers and Presentations
Status:
Other
Year Published:
2023
Citation:
Spencer J. Chang, Ritesh Chowdhry, Yangyang Song, Tomas Mejia, Anna Hampton, Shelby Kucharski, TM Sazzad, Yuxuan Zhang, Sanjeev J. Koppal, Chris H. Wilson, Stefan Gerber, Barry Tillman, Marcio F. R. Resende, William M. Hammond, Alina Zare. HyperPRI: A Dataset of Hyperspectral Images for Underground Plant Root Study presented as an unpublished poster in the CVPPA workshop at ICCV 2023.
- Type:
Conference Papers and Presentations
Status:
Published
Year Published:
2023
Citation:
Song, Y., Sapes, G., Chang, S.J., Chowdhry, R., Mejia, T., Hampton, A., Kucharski, S., Sazzad, T.S., Zhang, Y., Koppal, S.J. and Zare, A., 2023, October. Plant-Soil Hydraulic Connections As Mechanisms of Drought Tolerance and Rapid Recovery. Presentation at ASA, CSSA, SSSA International Annual Meeting. ASA-CSSA-SSSA.
|
Progress 01/01/22 to 12/31/22
Outputs
Target Audience: The target audience for our hyperspectral minirhizotron system, algorithm development, plant physiology and biogeochemical studies are soil and plant scientists/researchers carrying out field experiments and in situ data collection over time.The target audience for our education and outreach activities are graduate students and postdoctoral scientists. Changes/Problems:
Nothing Reported
What opportunities for training and professional development has the project provided? The students funded on this project were provided with research training through regular interaction with the PIs, participating in bi-weekly project meetings, meetings with their primary advisor, and participation in a lab seminar and journal club. Graduate students also received training on hyperspectral data collection using a hyperspectral lab camera system and imaging chamber. The students were also trained in hyperspectral data calibration and analysis techniques. Upon starting in November of 2021, co-PI's Hammond and Wislon initiated a postdoctoral development plan with postdoctoral research associate Yangyang Song. This included a pre-assessment of Dr. Song's needs for professional development, which are now targets of his postdoctoral development plan for 2022. Additionally, Dr. Song has already learned new techniques in ecophysiology (e.g., Scholander pressure chamber, hydraulic conductivity, optical xylem vulnerability) and spectroscopy, training with instruments to be used in ongoing experiments. How have the results been disseminated to communities of interest?The group has presented one poster presentation on results thus far (see Other Products). We are currently drafting multiple manuscripts and intend to submit them soon. What do you plan to do during the next reporting period to accomplish the goals? A. Physiology During 2023, we will work along with collaborators to refine our spectra model in predicting soil water content and get the full picture of water status and spectra signals of plants and soil during the dehydration process. A manuscript, "Spectra reflectance predicts plant and soil water status during dehydration (temporal title)," will be prepared and submitted. B. Algorithm and Computational 1. Semantic segmentation - We will train a model that incorporates both spatial and spectral HSI data to improve the average precision for segmenting plant roots. We are currently exploring the use of a 3D-UNET. Additionally, we are going to refine the overall segmentation pipeline to facilitate easier analysis of either root or soil pixels when given a plant root image. 2. Water potential problem - The regression model developed for the caps will be applied to the rhizobox dataset to predict water potential/content in those boxes where other measurements such as box weight and leaf spectra were available. The purpose of this analysis is to improve our understanding of plant health at different drought stages. C. Modeling The root injection experimental design created in 2022 will be executed. Data gathered from this experiment will be compared to the diffusive model, and microbial dynamics will be studied. We will also determine whether hyperspectral imaging can be used to analyze soil carbon dynamics. Lastly, a manuscript detailing the proposed model will be prepared.
Impacts
What was accomplished under these goals?
