Remote Sensing in Precision Agriculture

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1 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

2 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

3 Conventional Agriculture Uniform management of farms field average condition under and over management best field condition over management of most locations

4 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

5 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

6 Precision Management Optimal Resource Management WISDOM Implementation KNOWLEDGE Decision Process Map Based or Real Time Approach Analysis and Diagnostics Data Collection DATA* Information Management INFORMATION

7 Spectral Signatures

8 Bare soil reflectance Remote Sensing 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

9 Precision Nitrogen Management Reactive Strategies: Dynamic In-Season N Management (From J. Schepers, 2005)

10 Properties of N deficient Plants Green reflectance increases Red reflectance increases & NIR reflectance decreases Differences in reflectance greatest between nm, followed by rededge ( nm)

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12 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

13 Hyperspectral Data Cube Nigon, Rosen, Mulla et al., 2014

14 Best Reflectance Wavelengths? Thenkabail et al. (2000) The greatest information about plant characteristics with multiple narrow bands includes the longer red wavelengths ( nm), shorter green wavelengths ( nm), red-edge (720 nm), and NIR ( nm and 982 nm) spectral bands The information in these bands is only available in narrow increments of 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

15 Hyperspectral narrow-band vegetation indices available for use in precision agriculture Index Definition Reference SR1 NIR/Red = R 801 /R 670 Daughtry et al., 2000 SR7 R 860 /(R 550 * R 708 ) Datt, 1998 NDVI (R 800 -R 680 )/(R 800 +R 680 ) Lichtenthaler et al., 1996 Green NDVI (R 801 -R 550 )/(R 800 +R 550 ) Daughtry et al., 2000 (GNDVI) NDI1 (R 780 -R 710 )/(R 780 -R 680 ) Datt, 1999 NDI2 (R 850 -R 710 )/(R 850 -R 680 ) Datt, 1999

16 Aerial Remote Satellites Airplanes Sensing Unmanned Aerial Vehicles

17 Advantages UAVs 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

18 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

19 Ground Based N Sensor NTech Greenseeker Tractor Mounted Hand-held Units Real-time

20 Precision Ag US Survey Top 4 Huge growth potential Based on 18 th annual nation-wide survey (across 33 states) by Purdue Univ and Crop Life Magazine, 2013.

21 Profitability of Precision Farming Services Based on 18 th annual nation-wide survey (across 33 states) by Purdue Univ and Crop Life Magazine, 2013.

22 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

23 Future Research Fronts Precision plant management Real time simulation models for controlled management Smart robots (ground and aerial vehicles)