Yield monitoring and mapping

Transcription

1 Understanding, Analyzing, and Decision Making from mapped Yield Data Raj Khosla Colorado State University Monfort Professor of Precision Agriculture Yield monitoring and mapping by far the most popular precision agriculture practice provides invaluable information on both, scale and location of variability Prof. R. Khosla CSU 1

2 > average ~ average < average well < avg. Yield monitoring and mapping process of yield mapping calculating instantaneous yields eliminating errors in yield map transforming multiple years of yield data into decision maps Prof. R. Khosla CSU 2

3 What is Yield Monitoring? Process of measuring and mapping yield in real time Quantifies spatial and temporal variability in soil and crop properties Image source: CLAAS Agrosystems Yield mapping includes: Acquisition Analysis, and Summarization of crop yield data by location within a field d Monitoring Products/Yield%20Monitoring%20Main.jpg Prof. R. Khosla CSU 3

4 Definition of yield map A yield map is a spatially referenced, graphical representation of crop yield for a defined area. Final product is usually a tonal or colored map displaying ranges of yield within a field. Lat: Lon: Yield bu/ac 205 to to to to 185 Yield monitors provide a way to quantify yield variations that producers know exist Prof. R. Khosla CSU 4

5 How is yield Grain flow captured sensor instantaneously? GPS receiver Moisture sensor Family of sensor working together to measure yield Display console Header sensor (switch) Ground speed sensor Yield monitor components Header switch: Yield map GPS point Yield pixel Prof. R. Khosla CSU 5

6 Yield monitor components Ground Speed Sensor: Combine speedometer Ground speed sensor 10 km/h In slippery terrain An ultrasonic ground speed sensor Grain flow sensor Prof. R. Khosla CSU 6

7 Impact Force Sensor: Grain flow is measured by quantifying the amount of force applied when the grain strikes on a spring-loaded impact plate. Impact Plate Yield Force is measured by a load cell that converts load into electrical signal. A very light impact on the plate causes a measurable change in the resistance of the electrical current flow Change in electrical flow is proportional to amount grain flow Plate Displacement Sensor: Similar to impact force sensor. Measure displacement of plate and not force. Grain flow is measured by quantifying the displacement of the plate when the grain strikes on a spring-loaded impact plate. Displacement Plate Yield Prof. R. Khosla CSU 7

8 nmassflowsensorvideo.webm Moisture sensor It is a Capacitance type sensor, works on the principle of measuring di-electric property of a substance (Grain). Di-electric constant of water = 78 soil ~ 4 vacuum ~ 1 Prof. R. Khosla CSU 8

9 GPS receiver Grain flow sensor Moisture sensor Display console Header sensor (switch) Ground speed sensor Calculating Grain Yield on-the-go Prof. R. Khosla CSU 9

10 Calculating grain yield on-the-go To determine instantaneous crop yield need 3 pieces of information: 1. Grain flow rate (lbs/sec) 2. Combine s travel speed (ft/sec) 3. Cutting width of header (ft) Speed Width of header Calculating grain yield on-the-go Calculate instantaneous grain yield of a combine with an 8 row corn header harvesting at a speed of 5 miles per hour. The corn row width is 30 and the grain flow sensor recorded a flow rate of 44 lbs/sec for the previous second. Recall: 1mile = 5280 ft 1 hr = 3600 sec 1 acre = ft Prof. R. Khosla CSU 10

11 Calculating grain yield on-the-go 1) Area harvested Combine is moving at 5 mph Calculating grain yield on-the-go 1) Area harvested Combine has 8 rows of Yield pixel Prof. R. Khosla CSU 11

12 2) Volume harvested in bushels Grain flow sensor recorded a flow rate of 44 lbs/sec ) Measure yield in bu/a , ft 2 in 1 sec Yield pixel bu in 1 sec Prof. R. Khosla CSU 12

13 Cleaning yield data Prof. R. Khosla CSU 13

14 Cleaning yield data Raw yield map contains several data points with eroneous yield information. Out of range Outliers DGPS problems Speed change Lag error Raw yield map needs to be cleaned Cleaning yield data The raw crop yield data need further processing to provide the user with information that is more useable accurate. Prof. R. Khosla CSU 14

15 Cleaning yield data Table format Each line column is one contains point a different piece of information for that point STEP 1 Sort Cleaning yield data Loss of differential GPS fix Prof. R. Khosla CSU 15

16 STEP 2 Sort Cleaning yield data Removing low yield STEP 2 Cleaning yield data Removing high yield Prof. R. Khosla CSU 16

17 STEP 3 Cleaning yield data Combine speed variation error STEP 3 Speed (mph): Yield (bu/a): Cleaning yield data Combine speed variation error For cleaning, remove speed that is + or 25% of the median Area harvested in 1 second Prof. R. Khosla CSU 17

18 Cleaning yield data STEP 3 Combine speed variation error For cleaning, remove speed that is + or 25% of the median STEP 3 Cleaning yield data Combine speed variation error Speed (mph) Yield (bu/a) Prof. R. Khosla CSU 18

19 Cleaning yield data STEP 4 Mass Flow Delay Errors The mass flow delay is the time required for the grain to move from the header to the grain flow sensors. Edge of Field Cleaning yield data STEP 4 Mass Flow Delay Errors The mass flow delay is the time required for the grain to move from the header to the grain flow sensors. New x = UTM_X sin(heading in degrees) x Delay Distance in meters New y = UTM_Y cos(heading in degrees) x Delay Distance in meters Prof. R. Khosla CSU 19

20 Result of the Cleaning Process Summary Original data set: 46,348 data points Cleaned data set: 40,281 data points 13% of the points were removed Making Sense of Yield Data Prof. R. Khosla CSU 20

21 Average yield Y1: 193 bu/ac 41c, 117r 322c, 53r Year 12 3 Y2: 225 bu/ac Y3: 180 bu/ac Y4: 217 bu/ac Y5: 204 bu/ac Y6: 252 bu/ac Y7: 205 bu/ac Average yield Y1: 193 bu/ac Y2: 225 bu/ac Productivity potential of the field Y3: 180 bu/ac Low Y4: 217 bu/ac Medium Y5: 204 bu/ac High Y6: 252 bu/ac Y7: 205 bu/ac Prof. R. Khosla CSU 21

22 Normalized Yield (% max) Normalized Yield (% max) Clustering the yield data Average +/ bu 0-33% Equal parts 33-66% % Index Prof. R. Khosla CSU 22

23 Number of zones based on dataset? Generating zones zones Optimal number of zones varies Black box generates zones Are all zones significant? Prof. R. Khosla CSU 23

24 Zones must improve precision management Siginificant difference in yield Significant difference in soil fertility 41c, 117r 322c, 53r P What is the effect of soil properties on yield? K N Yield Prof. R. Khosla CSU 24

25 Colorado State University Thank you Prof. R. Khosla CSU 25