USING NEW TECHNOLOGIES TO VALIDATING CROP CUTTING EXPERIMENTSProf. Michael MannDept. GeographyGeorge Washington U

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PresenterPresentation Notes

Calculating Actual Yields

Conceptually very simple

Crop cuts consistently found to be as biased as farmer predictions

Percentage error by type

Satellite PlatformsNew moderate to high resolution satellite data are available:

New microsatellites 3-5m resolution

New 30m resolution satellites

Collect wide range of properties Visible light Infrared Thermal

Micro Satellites 3-5m resolution, Daily

PresenterPresentation NotesLandsat, plant labs, sentinal

Drone/Plane PlatformsDrones powerful, flexible, but expensive and computationally intensive Provide platform for specialty instruments Thermal Infrared Lidar

Costs/computation time decreasing rapidly

Mobile PlatformsNew low cost tools can be used to collect data on:

Plant health Cell phone imagery

Plot data through mini surveys Planting/Harvest dates Input use Weather Disease/pests

Farmer directly linked More efficient random

sampling possible

Sources of Crop Cut BiasThere are a variety of sources of bias introduced into crop cuts by enumerators

Measurement problems1. Inappropriate use of tools (scales, poor records etc)2. Failure to account for disease that make unharvestable3. Non-random or biased location of test plots

Enumerators avoid low-yield areas4. Failure to account for ripening or harvest over time5. Lack of accountability for enumerators

Problem: Measurement ErrorPlant Characteristics

New (and cheap) ground based LIDAR can quickly estimate: Row spacing Plant density Plant height / biomass Lodging Sowing method

Importantly measurements could be taken rapidly in multiple locations in field

Ground based LIDAR examples:

Problem: Measurement ErrorHead properties After hand threshing cell phone cameras and machine learning can be use to:

Flag potential disease / damage

Count grains Count heads Crop stage

Flowering/ripening etc

Problem: Timing of crop cut

Harvest dates can be estimates via satellite

Harvest dates could be used to correct for timing of crop cut

Harvest Date

15/5/16

20/5/16

25/5/16

30/5/16

05/6/16

10/6/16

Problem: Lack of accountabilityMobile automated geotagged records can improve accountability and ensure methods Verify timing Improve measurement Verify spatial sampling Confirm interaction with farmers

?provide automated feedback to farmers?

Vegetation IndicesNDVI and EVI Greenness Indexes

Vegetation indices are used to monitor vegetation conditions, land cover, land cover changes, and primary production. These data may be used as input to model global biogeochemical and hydrologic processes and global and regional climate.

Vegetation Indices Responsive to amount of chlorophyll, leaf area, canopy structure

Healthy or stress plants can be easily identified via satellite or drone

Problem: Plant health after cutPlant health can be monitored via vegetation indices, or through weather Adjustments to yield estimates can be made to include disease, water stress etc after crop cuts.

Problem: Biased location of test plots

Vegetation Indexes can be used to stratify sampling

Strata based on crop stress groups

Area of each strata can be calculated from imagery

Also better accounts for staggered ripening and harvesting

Low High

Med

Problem: Translating data to yields/losses

Machine Learning With high quality and diverse training data machine learning can integrate data from:

Remote sensing Ground LIDAR Weights/measures Cellphone Traditional crop cut Questionnaires Enumerator quality

Yield/Loss Estimate

Issue: Challenge of Training Data Ground Truth Data

Essential and Largely Missing for Public Local, contextual ground truth data is going to be required What is planted, where and

when? Management practices Plot level yields, crop cuts Pests, disease Farmer impressions of loss

Other Data of InterestIn-the-field capture of tenure rights by communities and individuals using mobile devices. Existing tenure data, aerial and satellite imagery can be cached on the device to support data capture in areas with no internet connectivity.

Other Data of Interest Open source version of google maps

Anyone can add/edit the global base map

Map plots, farms etc Getting major support as global base map for unnamed competitors of google.

Open Street Map

Other Data of Interest

Allows for field data capture without internet

Basemaps are printed, edited, and scanned back to openstreetmaps.org

Walking Papers

Issue: Alternative yield estimatesResearch Question To what degree can we

accurately estimate wheat yields for a location over time?

