Source: UNIVERSITY OF MISSOURI submitted to
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
Funding Source
Reporting Frequency
Accession No.
Grant No.
Project No.
Proposal No.
Multistate No.
Program Code
Project Start Date
Mar 1, 2015
Project End Date
Feb 28, 2018
Grant Year
Project Director
Chen, Z.
Recipient Organization
KANSAS CITY,MO 64110-2446
Performing Department
Non Technical Summary
The primary objective of this Seed Grant project is to develop a soil-hydrophobicity (or soil repellency) analytics system with rapid and cost-effective hyperspectral imaging and computing capabilities, which ultimately aids practitioners for improved and rational decision-making in using agricultural water. The project is first motivated by the exacerbating global weather changes that have cast significant uncertainties upon water supplies in agriculture. It further addresses excessive water runoff that affects irrigation efficiency in arid or semi-arid areas. One key parameter that affects water use and efficiency in agricultural fields is soil hydrophobicity. Upon completing this project through two-year US-Israel collaboration, we aim to demonstrate that soil hydrophobicity can be sensed through integrated hyperspectral sensing and mathematical modeling. One primary outcome is a novel hydrophobicity sensing and computing prototype system for use in generic small-scale UAVs. The integrated efforts in this project will build our strong competitiveness in the long term towards establishing research, education and outreach excellence in soil-hydrophobicity sensing and computing at a global setting.The proposed research is in the scope of the FY2014 Program Focus Area of Water for Agriculture Challenge Area, and directly addresses the Program Area Priority of Sustaining water quantity, quality, and availability for agricultural use while maintaining environmental quality through 2050.
Animal Health Component
Research Effort Categories

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
Goals / Objectives
The long-term goal of this SEED project is to establish a research, education and extension center of excellence in soil-water sensing and computing, which is dedicated to enhancing rational decision-making for agricultural water use at a global setting. The direct research goal of this project is to develop a soil-hydrophobicity analytics system with rapid and cost-effective hyperspectral imaging and computing capabilities. To achieve this, the emerging micro-unmanned aerial vehicles (micro-UAV or MAV) will be used as the basic platform.To realize this goal, the measurable research and project objectives at the end of this project include:1) To demonstrate that soil hydrophobicity can be sensed and identified through hyperspectral imaging and hyperspectral data mining;2) To develop a novel MAV-based soil-hydrophobicity analytics system with both laboratory and field verification;3) To build an internationally collaborative research and education infrastructure focusing on soil-hydrophobicity sensing and computing;4) To develop a comprehensive an integrated project proposal (e.g. the CAP grant) with the long-term and sustainable goal of innovating tools for rational soil-water management for agriculture and other applications.
Project Methods
The core research methods of this project have two components:Hyperspectral data mining: novel hyperspectral image processing and data mining methods that aim to identify and model soil hydrophobicity signatures will be researched and developed in the form of reusable software packages. The hyperspectral image data mining engine (PARCUDA ) developed by co-PI Ben-Dor will be optimized possibly through integrating efficient dimensionality reduction methods, which enables extraction of the best model to describe soil-hydrophobicity solely from hyperspectral imagery data, namely PARCUDA-SH in this project.Digital integration of imaging and computing: systematic integration of imaging, computing and UAV hardware will be carried out.To adopt these methods, three main research tasks (research efforts)are designed: Laboratory and Field-Based Experimentation.Hyperspectral Data Mining.Digital Integration of Imaging and Computing.Two additionalefforts are designed to realize the knowledge dissemination and education objectives:Interdisciplinary training of graduate students through international collaboration. Six (two in US and four in Israel) graduate students will be trained in the interdisciplinary areas of remote sensing, data mining, and soil sciences during the life cycle of the above Tasks.Final report and knowledge distribution. Scientific and applied research papers, reports/book chapters, and conference presentations will be produced for knowledge distribution.The last effort is a comprehensive verification plan that is laid out towards ultimately proving the effectiveness of the proposed hyperspectral imaging and computing methods and the developed platform prototype. The main contents include the following steps. At UMKC, the UAV prototype system will be deployed and verified in mock agricultural fields on campus (with and without vegetation). The onboard and off-board computing will be strictly verified for accuracy and robustness; the MAV-based imaging and computing platform built at UMKC will be shipped to Israel, during which the UMKC team will visit the TAU-HUJI teams. The TAU-HUJI teams will set up a ground system and deploy the airborne HRS system at a select agricultural site. TAU will acquire data, pre-processing them, and generating a hydrophobicity map of the study area, which will be then verified by HUJI. The Project Teams will oversee the systematic field experiment and system performance verification.

