Comparison of Active Optical Sensors

Slides:



Advertisements
Similar presentations
Do In and Post-Season Plant-Based Measurements Predict Corn Performance and/ or Residual Soil Nitrate? Patrick J. Forrestal, R. Kratochvil, J.J Meisinger.
Advertisements

Missouri algorithm for N in corn
Determine seeding rate and hybrid effects on: Phenotypical and physiological plant measurements Canopy and leaf sensor measurements A goal in precision.
Crop Canopy Sensors for High Throughput Phenomic Systems
FIELD-SCALE N APPLICATION USING CROP REFLECTANCE SENSORS Ken Sudduth and Newell Kitchen USDA-ARS Translating Missouri USDA-ARS Research and Technology.
Results Effect of Simulated Grazing Intensity on Dual-Purpose Winter Wheat Growth and Grain Yield Dillon Butchee and Jeff Edwards Department of Plant and.
Producing “New” Small Grain Crops in the Mid-Atlantic Wade Thomason.
Sensor Orientation to maize canopy row and estimating biomass and Nitrogen Status Paul Hodgen, Fernando Solari, Jim Schepers, John Shanahan, Dennis Francis.
N Reflections on Years of Sensor Research Algorithm Development and Jim Schepers.
May 6, Drought tolerant Miller ComparisonsEfawLCB Grain Yield (bu/ac) Drought tolerant vs. Non-drought tolerant Monsanto vs. Pioneer
Sensor research and algorithm development for corn in ND L.K. Sharma, D.W. Franzen, H. Bu, R. Ashley, G. Endres and J. Teboh North Dakota State University,
May 15,  Rates – current industry recommendations  Soil and Foliar Applied ◦ Boron, Chlorine, Copper, Zinc ◦ Calcium – Foliar only  NDVI, Soil,
The Nitrogen Requirement and Use Efficiency of Sweet Sorghum Produced in Central Oklahoma. D. Brian Arnall, Chad B. Godsey, Danielle Bellmer, Ray Huhnke.
Outline Introduction – Importance for SE Mississippi State – Sensor Comparison – Wavelength/Index Analysis University of Arkansas – Active detection of.
GreenSeekerTM Variable Rate Applicator Equipment and Applications
Comparison of Commercial Crop Canopy Sensors Ken Sudduth Newell Kitchen Scott Drummond USDA-ARS, Columbia, Missouri.
NUE Workshop: Improving NUE using Crop Sensing, Waseca, MN
Sensor Technology in India R.K. Gupta, B. Singh, P. Chandna, R.K. Sharma, M.L. Jat.
The Effects of Topdressing Organic Nitrogen on Hard Red Winter Wheat - Year 2 Name: Erica Cummings Date: March 15, 2012 Title: Crops and Soils Technician.
Nitrogen Use Efficiency Workshop Canopy Reflectance Signatures: Developing a Crop Need-Based Indicator for Sidedress Application of N Fertilizer to Canola.
Differences b etween Red and Green NDVI, What do they predict and what they don’t predict Shambel Maru.
Methods and Materials Soils – 25 Kansas soils were dried for 48 h at 60 o C, ground, sieved to 2 mm. After preparation, soils were analyzed for Walkley-Black.
Ask the Expert Improve Yields and Make Money by Saving Inputs with GreenSeeker Products Daniel Rodriguez Russ Linhart.
GreenSeeker® Handheld Crop Sensor
Determining the Most Effective Growth Stage in Corn Production for Spectral Prediction of Grain Yield and Nitrogen Response Department of Plant and Soil.
Tracy Blackmer Peter Kyveryga Advancing Agricultural Performance ® 1 Iowa Soybean Association On-Farm Network®
Use of Alternative Concepts for Determining Preplant and Mid-Season N rates.
Are NDVI Readings Sensors Dependent? Tremblay, N., Z. Wang, B. Ma, C. Bélec and P. Vigneault St-Jean-sur-Richelieu, Quebec.
NDVI: What It Is and What It Measures Danielle Williams.
The Use of Red and Green Reflectance in the Calculation of NDVI for Wheat, Bermudagrass, and Corn Robert W. Mullen SOIL 4213 Robert W. Mullen SOIL 4213.
Optimizing Nitrogen and Irrigation Timing for Corn Fertigation Applications Using Remote Sensing Ray Asebedo, David Mengel, and Randall Nelson Kansas State.
UTILIZATION OF CROP SENSORS TO DETECT COTTON GROWTH AND N NUTRITION
2013 NUE Conference Des Moines, Iowa August 5-7 Jacob T. Bushong.
