1. UTILIZATION OF CROP SENSORS TO DETECT COTTON GROWTH AND N NUTRITION
Tyson B. Raper, Jac J. Varco, Ken J. Hubbard,
and Brennan C. Booker
Plant and Soil Science Department
Mississippi State University

2. INTRODUCTION Recent increase in fertilizer costs
N in cotton production
Recent increase in fertilizer costs
Deficiency limits yield and lowers quality
Excess N causes
rank growth
boll rot
difficulty in harvesting
increased need for growth regulators, insecticides, and defoliants
Variable Rate N
Increase Nitrogen Use Efficiency (NUE)
Decrease environmental pollution

3. INTRODUCTION
Ground-Based Sensors
Provide real-time cotton biomass and greenness
Fertilize response to crop reflectance
Need a more thorough understanding of relationship between canopy reflectance, cotton growth, and N nutrition.

4. OBJECTIVE
Examine the effectiveness of a ground-based sensor to predict cotton
Plant growth
Leaf N

5. Courtesy: Web Soil Survey 2009
METHODS
Location
Plant Science Research Farm, Mississippi State, MS
Randomized complete block design
4 Treatments x 4 Replications
12 rows
125’ long
3 10’ alleys
38” row spacing
4 sub-locations
Courtesy: Web Soil Survey 2009

6. METHODS (CONT.) Treatment Cultural
0, 40, 80, and 120 lb N/acre in a split-application
Planting (50%)
Early square (50%)
Cultural
No-till on beds
DPL BG/RR 445
No growth regulator applied
Low pest thresholds established and maintained

7. METHODS (CONT.) Data Collection Reflectance
YARA N Sensor (YARA International ASA, Oslo, Norway)

8. Source: YARA (Hydro Agri), tec5Hellma
METHODS (CONT.)
YARA N Sensor
Tractor mounted spectrometer
Wavelength Channels: 5, user selectable*
Wavelength range: nm
Optical inputs: 4 reflectance, 1 irradiance
Acquisition interval: 1 second
Area scanned: m²/s
Positioning Data: Trimble Pro XR
Speed: 3.5 mph
Bandwidth= ±5 nm
Source: YARA (Hydro Agri), tec5Hellma

9. METHODS (CONT.)

10. METHODS (CONT.) Data Collection Reflectance (cont.) Data Processing
YARA N Sensor
Set 76” above soil
Sense entire field
Views rows 2, 3, 4, 9, 10, 11
Data Processing
Sub-plot locations
Center of 15’ buffer
4 points selected

11. METHODS (CONT.) Data Collection Sub-Location Plant Data Plant Height
5 measured per sub-location
Leaf Sample
5 recently matured per sub-location
% Leaf N
Whole Plant Sample
Prior to defoliation
Yield, total N uptake

12. Sensing / Sampling Stages
METHODS (CONT.)
Sensing / Sampling Stages
Physiological Stages
Pre-Square
Early Square
2nd Week of Square
3rd Week of Square
Early Flower
2nd Week of Flowering
Peak flower

13. RESULTS

14. RESULTS

15. RESULTS

16. GNDVI vs PLANT HEIGHT

17. GNDVI vs LEAF N

18. GNDVI vs LEAF N

19. GNDVI vs LEAF N

20. GNDVI vs LEAF N

21. RED EDGE INFLECTION
First Derivative of Reflectance Signature

22. RED EDGE INFLECTION REIP calculated on a per plot basis
Gaussian 4 Parameter Peak Equation
Utilize l
700
710
720
740

23. REIP vs LEAF N

24. REIP vs LEAF N

25. REIP and NDVI

26. CONCLUSIONS
GNDVI relationships with leaf N and plant height improve through to peak flower.
Consistency across growing seasons supports the utility of crop reflectance.
GNDVI and NDVI have the potential to be effective measurements of plant growth in cotton.
REIP has the potential to be an effective measurement of N status in cotton.
These results support previous REIP publications
(Buscaglia et al., 2002; Fridgen et al., 2004).

27. Questions?