Source: UNIVERSITY OF FLORIDA submitted to
SITS: HYPERSPECTRAL SIGNALS IN THE SOIL
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
NEW
Funding Source
Reporting Frequency
Annual
Accession No.
1024671
Grant No.
2021-67021-33445
Project No.
FLA-AGR-006015
Proposal No.
2020-08977
Multistate No.
(N/A)
Program Code
A1401
Project Start Date
Jan 1, 2021
Project End Date
Dec 31, 2023
Grant Year
2021
Project Director
Zare, A.
Recipient Organization
UNIVERSITY OF FLORIDA
G022 MCCARTY HALL
GAINESVILLE,FL 32611
Performing Department
(N/A)
Non Technical Summary
Studying plant roots, the soil surrounding roots, and their interaction in realistic environments over time is challenging. One of the most successful methods to do so is using minirhizotron tubes. These are clear plastic tubes inserted into the ground. Then, cameras placed in the tube can take photographs of the soil and roots that happen to grow alongside the tube over time. Minirhizotron tubes have been very successful in scientific studies to understand temporal root properties. However, they are expensive, tedious to use, and only collect standard color imagery. In this effort, we will build hyperspectral cameras (which also collect information outside of the visible range collected by standard color cameras) that will be inexpensive, have an automated mechanism to move up and down the tubes, and are paired with automated algorithms to process and understand the collected data. The project will open doors for understanding plant roots and their interaction with the soil surrounding them beyond our capabilities today.
Animal Health Component
0%
Research Effort Categories
Basic
40%
Applied
10%
Developmental
50%
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
1020110100025%
1021459102025%
1020199208025%
1027299202025%
Goals / Objectives
Minirhizotron technology is widely used for studying the development of roots. Such systems collect color imagery of plant roots in-situ using an imaging system within a clear tube inserted into the soil. They have been shown to be very effective at tracking root characteristics, architecture, and growth over time. However, these imaging systems have several significant limitations. First, minirhizotron systems are extremely costly. Each imaging system is generally on the order of tens of thousands of dollars which, in practice, restricts the number of systems that can be deployed by a team. Second, the imaging systems are tedious to deploy. Generally, operators need to manually lower the imager during the imaging process further restricting the throughput of the system. Third, a significant bottleneck of current systems is the post-processing of collected imagery. Each image is manually analyzed and traced to identify and characterize each root, an extremely tedious and time consuming process. Finally, current minirhizotron systems focus primarily on root analysis and monitoring with little regard to the soil. In our effort, we will tackle some of these most critical challenges by developing a low-cost, easy-to-operate hyperspectral imaging system and associated automated analysis algorithms for use with minirhizotron tubes. The minirhizotron hyperspectral imaging systems will provide root and soil characterization beyond what is possible with the current color imagery being collected. Specifically we will focus on characterizing soil moisture, texture, and soil organic matter to allow us to eventually model critical biogeochemical processes. We have assembled a transdisciplinary team with experts on image analysis and computer vision, sensor design, model and data fusion, machine learning, plant physiology, and soil science. In addition to developing an improved minirhizotron imaging system, we will answer fundamental questions about the interaction between root and soil properties.By developing a hyperspectral imaging system for minirhizotron tubes, we will enable the collection of richer data for root and soil analysis over time. Hyperspectral image analysis has been shown to be effective at characterizing soil nitrogen, carbon, carbonate, and organic matter as well as effective at monitoring root decay and providing the potential for physio-chemical root analysis. Providing the ability to perform this sort of analysis over time in field settings would have revolutionary impact in the study of plant roots, soil, and their interactions. The potential for monitoring root and soil properties over time in-situ can open possibilities for significant advances in biogeochemical modeling and understanding relationships between root architecture, soil properties and root function. Furthermore, the algorithm development efforts on this project will enable automated joint spatial-spectral analysis of root and soil interactions.This effort will enable a collection of broader impacts that will expand the accessibility and utility of minirhizotron imaging systems. We will develop and freely share software pipelines for processing and analyzing minirhizotron color and hyperspectral imagery. Further, because of our team's unique approach to achieving full integration of disciplines through co-development of research questions, approaches, data analysis, and interpretation, we will provide a unique opportunity for graduate student and postdoctoral training of convergent research. Lastly, our novel professional development programs, including the A2i, symposium and A2i+ workshops, we will provide all graduate students and postdoctoral associates mentorship from relevant industry professionals. Ultimately, we will provide intensive project-based transdisciplinary, convergent training for graduate students in computer science and engineering, plant physiology, and soil science.
