remote sensing in precision agriculture...computers, geographic information systems, global...
TRANSCRIPT
Remote Sensing in Precision Agriculture
D. J. Mulla Professor and Larson Chair for
Soil & Water Resources Director Precision Ag Center Dept. Soil, Water, & Climate
Univ. Minnesota
Challenges Facing Agriculture Need to feed and provide energy for an
additional 1 billion people in 10 years using sustainable approaches
Very little new land is available for new rainfed production
Climate change threatens to alter rainfall patterns and crop yield potential
Need to reduce agricultural impacts on water quality & greenhouse gases
Conventional Agriculture
Uniform management of farms – field average condition
● under and over management – best field condition
● over management of most locations
Precision Agriculture A management
practice applied at the right rate, right time and the right place.
Field sub-region management
– Nutrients – Drainage or
Irrigation – Pests and Weeds – Tillage and
Seeding Operations
Benefits of Precision Agriculture
Increased Profitability – improved efficiency of inputs – improved yield and quality of crop
Improved Crop Productivity and Quality Reduced Risk Protection of the Environment
Optimal Resource Management
Data Collection
Information Management
Analysis and Diagnostics
Decision Process
Precision Management
Implementation
WISDOM
KNOWLEDGE INFORMATION
DATA* Map Based
or Real Time Approach
Spectral Signatures
Remote Sensing Bare soil reflectance
– Soil organic carbon and water content – Iron oxides or carbonates
Crop reflectance – Leaf area index – Crop growth stage – Crop color and leaf N status – Weeds and disease
Thermal emission of energy – Surface temperature and crop water stress
Precision Nitrogen Management Reactive Strategies:
Dynamic In-Season N Management
(From J. Schepers, 2005)
Properties of N deficient Plants • Green reflectance
increases • Red reflectance
increases & NIR reflectance decreases
• Differences in reflectance greatest between 550 – 600 nm, followed by red-edge (680 – 730 nm)
Multi-spectral broad-band vegetation indices available for use in Precision Agriculture
Index Definition Reference
GDVI NIR-G Tucker, 1979
NDVI (NIR-R)/(NIR+R) Rouse et al., 1973
GNDVI (NIR-G)/(NIR+G) Gitelson et al., 1996
SAVI 1.5 * [(NIR-R)/(NIR+R+0.5)] Huete, 1988
GSAVI 1.5 * [(NIR-G)/(NIR+G+0.5)] Sripada et al., 2005
OSAVI (NIR-R)/(NIR+R+0.16) Rondeaux et al., 1996
Hyperspectral Data Cube
Nigon, Rosen, Mulla et al., 2014
Best Reflectance Wavelengths? Thenkabail et al. (2000)
The greatest information about plant characteristics with multiple narrow bands includes the longer red wavelengths (650-700 nm), shorter green wavelengths (500-550 nm), red-edge (720 nm), and NIR (900-940 nm and 982 nm) spectral bands – The information in these bands is only available in narrow increments of 10-
20 nm, and is easily obscured in broad multispectral bands that are available with older satellites
The best combination of two narrow bands in NDVI-like indices is centered in the red (682 nm) and NIR (920 nm) wavelengths, but varies depending on the type of crop, as well as the plant characteristic of interest
Hyperspectral narrow-band vegetation indices available for use in precision
agriculture Index Definition Reference
SR1 NIR/Red = R801/R670 Daughtry et al., 2000
SR7 R860/(R550 * R708) Datt, 1998
NDVI (R800-R680)/(R800+R680) Lichtenthaler et al.,
1996
Green NDVI
(GNDVI)
(R801-R550)/(R800+R550) Daughtry et al., 2000
NDI1 (R780-R710)/(R780-R680) Datt, 1999
NDI2 (R850-R710)/(R850-R680) Datt, 1999
Aerial Remote Sensing
Satellites Airplanes Unmanned Aerial
Vehicles
UAVs Advantages
– Flexibility in choosing when to fly – Inexpensive – High resolution remote sensing imagery
Disadvantages – Difficulty in getting COA from FAA – Light payload limits camera sophistication – Short battery life limits area scanned – Imagery must be mosaicked
Proximal Remote Sensing
Sensors can be mounted on tractors, spreaders, sprayers or irrigation booms – GreenSeeker – Crop Circle – WeedSeeker – Infrared thermometers
Allows real time site specific management of fertilizer, pesticides or irrigation
Ground Based N Sensor
NTech Greenseeker Tractor Mounted Hand-held Units Real-time
Precision Ag US Survey
Based on 18th annual nation-wide survey (across 33 states) by Purdue Univ and Crop Life Magazine, 2013.
Top 4
Huge growth potential
Profitability of Precision Farming Services
Based on 18th annual nation-wide survey (across 33 states) by Purdue Univ and Crop Life Magazine, 2013.
Conclusions Precision agriculture has exhibited enormous
growth around the world since its beginning in the mid-1980’s
This growth was driven by technological advances associated with the growing availability of computers, geographic information systems, global positioning satellites and remote sensing
Precision agriculture grew because it was good for business, good for farm production, gave greater efficiency and better cost effectiveness in managing farm inputs, and it was good for the environment
Future Research Fronts
Precision plant management Real time simulation models for
controlled management Smart robots (ground and aerial
vehicles)