Unmanned Aerial Vehicle System for Remote Sensing Applications in Agriculture and Aquaculture Dr. Randy. R. Price, Goutam. J. Nistala, Dr. Steven G. Hall.

Slides:



Advertisements
Similar presentations
You are the owner of a acre farm, and you are growing many different crops in your farm…
Advertisements

Remote sensing, promising tool of the future Mária Szomolányi Ritvayné – Gabriella Frombach VITUKI CONSULT MOKKA Conference, June
Predicting and mapping biomass using remote sensing and GIS techniques; a case of sugarcane in Mumias Kenya Odhiambo J.O, Wayumba G, Inima A, Omuto C.T,
Nutrient Management – Now and in the Future Richard Ferguson Tim Shaver University of Nebraska.
Remote Sensing Hyperspectral Imaging AUTO3160 – Optics Staffan Järn.
Combining multispectral and thermal infrared airborne remote sensing and airborne flux measurement for atmosphere- biosphere interaction studies WORKSHOP.
Use of Remote Sensing and GIS in Agriculture and Related Disciplines
Remote sensing in meteorology
January 20, 2006 Geog 258: Maps and GIS
Meteorological satellites – National Oceanographic and Atmospheric Administration (NOAA)-Polar Orbiting Environmental Satellite (POES) Orbital characteristics.
Mining Areas – Geographic Information Systems + Remote Sensing Faculty of Civil Engineering, CTU in Prague GWF - Mining and Exploration 15 May 2013 Potential.
UAVs and Precision Agriculture Carlyle C. Brewster 1, Erin L. Holden 1 and Jon C. Allen 2 1 Department of Entomology Virginia Polytechnic Institute and.
Use of remote sensing on turfgrass Soil 4213 course presentation Xi Xiong April 18, 2003.
Precision Farming What’s influencing our ag system? 2011 Bobby Grisso Virginia Tech Biological Systems Engineering.
Use of Multispectral Imagery for Variable Rate “Application-zone” Identification in Cotton Production Tim Sharp Beltwide Cotton Conference January 6-10,
Relationships Between NDVI and Plant Physical Measurements Beltwide Cotton Conference January 6-10, 2003 Tim Sharp.
Remote Sensing in Precision Irrigation
Satellite Imagery and Remote Sensing NC Climate Fellows June 2012 DeeDee Whitaker SW Guilford High Earth/Environmental Science & Chemistry.
Application of Drone Technology towards Economic Benefit of Southwest Georgia Atin Sinha Albany State University Albany, GA.
A comparison of remotely sensed imagery with site-specific crop management data A comparison of remotely sensed imagery with site-specific crop management.
Differences b etween Red and Green NDVI, What do they predict and what they don’t predict Shambel Maru.
Introduction to Remote Sensing. Outline What is remote sensing? The electromagnetic spectrum (EMS) The four resolutions Image Classification Incorporation.
VEGETATION CLASSIFICATION USING NDVI FROM UAV S A MANDA U RQUIZA M ENTOR : S RIKANTH S ARIPALLI 2015 S PACE G RANT S YMPOSIUM S ATURDAY, A PRIL 18 TH A.
Lesson 7 Understanding Remote Sensing Technology.
Recent advances in remote sensing in hydrology
GPS Capabilities and Future Products Robert E. Wolf, Randy K.Taylor Biological and Agricultural Engineering Dept. Kansas State University.
Introduction To describe the dynamics of the global carbon cycle requires an accurate determination of the spatial and temporal distribution of photosynthetic.
Chapter 5 Remote Sensing Crop Science 6 Fall 2004 October 22, 2004.
West Hills College Farm of the Future. West Hills College Farm of the Future Precision Agriculture – Lesson 4 Remote Sensing A group of techniques for.
PREFER 1 st Annual Review Meeting, 5-6 Dec 2013, Milano-Italy PREFER WP 3.2 Information support to Recovery/Reconstruction Task 7 Damage Severity Map PREFER.