A. Physiology: We have completed the Rhizo-box dehydration experiments of peanut and sweet corn. Physiological parameters, including leaf water potential, leaf relative water content (RWC), leaf chlorophyll fluorescence, leaf photosynthesis, stem water potential, stem hydraulic conductivity, root water potential, soil water potential, and soil RWC have been collected during drying down and re-hydration (15 days for peanut and 31 days for sweet corn). During the experiment, leaf spectra reflectance was collected to link spectra signals to the plant using a spectroradiometer (SVC, 400-2400). A partial least square regression model (R2 = 0.9, RMSE = 5.55) using spectra reflectance to predict leaf water potential has been established. An independent dataset was used to test the established model (R2 = 0.81, RMSE = 13.38). To better link spectra signals to the soil, soil spectral reflectance was collected using a hyperspectral camera (HinaLea, 400-1000nm) during the whole experiment. In addition, a full soil water retention curve was established using both HYPROP and WP4C soil from the METER group to get an accurate relationship between soil water potential and soil water content. Seventy steel caps containing 2 grams of soil and varying water content ranging from 0 (oven-dry) to 1.5 (fully saturated) g were prepared to establish a preliminary spectra reflectance model. These steel caps were imaged using the HinaLea camera and measured by WP4C for water potential. A preliminary partial least square regression model (R2 = 0.83, RMSE = 13.73) was established using the spectra reflectance and water content of steel caps and tested by an independent dataset (R2 = 0.85, RMSE = 10.14). B. ?Sensor Construction and Calibration Accomplishments We have set up a hyperspectral sensor system for use in the minirhizotron (MR). There are two steps to this. The first was hardware design, integration and testing. We have now a 6 wide band hyperspectral sensor with an LED ring. The device is designed to fit inside an MR. As part of this device testing, we collected more than 50 images of the rhizoboxes with plant roots. For each of these images, we captured a corresponding "ground truth" hyperspectral image using the Headwall camera. We also have shown calibration results, by using a stand UNet network to map between the images collected by our low-cost, compact camera and the larger, expensive ground truth camera. We found that we are able to map between these two, enabling the use of our camera in an MR to obtain hyperspectral imagery. In addition to the in-lab results with rhizoboxes, we expect to test in a real MR within the next month. Finally, we have also shown algorithmic results on how to select the right LED bands. We have demonstrated first an algorithm to select the best 10 bands for reconstructing ground truth HSI images of rhizoboxes. Then we showed an adaptive algorithm that selects the best 10 bands per scene, by taking a color (RGB) picture of the scene and then using that to obtain the 10 bands. All these algorithms have been compared favorably with standard baselines methods. C. Modeling AccomplishmentsUsing R's deSolve package we have created a model to track simple diffusion of root exudate carbon through the soil over time in 1D, 2D, and 3D. In order to compare results and understand microbial dynamics, a root injection experiment has been designed to accompany it. In this experiment, 40 rhizoboxes have been outfitted with one artificial root each and will be filled with field soil from the University of Florida's beef research unit. The boxes will be divided into one control group and three experimental groups with each experimental group receiving an artificial root exudate. The substrates being used to mimic root exudation will be glucose, oxalic acid, and glycine. Over the course of eight weeks, each box will receive their substrate mixture once a week in doses where the substrate carbon is equal to 5% of initial soil organic carbon. Each substrate mixture will include its 13C counterpart for diffusion analysis at the end of the experiment. The controls will receive doses of water or water/food coloring. After each injection, the boxes will be imaged with the HinaLea camera for hyperspectral analysis of SOC. D. Algorithm and Computational Accomplishments 1. Semantic segmentation of roots: Using RGB rhizobox images, we have hand-labeled roots in more than 25 peanut root images to enable supervised image segmentation fine-tuning of a UNET and other variants. First, the model was trained on an existing roots dataset (PRMI, ref?). Then, the model's best learned parameters were used for fine-tuning on a portion of the hand-labeled rhizobox images. The best models show an average precision of 0.84 or higher for masking out the root pixels within these RGB images. Further morphological post-processing of these results enables us to achieve a higher precision. This will allow us to carry out future analysis of the root HSI bands without the interference of soil pixel reflectance values. 2. Water potential prediction:The data collected for soil water potential, as described in the Physiology section above, involved the collection of seventy steel caps and corresponding water potential measurements. These data were used to analyze the signature of different water potential levels in the soil. The ultimate goal was to construct a regression model capable of predicting water potential from hyperspectral imagery of soil.
