The document discusses using new technologies like satellites, drones, mobile devices, and machine learning to improve the accuracy of crop yield estimates and validation of crop cutting experiments. It outlines how various remote sensing platforms and sensors could be used to collect high-resolution plant characteristic data on factors like plant height, density, and health. Computer vision techniques on cell phone imagery could also count grains and flag disease. The data collected could help address sources of bias in traditional crop cuts and potentially generate more accurate yield estimates and loss assessments through machine learning models trained on the diverse data sources.

1. USING NEW TECHNOLOGIES
TO VALIDATING CROP
CUTTING EXPERIMENTS
Prof. Michael Mann
Dept. Geography
George Washington U
michaelmann.i234.me/wordpress

2. Calculating Actual Yields
• Conceptually very simple
• Crop cuts consistently found
to be as biased as farmer
predictions
Percentage error by type

3. Satellite Platforms
New 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

4. Drone/Plane Platforms
Drones powerful,
flexible, but expensive
and computationally
intensive
• Provide platform for
specialty instruments
• Thermal
• Infrared
• Lidar
• Costs/computation
time decreasing rapidly

5. Mobile Platforms
New 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

6. Sources of Crop Cut Bias
There are a variety of sources of bias introduced into crop
cuts by enumerators
Measurement problems
1. Inappropriate use of tools (scales, poor records etc)
2. Failure to account for disease that make unharvestable
3. Non-random or biased location of test plots
• Enumerators avoid low-yield areas
4. Failure to account for ripening or harvest over time
5. Lack of accountability for enumerators

7. Problem: Measurement Error
Plant 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

8. Ground based LIDAR examples:

9. Problem: Measurement Error
Head 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

10. 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

11. Problem: Lack of accountability
Mobile 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?

12. Vegetation Indices
NDVI 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.

13. Vegetation Indices
• Responsive to amount
of chlorophyll, leaf
area, canopy structure
• Healthy or stress
plants can be easily
identified via satellite
or drone

14. Problem: Plant health after cut
Plant 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.

15. 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

16. 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

17. 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

18. Other Data of Interest
In-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.

19. 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

20. Other Data of Interest
• Allows for field data
capture without
internet
• Basemaps are printed,
edited, and scanned
back to
openstreetmaps.org
Walking Papers

21. Issue: Alternative yield estimates
Research Question
• To what degree can we
accurately estimate wheat
yields for a location over time?
Broader Project Objectives
1. 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.

22. Summary Statistics – Compressing Time
Properties of a
growing season can
be summarized in a
variety of ways
Greener
Wheat Rice Wheat Rice Wheat

23. Summary Statistics – Maximums, Means
etc
Values change each
season reflecting
growing conditions
Greener
Wheat Rice Wheat Rice Wheat

24. Greener
Summary Statistics – Area Under the
Curve (AUC)
Persistence and
intensity of greeness
Wheat Rice Wheat Rice Wheat

25. Greener
Summary Statistics – Comparisons to
Quantiles
How does this year
compare to the best
years?
Wheat Rice Wheat Rice Wheat

26. Visualizing Model Performance
Within R-Squared: 0.67
Predicted
Actual
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?

30. • 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

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

32. 1 m horizontal resolution
0.1m vertical

33. • A developmental main stage when yellow anthers are
clearly visible on spikes. It is also called ‘flowering’. Each
floret’s 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.
•

49. The Possibility of Training Data
New 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.

50. Future Directions for Remotely Sensed
Data Machine Learning &
Computer Vision
Data from satellites,
cellphones, stationary
cameras, networked sensors.
Monitor:
• Yields
• Plant growth, height
• Pest / Disease
• Irrigation systems
• Weed management
• Row spacing

51. Vegetation Indices
NDVI 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.