row crop harvest logistics for single harvester/grain cart
TRANSCRIPT
Row Crop Harvest Logistics for Single Harvester/Grain Cart Operations
John Evans, MSPh.D. Candidate
Joe Luck, Associate Professor Santosh Pitla, Assistant Professor
Department of Biological Systems Engineering University of Nebraska Lincoln
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• Project Motivation • Objectives • Harvester Modeling • Grain Cart Modeling• Decision Support Tool
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Outline
Shrinking Profit Margins§ Cost of equipment ↑§ Cost of inputs ↑§ Commodity prices ↓
Higher in-field efficiency is needed to reduce cost and increase productivity § Equipment selection § Time and compaction reduction
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Project Motivation
Create a practical and feasible harvest logistics model with decision support tools for single harvester, single grain cart operations. • User Inputs
§ Yield Monitor Data § Machine Capacities
- Harvester- Grain Cart
• Outputs§ Optimized Paths
- Productivity- Bottlenecks- Economics
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Project Objective/ Scope
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Harvester Logistics Modeling
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ObjectiveDevelop a mathematical model of single harvester operation in headland field patterns capable of the following:1.Minimizing the non-working in-field travel of the harvester in
irregular shaped fields.2.Producing solution that allows for unloading on the go. 3.Calculate the possible reduction in non-working travel compared to
actual harvest data.
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Project Objective/ Scope
OptimizationAlgorithm
PassesThatTheHarvesterMustComplete OptimalRouteMinimizingTime
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General Models • Bochtis, D. D., & Vougioukas, S. G. (2008). Minimizing the non-working distance travelled by
machines operating in a headland field pattern. Biosystems Engineering, 101(1), 1–12. https://doi.org/10.1016/j.biosystemseng.2008.06.008
• Oksanen, T., & Visala, A. (2009). Coverage path planning algorithms for agricultural field machines. Journal of Field Robotics, 26(8), 651–668. https://doi.org/10.1002/rob.20300
Harvest Modeling• Hansen, A. C., Zhang, Q., & Wilcox, T. A. (2007). Modeling and analysis of row crop
harvesting patterns by combines. Transactions of the ASABE, 50(1), 5–12.
• Ali, O., Verlinden, B., & Van Oudheusden, D. (2009). Infield logistics planning for crop-harvesting operations. Engineering Optimization, 41(2), 183–197. https://doi.org/10.1080/03052150802406540
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Past Work
Need spatial data to validate and compare against harvest optimization model.• Desired Data:
§ Spatial Machine Data - Engine Speed - Fuel Usage - Unload Auger Status
§ Spatial Agronomic Data- Yield
~ 3600 acres of data collected from two producers
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Harvester Data Collection
• Loop
For w < 2r, theta is given by: 𝜃 = cos'( )*+
𝐿𝑜𝑜𝑝𝑇𝑢𝑟𝑛𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 = 𝜋 ∗ 𝑟 ∗ (𝜃 + 90)
90 + ∆𝑦
• U-Turn
𝑈_𝑇𝑢𝑟𝑛𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 = F𝑑GH
�
G∈H
+ ∆𝑟𝑖 + ∆𝑟𝑗
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Turning Distance Calculation
• Plotted actual vs. predicted of every turn in real field path
• Predicted was almost always less than actual because predicted is perfect turn at tightest turning radius.
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Turning Distance Verification
Min ∑Harvester_Non-Working_Distance
With respect to:
1) Unload Auger Position2) Unloading Rate 3) Bin Capacity
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Optimization Equation
Genetic Algorithm§ Generate Random Population (Set of Paths)
- Paths were created from permutation of passes- Direction of passes was based on order from first
pass§ Calculate Route Length
- Check constraints - add penalty if necessary - Distance between passes was calculated and
summed§ Selection of Best Routes
- Top 10% of shortest paths are selected as “parents” and are crossed and mutated to generate a new population of “children”
§ Settings - Elitist Selection - Population (group of paths) size = 600- Max iterations = 100
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Optimization Method
Passes
Path
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ResultsActual vs. Optimized • Irregular shaped fields*• Harvest unload
consideration • 29.1% average
reduction of non-working travel
Field Crop Area (m2)[ac]
Actual Path Order w/ Modeled Turns
Optimized Route Distance (m)
% Non-Working Travel Reduced
EFE Soybeans 22070 [54.53] 2705 1880 30.50
R Soybeans 35360 [87.38] 3019 2067 31.53
H Corn 3349 [8.27] 1317 984 25.28
* Continuous AB passes required
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Grain Cart Modeling
Calculate cycle time of the grain cart § Account for cart unloading § Avoid obstacles (unharvested crop, fence lines, etc..)
Inputs: § Harvester path§ Harvester unloading points§ Cart unloading location§ Speed
- Loaded - Unload
Outputs:§ # of harvester waiting events§ Total harvester waiting time § Total grain cart travel distance
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Grain Cart Model Objective
Need grain cart data to determine if it is a limiting factor• Desired Data: Spatial Machine Data and Bin Level (Grain
Mass)• Gap in current data collection methods:
§ Scale data is separate from machine data. § Scale data is usually only recorded when off loading to truck. § Machine data not usually recorded at all. § GPS is not available because units are often shared between tractor and
combine.