Publications
|
Progress 01/01/21 to 12/31/21
Outputs
Target Audience:?The target audience for our hyperspectral minirhizotron system, algorithm development, plant physiology and biogeochemical studies are soil and plant scientists/researchers carrying out field experiments and in situ data collection over time. The target audience for our education and outreach activities are graduate students and postdoctoral scientists. Changes/Problems:One of our original Co-PIs on the team (Diane Rowland) transitioned from a faculty position at UF to an administrative role (specifically, Dean) at the University of Maine. To address this, we identified an alternative Co-PI (William Hammond)to take over Prof. Rowlands tasks and aims in the effort. During this reporting year, we obtained the necessary permissions and modifications to enact this change of Co-PI. What opportunities for training and professional development has the project provided?The students funded on this project were provided with research training through regular interaction with the PIs, participating in bi-weekly project meetings, weekly meetings with their primary advisor, and participation in a weekly lab seminar and journal club. Graduate students also received training on hyperspectral data collection using a hyperspectral lab camera system and imaging chamber. The students were also trained in hyperspectral data calibration and analysis techniques. Upon starting in November of 2021, co-PI's Hammond and Wislon initiated a postdoctoral development plan with postdoctoral research associate Yangyang Song. This included a pre-assessment of Dr. Song's needs for professional development, which are now targets of his postdoctoral development plan for 2022. Additionally, Dr. Song has already learned new techniques in ecophysiology (e.g., Scholander pressure chamber, hydraulic conductivity, optical xylem vulnerability) and spectroscopy, training with instruments to be used in ongoing experiments. How have the results been disseminated to communities of interest?
Nothing Reported
What do you plan to do during the next reporting period to accomplish the goals?Physiology: During 2022, a dry-down experiment will be conducted with rhizoboxes, during which high-frequency hyperspectral imaging is paired with measurements of soil properties and plant physiology. Additionally, canopy reflectance will be measured with a spectroradiometer. Together, this time series of measurements (from a wet to dry state), paired with the data collected in 2021 of plant growth from germination to fully-grown-in rhizoboxes, will provide a rich dataset for algorithm selection and development to link soil process and plant processes with spectra, to identify the most important bands to include in the multi-spectral minirhizotron camera. Sensor development: We will expand the device to add color filters in the optical path. The PCB board is being reworked via Altium to hold more LEDs and possibly implement surface mounted components which would fit better in the compact minirhizotron tube. Currently working on camera calibration and possibly changing to another camera which would require more hardware to operate but would give much more control to the user. We will develop and jointly train a second network that selects for appropriate wavelengths for reconstruction. Communicate with the other team members to change the network towards prediction for tasks deemed appropriate for rhizosphere understanding. Modeling: We will continue to refine our analytical model, and study it numerically to derive hypotheses that can link to exudation and to a time series of spatiotemporal hyperspectral data. This model will be implemented within a Bayesian data assimilation framework to iterate with the outputs of the physiological and sensor development portions of this project. Automated Analysis: We will continue to adapt and extend existing hyperspectral analysis libraries to better address rhizosphere data analysis and incorporate what is learned from modeling, sensor development, and physiology components. We will develop approaches for analysis that leverage prior knowledge from experiments being run to better constrain the solution space.
Impacts
What was accomplished under these goals?