Active-Crop Sensor Calibration Using the Virtual-Reference Concept K. H. Holland (Holland Scientific) J. S. Schepers (USDA-ARS, retired) 8 th ECPA Conference.
GreenSeeker Sensor Brian Arnall Precision Nutrient Management Plant and Soil Sciences Department Oklahoma State University.
Improving NUE Using Crop Sensing : “It Takes a Village”
Enhancing Sorghum Yield and Profitability through Sensor Based N Management Dave Mengel and Drew Tucker Department of Agronomy K-State.
Variable-Rate N Fertilization of Wheat and Corn in the Mid-Atlantic Variable-Rate N Fertilization of Wheat and Corn in the Mid-Atlantic Wade Thomason,
Development of Vegetation Indices as Economic Thresholds for Control of Defoliating Insects of Soybean James BoardVijay MakaRandy PriceDina KnightMatthew.
Refinement of the Missouri Corn Nitrogen Algorithm Using Canopy Reflectance Newell Kitchen, Ken Sudduth, and Scott Drummond USDA-Agricultural Research.
State of Engineering in Precision Agriculture, Boundaries and Limits for Agronomy.
LATE SEASON N APPLICATIONS FOR IRRIGATED HARD RED WHEAT PROTEIN ENHANCEMENT. S.E. Petrie*, Oregon State Univ, B.D. Brown, Univ. of Idaho. Introduction.
N Fertilization in Colorado Raj Khosla Colorado State University May 19 th & 20 th Oklahoma State University Raj Khosla Colorado State University May 19.
Three Alternative Nitrogen Management Strategies for Cereal Grain Production Brian Arnall Brian Arnall Plant and Soil Sciences Department Oklahoma State.
On-farm Evaluation of Optical Sensor Technology for Variable Rate N Application to Corn in Ontario and Quebec Bao-Luo Ma, and Nicolas Tremblay Eastern.
NFOA for Wheat and Corn. Yield Potential Definitions INSEYIn Season Estimated Yield = NDVI (Feekes 4 to 6)/days from planting to sensing (days.
Dave Mengel, Kansas State University Multi-State Winter Wheat Sensor Project,
REAL-TIME CALIBRATION OF ACTIVE CROP SENSOR SYSTEM FOR MAKING IN-SEASON N APPLICATIONS K.H. Holland and J.S. Schepers Holland Scientific and USDA-ARS,
Oklahoma State University Precision Agriculture / Soil Fertility / Soil Nutrient Management Why should Nitrogen Use Efficiencies be improved? What technologies.
Dave Franzen – North Dakota State University Newell Kitchen – USDA-ARS, Columbia, MO Laura Stevens, Richard Ferguson – University of Nebraska-Lincoln.
Theory of Predicting Crop Response to Non-Limiting Nitrogen.
Drew Tucker and Dave Mengel KSU Agronomy An update on Kansas sensor based N recommendations.
DEPARTMENT OF ENVIRONMENTAL SCIENCE & TECHNOLOGY Laboratory for Agriculture and Environmental Studies Sensor Based Variable Rate N Management in Mid-Atlantic.
Regional Project Objectives  To evaluate the performance of sensor-based N recommendation algorithms across a wide range of soil and climate conditions.
Exploratory Research in Corn
Variable Rate Nitrogen Application in Corn Production
Developing Sidedress N Recommendations for Corn in Central PA
NDVI Active Sensors in Sugarbeet Production for In-Season and Whole Rotation Nitrogen Management.
Evolution of OSU Optical Sensor Based Variable Rate Applicator
Crop-based Approach for In-season N Application
Sensing Resolution in Corn
Strategies for Split N Applications for Corn
E.V. Lukina, K.W. Freeman,K.J. Wynn, W.E. Thomason, G.V. Johnson,
Dave Franzen – North Dakota State University
Annual ASA Meeting, Indianapolis
UNL Algorithm for N in Corn
Eric Miller August 3rd, 2010 Dr. Camberato & Dr. Nielsen
Methods of Determining Canopy Closure in Winter Wheat
Tastes Great. Less Filling
Reflectance and Sensor Basics
Presentation transcript:

Comparison of Active Optical Sensors Ray Asebedo and Dave Mengel Kansas State University

Previous work K-State has had sensor based N rate algorithms available for use with the GreenSeeker and CropCircle sensors since 2009, for wheat and grain sorghum. The wheat algorithms were revised and expanded as to single topdress or intensive N management versions in 2013. These algorithms seem to work successfully with the GreenSeeker and Crop Circle sensors.