Progress 03/01/15 to 02/29/16

Target Audience: The direct research goal of this project is to develop a small UAV based soil-hydrophobicity analytics system with rapid and cost-effective hyperspectral imaging and computing capabilities. While realizing this goal, the research team (the UMKC team led by PD Dr. ZhiQiang Chen; and the Israel Team led by PDs Dr. Eyal Ben-Dor and Dr. Rony Wallach) have made many attempts to reach out different categories of audiences, which have greatly increased the visibility and potential impact of the research project. Due to the SEED grant nature of this proposal (i.e. focusing on developing a technical capability), the target audience is not geared towards the general public. Rather, we have mostly aimed at increasing our visibility in the research/education and the industry communities. For the UMKC team, the following activities are summarized: 1. Target Audience: University of Missouri System's faculty, staff, and students. Format: news media coverage Upon the project's kickoff, UMKC's media department interviewed Dr. Chen and published a news article ( The coverage introduced the background, the research goals, expected outcomes, and especially the international collaborative nature of the project. Potential impact: increased the visibility of the SEED research, and awareness on all UM campuses (besides UMKC, it has three campuses: University of Missouri, Columbia and its Extension; Missouri University of Science and Technology; University of Missouri, Saint Louis). 2. Target Audience: University of Missouri Systems Researchers and Collaborators. PD Dr. Chen has received numerous inquiries and collaboration invitation from UM's colleagues and beyond. Several initiatives have started. Format: emails, telephone communication, field/lab visits, and in-person meetings These include: Secured an internal research grant with Dr. Xiaoming He at Missouri University of Science and Technology in mathematical modeling of soil-water flow considering water repellency, which aims to develop a novel mechanics-based modeling approach. This project will complement the Collaborative Project in developing a forward (predictive) modeling approach. Initiated and discussed with Dr. Steve Anderson at University of Missouri for potential research collaboration in X-ray reconstruction of soil structure for potential mechanics-based modeling Working with Dr. Wei Zhang at Michigan State University on a NASA research proposal, which will further develop the proposed UAV-based hyperspectral imaging platform (to be submitted in Jun 15). Invited by Professor John Walker to develop a subcontract proposal to an ongoing NSF's support program in Missouri (Missouri Transect,; to be submitted in Jun 30. Initiated and discussed with Dr. Xi Xiong at University of Missouri for potential research and proposal collaboration in turf grass management, particularly on sandy soil hydrophobicity assessment and mitigation. Contacted researchers at Missouri and USDA Extension for potential research collaboration and for the flight campaign in Missouri. Key personnel includes: Dr. Kenneth Sudduth, Agricultural Engineer, USDA Agricultural Research Service; Adjunct Professor of Bioengineering, University of Missouri; President, International Society of Precision Agriculture Dr. John Sadler, USDA-ARS Cropping Systems & Water Quality Research Unit; University of Missouri 3. Target Audience: hyperspectral imaging vendors and manufacturers. Format: telephone and email communication The equipment acquisition turned out to be the most critical and time-consuming component of this project. During the course of the process, the PD Dr. Chen has contacted and introduced our project to following international and domestic vendors/manufacturers. Email communication with sales representative, technical discussion, and negotiation have been intensive with all the following units. For the finalist (Cubert and Rikola), serious image-based analysis was conducted for evaluating their cameras and appropriateness for UAV use. The list of vendor/manufacture names with significant/technical discussion includes: Headwall Photonics Inc. (, USA Resonon (, USA Bayspec Inc. (, USA Specim Spectral Imaging (, Finland Cubert-GmbH (, Germany Rikola Ltd Oy (, Finland Gaiasky (, China 4. Target Audience: students at UMKC. Through the first year of the project, four graduate and undergraduate students were directly involved in the project. Many other (more than 8) students were indirectly involved and informed about the significance of the project through student recruiting and interviewing process. Format: laboratory instruction. The students who are partially supported by this project include: Mr. Jianfei Chen (completed). Doctoral student; and in charge of development of an aerial imaging and ground sensing system for networked agriculture field application A UMKC's Technology disclosure was filed based on this work Ms. Shimin Tang. Doctoral student; and is working on development of high-performance hyperspectral image analysis methods for hydrophobicity detection in soil and other precision agriculture applications Mr. Caleb Chase. Undergraduate student; development of a UAV-based robotic arm for soil sampling and sensor deployment Mr. Bryce Coulter. Undergraduate student; preliminary research on developing artificial hydrophobic soil using an industrial agent (ZycoBond from Zydex). Changes/Problems: The major changes include: (1) Due to the market maturity of small form-factor hyperspectral imagers for UAV use, and after careful studies, comparison and discussion with numerous vendors/manufactures, we have decided to use a snapshot hyperspectral imager instead of a push-broom type. This has delayed our acquisition process. (2) New research in development of an aerial-ground imaging network. After carefully considering the future of small UAV (drones) imaging for agriculture application and resilient response to climate-change related hazards, we believe that an internet of things concept should be adopted, where the rapid and flexible UAV imaging should be simultaneously companied by ground sensing that provides ground-truth data for point-based and partial evidence. (3) We have decided to develop an opensource UAV hyperspectral imaging toolbox besides the use of the PARACUDA system that is registered in Israel only and in the near future not available to users in the US. (4) After careful discussion with the Israel team and researchers in Missouri, we have not found water repellant soil in Missouri's agricultural fields and research plots. More attempts are still being made, and a possibility is to obtain water repellant turf grass soil samples. In the meantime, if not successful, we will create and use artificial water-repellant soil based on a protocol we are developing with an industrial partner. What opportunities for training and professional development has the project provided?Through the first year of the project, four graduate (in Electrical Engineering) and undergraduate students (one in Computer Science and one in Civil Engineering) were directly involved in the project. Many other (more than 10) students were indirectly involved yet informed about the significance of the project through student recruiting and interviewing process. How have the results been disseminated to communities of interest?Due to that we have partially completed the research goals and tasks, we are working and will focus on disseminating results of the project during the second year of the project. The format and types include journal publications, conference presentations, workshop attendance, and field trips and visit stakeholders. What do you plan to do during the next reporting period to accomplish the goals?The major goals in concert with the Project's objectives in the 2nd year include: Final acquisition of the hyperspectral imager and develop initial control and calibration interface, and integration with a small drone Flight campaign in the US and data analysis Israel team continue research in final tuning of the most probable range for soil hydrophobicity detection in the VNIR and SWIR range, and expect to achieve detection in the VNIR range Continue development of the hyperspectral image analysis toolbox for realizing rapid soil parameter mapping and analysis Publication of results in journals and conference venues, and potentially organize a technical workshop/field flight campaign with attendees from University of Missouri Extension, faculty, students, and other stakeholders.

What was accomplished under these goals? We have partially completed the goals listed above. Specifically, resolving around these project goals, the following achievement have achieved: 1. Demonstration of that soil hydrophobicity can be sensed and identified. Led by Prof. Ben-Dor and Prof. Wallach, we characterized the reflectance spectra of soils under the effects of hydrophobicity and identified features correlated to different water repellency levels. To create the database, soil was sampled from the orchard plots in Sitria, an area of sandy soil in Israel. Soil in this area is classified as having high potential to develop water-repellency characteristics. Undisturbed soil samples were taken from several plots of different types. To obtain various levels of organic matter, samples were taken at various distances from the tree line. Using the PARACUDA system for the soil samples in the first year, we obtained good feasibility of hydrophobicity prediction. The best model was obtained by separating the data into two groups based on the repellency values and providing R2 = 92 (Figure 2) for the validation group. In doing so, the project team demonstrated that soil hydrophobicity can be sensed and identified through visible-near infrared-shortwave infrared hyperspectral spectroscopy (namely, wavelengths ranging from 400 nm to 1400 nm, VNIR; and 1400 nm to 2500 nm, SWIR, respectively). These results confirm the basic hypothesis of this project and show that we are on the way to implementing the spectral information that will be obtained remotely from the MAVs. 2. Development of a novel MAV-based soil-hydrophobicity analytics system For this objective, the research activities primarily led by Dr. Chen at UMKC were conducted. 