Development of an Enterprise Level Precision Agriculture System
James Ellingson, Chih Lai
University of St. Thomas, School of Engineering
2115 Summit Ave, St. Paul, MN USA
elli4729@stthomas.edu;
Abstract – In this paper, a plan for the development of an Enterprise Level system for Precision Agriculture (PA) is described.
The basic elements of the PA system include ground based monitors of weather, precipitation and available sunlight, remotely controlled, autonomous small unmanned air vehicles (sUAVs) used to collect multi spectral crop data, databots for the collection of macro weather data from the web, and a collection of big data tools to analyze the very large amounts of collected data.
The University of St. Thomas School of Engineering has formed the Center for Optimized Autonomy and Control (COACo). The mission of this organization is to provide innovative applied learning opportunities for students in Engineering and Software disciplines, where they can build operational systems, deploy them on focused use cases, and engage responsibly with community efforts (both locally and globally) to create technology solutions for the common good.
The first operational system this Center is building focuses on Precision Agriculture. Precision Agriculture is a crop-production management concept based on implementing modern information technologies such as GPS (Global Positioning Systems), Remote Sensing Technology and GIS (Geographic Information Systems). Precision Agriculture is characterized as the precise application of agricultural inputs for crop growth considering relevant factors such as: available light, soil, weather and crop management practices. The implementation of this methodology is site-specific and provides a basis for sustainable agriculture with long term financial and cultural benefits.
The Precision Agriculture Enterprise Concept Project is intended to define, develop, prototype and refine the tools and methodologies necessary to infuse enterprise level information technology, sophisticated, miniaturized, autonomous remote sensing platforms, and in-situ controls with existing farm machinery to produce best quality agricultural products using a sustainable methodology to revitalize the farm land that produces the product. The project will introduce the technology in a manner that does not require the agronomist to become an IT professional, UAV (Unmanned Airborne Vehicle) pilot, remote sensor operator or data analyst.
The goals of this project are to integrate small Unmanned Airborne Vehicles (sUAVs), a multi-rotor helicopter platform (multi-copter), multi spectral cameras, and crawling robots for collecting earth samples and taking measurements below the crop canopy. The required land based sensors for such a system include a wireless network for sUAV communication as well as ground based sensors to monitor weather conditions, incident sunlight, precipitation and ground moisture. The team will integrate weather, light and soil information in-situ (in the field) and correlate that data with other internet-based climate information in order to provide focused tracking of field and crop conditions.
The UST team is currently working to implement the first generation combined testbed, and use this system for practical applications in agriculture. The project will afford our students hands on experience in learning how to build, interconnect, operate, and refine these highly integrated systems. Necessary hardware components have been acquired, a technical roadmap of goals and sub-projects has been established, and system design documents (including Project Plan, and Data Dictionary) have been defined. The team is currently seeking grant funding to support this work.
Part of this work centers on integrating multispectral cameras into sUAV and multi-copter platforms. A multi spectral image produced using a Tetracam Micro camera can be processed ton compute the NDVI (Normalized Difference Vegetation Index). Green areas have strong photosynthesis and are growing well and unstressed. Magenta areas (snow, concrete, asphalt) indicate no photosynthesis.
Progress to date: The Tetracam has been integrated into the flight control system and is sharing a common GPS. Way point navigation has been achieved and high resolution, near IR images are recorded at a rate of one per six seconds. These images are analyzed off line to assess the level of plant activity and to identify any regions of pathology for further investigation using ground based unmanned platforms (vehicles) prior to alerting the agronomist or farmer.
Keywords: precision agriculture, sAUV, big data, multi spectral imagery.