Physiology Accomplishments: Progress toward determining the bands that may be most informative to include in the final camera was made during preliminary measurements and the setup of a dry-down experiment to be conducted in early 2022. An existing hyperspectral camera (HinaLea, 400-1000nm) was used to image plants grown in rhizoboxes, using the same acrylic material that will be present between the minirhizotron camera and soil/roots. Plants of varied life strategies (annual, perennial) and growth forms (herbaceous, woody) were imaged in rhizoboxes after germination (wet soil), and were then repeatedly imaged in time series as roots developed. To link hyperspectral signals in the soil to meaningful measures of plant physiology, several methods have been developed and project personnel trained to acquire these data coincident with the images being taken (as described in prior paragraph). Plant water content measurements were tested, along with determining the proper rehydration time, and postdoctoral research associate Yangyang Song was trained in these methods, along with measurements of plant water potential using a Scholander pressure chamber. Additionally, above-ground spectral reflectance measurements were taken using a spectroradiometer, another new skill for Dr. Song. These reflectance data, combined with the below-ground imaging of rhizoboxes, will allow us to investigate the relationship between signals above and below the soil. To link soil-specific qualities to the hyperspectral measurements of rhizoboxes, Dr. Song conducted a soil moisture release curve using a dewpoint potentiometer. To infer soil water potential, Dr. Song also developed a protocol for weighing boxes during imaging such that the water content of each imaged box can be used to estimate that plant's soil water potential. In upcoming drought experiments, this will provide the opportunity to investigate how soil moisture changes over space and time, relative to the location of old and newly growing plant roots. Sensor Construction and Calibration Accomplishments: Our goal is to create a compact camera that can fit inside a minirhizotron. The camera will have set of M LEDs set as rings, as well as a set of N color filters inside the optical assembly, and the intersection M*N will be the number of wavelengths measured. This year we built the full system camera without the N color filters, which will be next year's work. We used an extremely compact third-party camera capable of both RGB and infrared photos and videos. A temporary LED system was built to sequentially flash LEDs while the camera took a short video of a color chart. Using MATLAB, the color chart data was broken down to show a clear increase in reflectance when the LED color matched the color chart square. Preliminary testing of the LED system was done on two cotton plants with different soil states: watered and dry. The watered soil results produced a clear reflectance spike for green LEDs on the root, which is in line with previous experiments. To make sure the camera could fit into an MR system, we modeled the entire mechanics in Solidworks and developed an attachable rig to fit onto the height-adjustable metal rod. The rig is designed to hold a 9V battery, an Arduino nano microcontroller, the aforementioned small third-party camera, and a ring-shaped PCB board housing multiple through-hole LEDs of specific wavelengths. Multiple rig iterations were then printed with PLA plastic and slightly adjusted for better fit. Modeling Accomplishments: We developed a simple modeling framework that describes a simple microbial dynamics often used to describe soil organic matter breakdown, in combination with dissolved organic carbon transport. The model has been simplified to obtain closed-form solutions for boundary conditions where the root surface releases exudates suitable for microbial uptake and priming of microbes in the surrounding soil. These analytical solutions show that dissolved organic matter patterns, as well as soil carbon depolymerization, have a function of characteristic distance formed by the ratio of diffusion in soil solution and microbial carbon turnover. While the assumptions in the simple analytical model need to be tested, the framework can help to test hypotheses regarding the role of root exudates on soil carbon turnover, for which the imaging process could provide crucial data points. Algorithm and Computational Accomplishments: During this reporting period, we began development of automated analysis of collected hyperspectral imagery from both laboratory hyperspectral data as well as future sensor data. Analysis algorithms being developed include spectral unmixing approaches (algorithms which decompose hyperspectral signatures into constituent materials (i.e., "endmembers") and the percentage of each pixel associated with material, dimensionality reduction approaches via autoencoders, and automated segmentation into regions of interest. For all of these analysis approaches, we are beginning with the application of existing approaches to collected hyperspectral imagery and existing hyperspectral imagery from other studies (e.g, aerially-collected hyperspectral over peanut and cotton fields). Existing approaches that have been implemented and are being evaluated (and, eventually extended) include the Sparsity Promoting Iterated Constrained Endmembers (SPICE) methods for unmixing, U-Nets deep learning architectures for spatially-driven image segmentation, and standard feed-forward autoencoders for dimensionality reduction.
Publications
|
|