2.1 Hyperspectral imaging specification and imager acquisition For the acquisition of a proper hyperspectral camera, a great deal of communication and discussion had been engaged. This has generated a large database of specifications, data sheets, and sample hyperspectral images for nearly all hyperspectral imagers in the market that claimed for use in small UAVs (e.g. quad- and octo-copters). In the US market, the hyperspectral cameras are primarily push-broom. Therefore, the project team started with this type and spent much time in two major products in the US: the Headwall's MicroSpec system; and the Resonan's Pika system. Spectral ranges and resolution were the primary parameters during the discussion. Based on the spectral analysis, the spectral ranges were initially determined broadly, including VNIR and SWIR (see Section 1). However, regardless of Headwall or Resonan's camera systems, neither provides one imaging system that covers both VNIR and SWIR. Furthermore, the project teams realized that a more critical limitation of push-broom cameras is their stability in obtaining usable and high-quality images using a UAV platform, particularly, considering the goal of achieving a highly low-cost and rapidly deployment imaging system. For any push-broom camera, photogrammetric processing becomes intolerable when using a light-weight UAV, which can hover, flight abruptly, and cover regions without a high-accuracy GPS route. This limitation finally forced the team consider a 'snapshot' camera, which can provide hyperspectral data (in terms of hyperspectral cubes) in the flight. In this sense, the photogrammetric processing will be basic and minimal, which includes only atmospheric calibration. Geometric processing for snapshot hyperspectral cubes taken at different geospatial locations and perspectives can further provide 3D geometric models, which eventually lead to high-fidelity hyperspectral-geospatial data. It is noted that this possibility will open a new and unique direction for remote sensing-based precision agriculture application at small scales (compared with satellite or airborne remote sensing). Finally, the project team has finally chosen the Cubert 185-Firefly camera for the potential UAV payload. The PD is working with UMKC's relevant departments for the bidding process. It is hoped that this camera will be acquired during the earlier months of the 2nd year project year. 2.2 Development of an energy efficient aerial-ground sensing network for resilient agricultural soil-water environmental sensing Developed by the graduate student Mr. Jianfei Chen and the PD, the basic concept and proposed innovative scheme are illustrated as follows. A low-attitude and small unmanned aerial vehicle (UAV) with basic imaging and computing capability is further programmed and equipped with designed and programmed circuits, which then acts as an aerial sensor-control unit, communication gateway, and data hub for a ground-based wireless sensing network. The ground sensing nodes are programmed in mostly sleep modes and to perform sensing tasks at scheduled times with short durations. When deployed to fly, the UAV can capture and transmit ground scene images to the operator, who makes tactic decision to control the aerial path of the UAV. The UAV can transduce coded radio-frequency (RF) signals that are used to power and wake up targeted ground sensors. The RF receiving circuits do not consume any power in the sensor nodes. The waked sensor nodes then activate the wireless transmission modules and transmit delayed sensor data to the UAV. Based on the above innovation, a UMKC technology disclosure has been filed (UMKC Tech Disclosure #16UMK017), and some component of this innovation is being considered for filing a US patent. 2.3 Development of a UAV-based hyperspectral image analysis toolbox and workflow During the first year of the project, the imager acquisition has been significantly delayed due to the market maturity of this technology that has demanded extensive communication and associated data analysis and comparison, therefore we are not able to process real data from the desired snapshot imager for the UAV platform. Following the research objective laid out and with discussion with our Israel partners, the PD and his student (Ms. Shimin Tang) have embarked on developing hyperspectral imaging and analysis tools customized for small UAV use. To this date, we have developed a mathematical hyperspectral imaging noise model for new sensors (e.g. the snapshot type this project considered). More modules for completing the goal of enhanced hyperspectral analysis besides using the PARACUDA system will be focused during the 2nd year of the project. We expect that this toolbox will be opensource and freely to use by the research community. 3. Internationally collaborative research and education infrastructure During the past year in carrying the project, the US-Israel teams have been truly together in conducting the research and educating the students. The major collaboration includes: Soil sampling, spectral analysis, and development of imaging equipment. The soils were sampled using a protocol developed at the Hebrew University of Jerusalem (HUJI) and taken to the laboratory. The full dataset of the soil spectra and hydrophobicity values were analyzed by the Tel Aviv University (TAU) team to generate a spectral model. The spectral analysis was performed across different spectral regions to help the University of Missouri-Kansas City (UMKC) team characterize the required parameters for the hyperspectral sensor that will be operated in the second year. Education and collaboration. The student researcher groups at two countries (led by Mr. Amihai Granot at TAU; and led by Mr. Jianfei Chen first, now Ms. Shimin Tang) commutated often and established unique working relationship. 4. Proposal Development We also expect that during the 2nd year and beyond, the US-Israel team and infrastructure will grow prosperously in terms of proposal development, research collaboration, and other scholarly development.