Project Methods
A. Sensor Development Research PlanTask A.1: Characterize ability of low cost, broadband filters in approximating spectral signatures:In this task, we will create a database of broadband filters from Roscolux filter bankand a small set of target soil/root tasks that already have relevant signatures.We will use non-negative optimization to find coefficients to minimize a linear combination of filters using sparse coefficients.Task A.2: Model the ray geometry of 3D printed pinhole imagery through the MR tube material and propose a calibration algorithm: This take will consist of selecting a web-camera that fits in the MR tube and collecting data through the side of the tube onto an LCD display. We will collect de Bruijn patterns mapping the flat, display plane to the pixels on the device. Then, we will test the above with pinholes printed onto the web camera of different sizes (3x3 and 4x4) and using two kinds of optics: (1) basic pinhole; (2) pinhole immersed in refractive slab (for wide-angle viewing).Task A.3: Build a prototype device with a LED ring and apply multi-view geometry to align the imagery to obtain hyperspectral signatures: In this task, we will select an LED signature that is broadband in our targeted reflected radiances. Then, we will attach an LED ring to the web-camera with 3D printed setup. Integrate color-filters onto the device. Using this, we will collect data of a variety of scenes and create a multi-view algorithm to map between different cameras. One version will be to use a geometric-based approach by sliding the camera along a rail in the tube, collecting epipolar plane images (EPIs) that allow for line detections (with Hough transforms) to recover depths from the imagery. Another strategy is to use a learning-based approach by collecting data from the sensor through the tube siding onto scenes that are also captured in the lab using a Kinect. A mapping learned between the imagery and the depth map can be obtained, and used to align the array.Task A.4: Construct robotic system for moving camera through the tube: We will actuate the novel sensor described above using an automatic image capture system. The final goal is to create a setup which can image and seamlessly stitch the imagery into a "panorama-minirhizotron" image that can allow for better user viewing, easier data collection, and also can form a geometrically-salient visual context for the machine learning algorithms to process.B Research Plan for Laboratory, Mesocosm and Field StudiesTask B.1: The Rowland lab has established a rhizobox imaging system (Racette et al., 2019) which enables digital and HSI of roots and associated soils through one side constructed of clear plastic. Seeds are planted within boxes, inclined at 30 degrees and scanned or imaged using cameras through the plastic. Variations of the rhizobox construction have allowed for separation of soil sections in thirds: a top section roughly 12 cm in depth, and two side by side chambers of approximately 7x5 cm. These chambers allow the placement and maintenance of soil at various predefined water contents, SOM, texture, and bulk density. Lab analyses will be conducted testing different soil water contents, textures, SOM, and compaction in the rhizoboxes with peanut (Arachis hypogaea L.) and sesame (Sesamum indicum L.) seedlings. Rhizoboxes will be imaged daily using the lab-based hyperspectral camera.Hyperspectral signatures that are indicative of the manipulated soil properties will be identified.Because the clear plastic front of the rhizoboxes are removable, images can be taken with and without the cover to determine the impact of imaging through this plastic. This information can be used to aid in algorithm development to understand the impact of the plastic on HSI measurements and analysis.Task B.2: Evaluate Soil Water and Root Differences with Hyperspectral Sensor at Mesocosm Scale. MR ground-truthing data will be the ultimate and essential test of the sensor's capabilities. We propose testing in a variety of soil types, conditions, crops, and management treatments. Further, these studies can quantify root water uptake when coupling MR imaging with a separate measurement using a Time Domain Reflectrometry (TDR) sensor that quantifies soil water. Studies will be conducted at both locations using the traditional MR imaging systems and the developed hyperspectral sensor. Visible images will be analyzed for total root length, surface area, root volume, and root diameter using the techniques established in the Zare and Rowland labs. Groundtruth data on soil properties will be collected including soil water, texture, and SOM.Task B.3: Field Scale Studies: Experiments conducted in peanut involving full irrigation (meeting crop demand) and partial irrigation using two cultivars will be conducted under rainout shelters at the Plant Science Research and Education Unit (PSREU) in Citra, FL. The experimental design will be conducted as a Randomized Complete Block (RCB) with four replications and a split plot restriction on randomization, where levels of irrigation (full and partial) are randomized to main plots and levels of cultivar (2) to subplots.C Algorithm Development Research PlanTask C.1: Guided Unmixing Development: In this task, we will study and develop approaches to determine hyperparameter settings and priors based on laboratory, mesocosm and field studies.Task C.2: Leverage Spatial Information during Unmixing: Many methods in the unmixing literature have been developed to leverage spatial information during hyperspectral analysis. Furthermore, spatial information has been leveraged during LDA usage. Of particular note are those that extend LDA to incorporate spatial information and structure (Wang and Grimson, 2008; Cao and FeiFei, 2008). However, many of these approaches are computationally intensive (i.e., methods relying on Markov Random Field models).Task C.3: Supervised ML Mapping Approaches: In parallel with unmixing approaches, neural networks (NN) and support vector machines will be trained to map hyperspectral data to measured root and soil properties.Task C.4: Evaluation of Approaches: Both the unmixing and supervised mapping methods will be evaluated against a suite of standard hyperspectral analysis methods previously published in the literature including linear and non-linear spectral unmixing approaches. These methods will be evaluated by their ability to estimate soil moisture, texture and SOM under cross-validation.D Biogeochemical Modeling Research PlanTask D.1: Formulation of Microscale Models: In order to link root traits to SOM, we will utilize HSI to separate out deposition of litter from exudation (inferred from presence of root tips, root hairs and possibly novel spectral features). We will parameterize and compare both a microbially-explicit model based on the formulation in Georgiou et al. (2017) and a simplified first-order model (based on CENTURY). We will combine model development and simulation with data-model integration using Bayesian Hierarchical Models (BHM) to leverage the unique advantages of the data and experimentation. We will perform posterior predictive checking - including leave-one-out cross-validation - using soil carbon concentration changes as our predictive quantity of interest, while treating microbial pools as latent variables.Task D.2: From Micro to Macroscale: Aggregation over microscale models will be studied analytically where possible, as well as through simulation experiments. The mathematical machinery we develop will be incorporated into our BHM fitting of the PBMs. Given that each rhizotron will provide data on 100 spatial locations during each measurement, we anticipate having rather large datasets with which to constrain these processes.