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)