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Grain Cart Data Collection
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Grain Cart Data Collection• Initial Solution
§ Publish Scale Data on CAN Bus- Instrumentation Amplifier - CAN Node
§ CAN Data Logger w/ GPSSCALE DISPLAY
INSTRUMENTATIONAMPLIFIER
CAN NODE
J1939 CAN LOGGER
POWERCONTROL
TRACTOR DATASIMULATOR
(TESTING ONLY)
TEST LOAD CELL
Bench Test Spatial Variation of Grain Cart Weight
LegendGrain_CartWieght__lb
< 15550
15499 - 32800
32799 - 48500
48501 - 61800
> 61800
Unloading Location
Weight lbs.
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Boundary Generation
Coverage map using polygons Binary representation
Binary Mask Legend▪ RestrictedTravel▪ UnrestrictedTravel−CartPath
Legend▪ HarvesterCoverage○ HarvesterUnload− Boundary
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Grain Cart
Binary Mask Legend▪ RestrictedTravel▪ UnrestrictedTravel−CartPath
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Grain Cart Simulation
Binary Image
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Grain Cart Simulation
80536 Meters (~50 Miles) of Cart Travel
Actual Grain Cart Travel Data
Legend− HarvesterUnloadPath−ActualCartPath
Simulated Grain Cart Travel Data
54305 Meters (~34 Miles) of Cart Travel
Legend− HarvesterUnloadPath−ActualCartPath
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Grain Cart Model Example Output
§ # of harvester waiting events = 2§ Total harvester waiting time = 4.56 min § Total grain cart travel distance = 51.0km
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Decision Support Tool
Tool capable of identifying/calculating: • Bottlenecks • In-field travel • Cost per bushelVarying:
§ Header width § Bin capacities
- Harvester - Grain cart
§ Unloading rate - Harvester - Grain cart
§ Economic factors
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Objective
Purpose:Define the passes the harvester needs to complete based on:
- Header width- AB pass orientation
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Pass Generation
• Split passes§ Distance § Heading§ Swath width
• Find straight passes§ STD of heading
• Find AB pass§ Fuzzy subtractive clustering
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Pass Generation
- AB PASS
- HEADLAND
• Find baseline pass using boundary, AB pass heading, and new header width
• Offset new lines • Interpolate yield to new
points on 1hz interval
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Pass Generation (Header Width Change)
AB Baseline
Start Simulation
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Decision Support Tool GUI
Data Import
Economic Values
Grain Cart Parameters
Harvester Parameters
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Decision Support Tool Results
Harvester 1 2HeaderWidth(ft) 40 40
Area(ac) 43.06 43.06Distance(mi) 9.06 9.06
Productivity(ac/h) 23.78 20.45Efficiency 0.9 0.9Fuel($/ac) 2.7 3.4
TotalCost($/ac) 13.26 19.52TotalCost($/bu) 0.31 0.45
GrainCart 1 2Binsize 1282 880
#ofwaitevents 1 5
CumulativeWaitTime(min) 2.43 13.65
• Harvest Logistics Model§ Real field data § Irregular shape fields* § Optimized path
• Grain Cart Model § Calculate
- Cycle time- Total distance
§ Identify - Limitations- Current Efficiency
• Decision Support Tool§ Economics of equipment sizing
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Project Outcomes
CLAAS OmahaMaury Salz
University of Nebraska LincolnDr. Santosh Pitla
Dr. Joe Luck
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Special Thanks
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Thank You
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Grain Harvest Logistics Modeling
• Loop
For w < 2r, theta is given by: 𝜃 = cos'( )*+
𝐿𝑜𝑜𝑝𝑇𝑢𝑟𝑛𝑑𝑖𝑠𝑡𝑎𝑛𝑐𝑒 = 𝜋 ∗ 𝑟 ∗ (𝜃 + 90)
90 + ∆𝑦
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Turns
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Pass Generation from Planter Path Files
• U-Turn
𝑈_𝑇𝑢𝑟𝑛𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 = F𝑑GH
�
G∈H
+ ∆𝑟𝑖 + ∆𝑟𝑗
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Turns
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Simulation
Min ∑Harvester_Operation_Time
With respect to:
1) Unload Auger Position2) Bin Capacity 3) Boundaries4) Grain Cart Cycle Time
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Optimization Equation
1 2 4 5 3 7 6 8 11 9 10 14 15 13 12 16 17 18 19 20↓↑↑↓ ↓↓↑↑↓↓↑↑↓↓↑↑↓↑ ↓ ↑
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Project Objective/ Scope
1 2 4 5 3 7 6 8 11 9 10 14 15 13 12 16 17 18 19 20↓↑↑↓ ↓↓↑↑↓↓↑↑↓↓↑↑↓↑ ↓ ↑
Scenario Analysis§Change Machine Parameters§Re-run Harvest Simulation §Compare
- Time to Complete- Field Efficiency - Limiting Factors