GreenSeeker® Handheld Crop Sensor
West Hills College Farm of the Future The Precision-Farming Guide for Agriculturalists Chapter Five Remote Sensing.
Introduction to the Principles of Aerial Photography
PREFER 1 st Annual Review Meeting, 5-6 Dec 2013, Milano-Italy PREFER WP 3.2, Task 7.
Remote Sensing of Vegetation. Vegetation and Photosynthesis About 70% of the Earth’s land surface is covered by vegetation with perennial or seasonal.
Satellite Imagery and Remote Sensing DeeDee Whitaker SW Guilford High EES & Chemistry
EG2234: Earth Observation Interactions - Land Dr Mark Cresswell.
GPS Aided INS for Mobile Mapping in Precision Agriculture Khurram Niaz Shaikh Supervised by: Dr. Abdul Rashid bin Mohammad Shariff Dept. of Biological.
Development of a Small Remotely Piloted Vehicle for the Collection of Normalized Difference Vegetative Index Readings Dr. Randy R. Price, Goutam Nistala.
Development of Vegetation Indices as Economic Thresholds for Control of Defoliating Insects of Soybean James BoardVijay MakaRandy PriceDina KnightMatthew.
State of Engineering in Precision Agriculture, Boundaries and Limits for Agronomy.
Interactions of EMR with the Earth’s Surface
Measuring Vegetation Health NDVI Analysis of East Sacramento 1.
Weekly NDVI Relationships to Height, Nodes and Productivity Index for Low, Medium, and High Cotton Productivity Zones T. Sharp, G. Evans and A. Salvador.
GIS: The Systematic Approach to Precise Farm Management Robert Biffle Precision Agriculture April,
Precision Agriculture John Nowatzki Extension Ag Machine Systems Specialist.
Unmanned Aerial Vehicles Plant and Weed Management Art Hirsch & Chris Rice January 7, 2015.
Assessment on Phytoplankton Quantity in Coastal Area by Using Remote Sensing Data RI Songgun Marine Environment Monitoring and Forecasting Division State.
Farms, sensors and satellites. Using fertilisers Farming practice are changing Growing quality crops in good yields depends on many factors, including.
Satellite Imagery and Remote Sensing DeeDee Whitaker SW Guilford High EES & Chemistry
UNIT 2 – MODULE 5: Multispectral, Thermal & Hyperspectral Sensing
Remote Sensing. Content Introduction What is remote sensing? History of Remote Sensing Applications of Remote Sensing Components Advantages Disadvantages.
ENVIRONMENTAL INTELLIGENCE PLATFORM Company Specialized in : SEARCHES FOR INNOVATION SECURITY ENVIORMENT AND LOGISTICS ENERGY.
© 2016 Global Market Insights, Inc. USA. All Rights Reserved Global Agricultural Drones Market share to reach $1bn by 2024: Global Market.
Dr. Pinliang Dong Associate Professor Department of Geography University of North Texas USA.
Mapping Variations in Crop Growth Using Satellite Data
Using vegetation indices (NDVI) to study vegetation
Askar Choudhury, James Jones, John Kostelnick, Frank Danquah,
Drone applications in Forestry APEC/APFL forum, February 2017
Fly Expertly. Detect Truthfully. Perform Consistently.
Hyperspectral Sensing – Imaging Spectroscopy
GPS in Agriculture Ag Science II Mr. Dieckhoff
Mapping wheat growth in dryland fields in SE Wyoming using Landsat images Matthew Thoman.
Remote Sensing What is Remote Sensing? Sample Images
Unmanned Aircraft System-Based Structural Health Monitoring
By Blake Balzan1, with Ramesh Sivanpillai PhD2
Remote Sensing Section 3.
REMOTE SENSING.