KSU Sensor Research and Development Focus on developing Nitrogen fertilizer Recommendation and yield estimation models for corn, wheat and grain sorghum. Models are for use state wide, over a wide range of growth stages, climatic conditions, cropping systems and variable field conditions. Currently available algorithms and those under construction are not made specifically for one brand of sensor, but for all active light source sensors that use Red NDVI.

Basic questions Do we need to develop specific algorithms for each of the sensors currently available when using sensors on corn? Can we develop calibrations for a variety of sensors in order to normalize the data and allow our algorithms to be used with multiple brands and models of sensors? Can any Red NDVI sensor work with the KSU algorithms with the accuracy and precision we intended?

Sensor Comparison Topics Height Sensitivity Speed Sensitivity Signal to Noise Correlation of Red NDVI across sensors

Experiment Design 7 Corn sites in 2013 Plot size 10 feet wide by 50 feet long 0 to 220 lbs N/ac applied in single and split applications at preplant, V6, V10, and R1. Treatments arranged in an RCBD with 4 replications Plant populations ranged from 17000 plts/ac to 33000 plts/ac

Sensors Utilized and Methods Trimble Greenseeker 2 Trimble Greenseeker HandHeld (pocket sensor) Holland Scientific Crop Circle ACS-470 Holland Scientific Rapid Scan (obtained late) Ground Speed 3 fps (walking) Height 36 inches above canopy

Basic Sensor Differences Trimble Greenseeker 2 Uses Red 660 nm and NIR 770 nm Sensor to Canopy Range: 24 to 60 inches Sampling rate 20 hz Trimble Greenseeker Handheld Uses Red 660 nm and NIR 770 nm Sensor to Canopy Range: 24 to 48 inches Sampling rate 2 hz Pulse Modulation

Basic Sensor Differences Holland Scientific ACS-470 Red 670 nm and NIR 760 nm Sensor to Canopy Range: 22 to 60 inches Sampling Rate : 10 hz Holland Scientific Rapid Scan Red 670 nm and NIR 780 nm Sensor to Canopy Range: 12 to 118 inches Sampling Rate : 12 hz

ACS-470 Above Greenseeker 2 Below Rapid Scan Above Greenseeker Handheld Below

2013 Corn V6 through R1

2013 Corn V6 through R1

2013 Corn V6 through R1

Initial Summary Greenseeker 2 and ACS-470 provide similar data throughout the growing season. Rapid Scan and Greenseeker Handheld also correlate well with the Greenseeker 2 and ACS-470. The NDVI values from the handhelds are slightly higher. Appears to be more variability in the data from the handhelds.

Site Specific All of the sensors follow the same trends. The amount of observed NDVI change is less in the handheld sensors. Lower plant population, erect leaf architecture, sensor footprint and sampling rate are potential reasons.

Sterling Corn V10

Site Specific Within plot height variation caused significant issues with the data obtained with the Greenseeker Handheld due to a smaller working range for height. The Rossville site was on a deep sand soil but contained pockets of clay lenses which led to extreme height variation.

Rossville Corn V10

Field N Recommendation

Summary The ACS-470 and Greenseeker 2 can use the same basic algorithm, but calibrating the algorithm for the sensor being utilized will likely improve the recommendations. The Rapid Scan and Greenseeker Handheld will require calibration of an algorithm designed for the ACS-470 or Greenseeker 2. The Greenseeker Handheld displays a tendency to give inconsistent data for corn sites with significant variability. Therefore we cannot recommend its use on corn at this time until this issue has been resolved.

Continuing Research Further research is needed in order to evaluate the site specific performance of these sensors and determine if sensor methods and settings need to be adjusted depending upon the crop condition, hybrid, populations, and current environmental conditions in order to optimize results for Variable Rate and Bulk field N applications.