Broader Project Objectives1. Estimate wheat yields at a

variety of temporal and spatial scales

2. Develop scalable algorithms, with an eye towards using high resolution imagery in the future

Mann, M. L., & Warner, J. M. (2017). Ethiopian wheat yield and yield gap estimation: A spatially explicit small area integrated data approach. Field Crops Research, 201, 60-74.

Summary Statistics Compressing Time

Properties of a growing season can be summarized in a

variety of ways

Gre

ener

Wheat Rice Wheat Rice Wheat

Summary Statistics Maximums, Means etc

Values change each season reflecting

growing conditions

Gre

ener

Wheat Rice Wheat Rice Wheat

Gre

ener

Summary Statistics Area Under the Curve (AUC)

Persistence and intensity of greeness

Wheat Rice Wheat Rice Wheat

Gre

ener

Summary Statistics Comparisons to Quantiles

How does this year compare to the best

years?

Wheat Rice Wheat Rice Wheat

Visualizing Model Performance

Within R-Squared: 0.67

PredictedActual

The Take Away

Aggregated across districts NDVI by itself can reasonably predict wheat yields over time.

Can these tools be applied at the plot level?

Plant density plant spacing

Evenness of the field X Number of heads Area measurements Head length # grains per head Grain weight / size Crop maturity

Assess soil moisture immature crops Satellite can calculate days until harvested

After hand threshing, cellphone cameras with machine learning can: Count kernels Flag potential disease

1 m horizontal resolution0.1m vertical

A developmental main stage when yellow anthers are clearly visible on spikes. It is also called flowering. Each florets lemma and palea are forced apart by swelling of their lodicules, which allows the anthers to protrude. After a day or two, the lodicules collapse and the florets close again. In some circumstances, florets may never show the anthers. When anthers are sterile, as may occur in low-boron soils, the florets may stay open for days, or until cross-pollination occurs.

The Possibility of Training DataNew low cost tools can be used to collect data on:

Plant health Cell phone imagery

Plot data through mini surveys Planting/Harvest dates Input use Weather Disease/pests

Farmer directly linked Impressions of loss Mechanism for making a claim

As far as I am concerned, this changes everything.

Future Directions for Remotely Sensed Data Machine Learning &

Computer VisionData from satellites, cellphones, stationary cameras, networked sensors.

Monitor: Yields Plant growth, height Pest / Disease Irrigation systems Weed management Row spacing

Vegetation IndicesNDVI and EVI Responsive to amount of

chlorophyll, leaf area, canopy structure

Vegetation indices are used to monitor vegetation conditions, land cover, land cover changes, and primary production. These data may be used as input to model global biogeochemical and hydrologic processes and global and regional climate.

Using new technologies to validating crop cutting experiments Calculating Actual YieldsSatellite PlatformsDrone/Plane PlatformsMobile PlatformsSources of Crop Cut BiasProblem: Measurement ErrorPlant CharacteristicsGround based LIDAR examples:Problem: Measurement ErrorHead properties Problem: Timing of crop cutProblem: Lack of accountabilityVegetation IndicesVegetation IndicesProblem: Plant health after cutProblem: Biased location of test plots Problem: Translating data to yields/lossesIssue: Challenge of Training DataOther Data of InterestOther Data of InterestOther Data of InterestIssue: Alternative yield estimatesSummary Statistics Compressing TimeSummary Statistics Maximums, Means etcSummary Statistics Area Under the Curve (AUC)Summary Statistics Comparisons to QuantilesVisualizing Model PerformanceSlide Number 27Slide Number 28Slide Number 29Slide Number 30Slide Number 31Slide Number 32Slide Number 33Slide Number 34Slide Number 35Slide Number 36Slide Number 37Slide Number 38Slide Number 39Slide Number 40Slide Number 41Slide Number 42Slide Number 43Slide Number 44Slide Number 45Slide Number 46Slide Number 47Slide Number 48The Possibility of Training DataFuture Directions for Remotely Sensed DataVegetation IndicesSlide Number 52