Unit 2 Unmanned Aircraft
Remote sensing in meteorology
Presentation transcript:

Unmanned Aerial Vehicle System for Remote Sensing Applications in Agriculture and Aquaculture Dr. Randy. R. Price, Goutam. J. Nistala, Dr. Steven G. Hall Department of Biological and Agricultural Engineering Louisiana State University, AgCenter LSU BAE Remote Sensing in Agriculture and Aquaculture  Determination of a quantity by detecting it from a distance.  A common application of remote sensing is the use of satellite-borne instruments to determine the location and amount of resources on the surface of the Earth.  Management of agricultural crops and aquacultural products is important.  Yield of crops is based on many factors like inputs, weather, irrigation conditions, quality of pesticides and several other factors.  Optimization of inputs, yield and quality is very important.  Other methods like self diagnostics, crop scouting and pest reduction are inefficient and result in redundant application of resources.  Remote Sensing is looked upon as the alternative.  Piloted aircraft and satellites are the primary sources used to obtain RS images. Satellite Imagery for Remote Sensing Advantages  The system is reliable.  The farmer need not have special skills to obtain and use the data. Disadvantages  The quality and resolution of data and imagery obtained may not be sufficient for accurate diagnostics.  Data obtained can be easily affected by bad weather conditions like clouds or rain.  Non availability of data “when and where” required.The data can be obtained only at regular intervals.  Very expensive. Disadvantages  Significant experience required to fly the UAV.  Easily destructible. Automated Flight Control Selected references  Continuous Georeferencing for video based remote sensing on agricultural aircraft - S.J.Thomson, J.E.Hanks, and G.F.Sassenrath-Cole. Published in the transactions of ASAE Vol 45(4):  Airborne Multispectral Imagery for mapping variable growing conditions and yields of cotton, grain sorghum and corn - C.Yang, J.M. Bradford, C.L. Wiegand  The Development of Remote Sensing System using Unmanned Helicopter – Ryo SUGIURA, Noboru NOGUCHI, Kazunobu ISHII, Hideo TERAO. Proceedings of the July 26-27, 2002 Conference (Chicago, Illinois, USA) 701P0502.  A Hyperspectral Imaging System for Agriculture Applications – Chenghai Yang, James H. Everitt, Chengye Mao. Written for presentation at the 2001 ASAE Annual International Meeting, sponsored by ASAE Sacramento Convention Center. Objectives  To explore the use of an UAV for acquiring remotely sensed imagery and data in a cost efficient manner  To construct an UAV and its control system with stable flight characteristics  To make the UAV autonomous with an automatic guidance system  To equip the UAV with an image acquisition system. Control System Types Manual Control Use of FMA-Copilot Calculation of NDVI  The “Normalized Difference Vegetative Index (NDVI,) is a calculation, based on several spectral bands, of the photosynthetic output in a pixel in an image.  It measures the amount of green vegetation in an area.  Actively growing green plants strongly absorb radiation in the visible region of the spectrum (Photo synthetically.  Active Radiation) while strongly reflecting radiation in the Near Infrared region.  The UAV obtained images are used to calculate NDVI values of the entire field. Conclusions UAVs provide possibilities for:  Image capture at low altitude, reducing cloud problems  Interaction with pests including birds on aquaculture ponds  Automated or semi-automated operation, reducing labor  Repeatable performance  Cost effective use of technology  Further testing is underway  The co-pilot uses four infrared temperature sensors to monitor the aircrafts relationship to earths horizon.  In the infrared spectrum,the earth is warm below the horizon and the sky is cold above the horizon.  The copilot senses the aircrafts position relative to horizon during a flight and sends corrective signals to the aileron and elevator servos. The UAV’s that have been used Aerial images and destroyed Route programmed into the GPS Actual track of the UAV flight Unmanned Aerial Vehicle Advantages  It can be made and built in a time of 3-4 days.  All components are locally available.  Flight need not be scheduled. It can be based on the weather conditions and preferences of the farmer.  Availability of data and imagery immediately after the flight. Agriculture Aquaculture