Recipient Organization
NORTH CAROLINA STATE UNIV
(N/A)
RALEIGH,NC 27695
Performing Department
Biological & Agr Engineering
Non Technical Summary
Today, the development of new technologies and sensors to automate the collection of data across a wide range of agricultural and biological processes is widespread. The potential for integrating robotics, automated sensing systems, and computational tools in agricultural and biological applications is increasing as costs reduce, capabilities are enhanced, and barriers are overcome. The rapid and continuous integration of these technologies and tools into the entire agricultural value chain has led to what is now known as the "digital agriculture revolution", which is currently ongoing. Though exciting and groundbreaking, these new technological advances bring new challenges. In particular, what has been lacking is a wholistic approach to developing new sensor systems that incorporates technical innovation, engineering approaches, data analytics, and human factors to develop valuable technologies that are scientifically robust yet maintain usability. Moving forward, there is a need to integrate deep technical knowledge about these new technologies with domain knowledge about the application system so their full utility can be achieved.This project will bridge the fields of robotics, automation, and sensing (various combinations of computer science, electrical engineering, and mechanical engineering) with biological and agricultural systems analysis to generate case studies that demonstrate how researchers and practitioners in this field can extract useful information and solve problems using technology. Systems to be utilized in this project include small unmanned systems (ground, aerial, underwater, and surface systems), image and spectral sensors (hyperspectral, multispectral, RGB), human-technology interaction technologies (robot interfaces), and sensors and control systems (environmental sensors). Example application systems include water resources, plant breeding and phenotyping, production agriculture, aquaculture systems, among others. This project will involve many interdisciplinary applications, primarily of those who have significant domain expertise of the application system. Through these collaborations, a community of researchers, stakeholders, students, and practitioners will become more aware of the benefits of integrating automated sensing systems into current practices and how value can be generated in the form of new data, insights, cost savings, reduced environmental impact, among many others.
Animal Health Component
50%
Research Effort Categories
Basic
50%
Applied
50%
Developmental
0%
Goals / Objectives
The primary goal of this project is to advance agricultural and biological sensing systems by integrating expert-level technical knowledge of robotics and controls with domain knowledge about the application and/or research problem. To meet this goal, new sensing and robotic systems will be developed for solving well-defined biological and agricultural problems (estimated between 5-10 different problems or opportunities) in order to present a series of case studies that demonstrate the positive benefits of integrating novel technologies into current agricultural and biological applications. These case studies will include collaborators who have deep domain knowledge about the application or problem, but who may have not been successful in integrating technology into their application, thereby growing the network of researchers and practitioners who have an understanding and appreciation of these technology-driven approaches.
Project Methods
Methods for developing new systems will include applying principles of control theory (linear and nonlinear), circuit design, mechanical fabrication and testing, and software development (utilizing C++, Python, ROS, and others). In addition to system development, off-the-shelf sensors may be used to enhance or augment the system capabilities. These new systems will be tested in the field to generate data sets focused on the specific case study; these data sets may be supplemented with existing data sets from in situ sensors, surveys, previous studies, among others. This work will be carried out with collaborators (university and federal researchers, stakeholders, practitioners) who likely have not consistently utilized automated technology in their work, thereby increasing and broadening the adoption of new sensing systems. Additionally, students will be trained through formal classroom and laboratory instruction, and updated curriculum will be developed as technology advances to ensure curriculum is up to date. A smaller subset of these students will be intensively trained in the use of technology development, deployment, and evaluation through the development of MS thesis and PhD dissertation projects that integrate deep technical knowledge with domain knowledge about biological and agricultural systems. To evaluate the impact on the intended audiences, progress towards developing the outlined products (e.g., journal articles, presentations, extension materials) will be monitored.