AgAI engr 245 lean launchpad stanford 2019

Our Lean Launchpad Journey
Then Now
Optimizing nutrients for
orchards using computer
vision
Crop counting for
specialty crops
AND
Precision weeding for
corn and soy

Alex Kolchinski Rachel Luo Sami Tellatin Jonathan Kuck Edward Silva
PhD Candidate,
Learning Sciences
& Tech Design
PhD Candidate, EE
Stanford Vision
Lab, AI Lab
MBA 20’
Stanford GSB
PhD Candidate, CS
Stanford AI Lab
MBA 20’
Stanford GSB
Helping farmers know more of what they grow
Mentor:
Todd Basche
129 Interviews Total

Almond Board workshop in
Central Valley
1
2
3
4
5
6
7
8
9
10

AgEmerge: Growers are confident
in their current practices.
1
2
3
4
5
6
7
8
9
10

1
2
3
4
5
6
7
8
9
10
Almond crop count is important,
but farmers can play the market.

1. Specialty crop
farmers and/or
farm managers
2. Specialty crop
farmers with
perishable, multi-
harvest per-
season crops.
3. Specialty crop
farmers with
non-perishable,
single-harvest
per season
crops.
1.Trust: Ongoing data
analysis that
produces reliable,
usable data.
2.Insight: Analysis tools
for strategic
management.
2
4
1.Farmer relationships.
2.Proprietary hardware
for data collection.
3.Proprietary software for
crop counting.
4.Agricultural+AI SME.
6
1.Sell analysis via platform for a subscription
OR
2.Charge a % of revenues (to be proved out)
1.Development costs of technology, including testing.
2.Hardware and prototyping costs.
3.Customer acquisition costs, especially upfront.
1.Reduced costs
through targeted
water/fertilizer
application).
2.Increase
captured revenue
through optimized
pricing.
3.Reduce labor
costs previously
needed to
count/estimate
crop count.
1.Development of AI for
image recog. &
counting of crops
2. Analysis of crop
count for price
optimizing.
3.On-demand data
collection.
1.USDA and public
university extension (i.e.
UC Davis Extension).
2.Certified Crop
Advisors (example).
3.Specialty crop
farmers*
4.Industry Associations
(i.e. Almond Board of
California). 1.Direct (trade shows)
2. Through crop
advisory board
workshops/monthly
grower meetings
3. Top down from
farm contract holders17
8
3
5
9

1
2
3
4
5
6
7
8
9
10
Berry farmers harvest every acre
3x per week, 8 months per year.

Largest global berry company in
the needs better forecasting.
“The biggest tech opportunities Driscoll’s sees are
automatic harvesting and yield prediction. Yield
forecasting right now is done manually and is very
speculative and error prone. Small errors can shift
large amounts of revenue.”
-Michael Christensen, R&D Director, Driscoll's
1
2
3
4
5
6
7
8
9
10

1
2
3
4
5
6
7
8
9
10
Not all berry counting is desired equally,
and size of the farm/shipper matters.

Better crop counting can help avoid money
being ‘left on the table.’
1
2
3
4
5
6
7
8
9
10
“Last week alone, based on one forecast, we lost $20k on
a 100 acre farm because we manually overestimated yield
after a bad storm. Had we estimated correctly, it would
have been a different story.”
~David Lawrence, President, Red Blossom Berries

TAM of Crop Counting in Berries
Globally
● $15-60m per year
● Doesn’t include other specialty crops
(i.e. Processing tomatoes: 12m acres)
Strategy:
● Get a prototype into the field.
● Win for this customer.
● Iterate, then expand to new markets.
12/16 Berry Interviews said yes to crop
counting, including 4 biggest companies.
Zoom in & dominate for your customer in
a smaller market first, then expand.
1
2
3
4
5
6
7
8
9
1
2
3
4
5
6
7
8
9
10

Zoom in and zoom out in parallel:
Specialty & Row Crops
Zoom Out & ExploreZoom In & Dominate
1
2
3
4
5
6
7
8
9
10

We start exploring bigger markets
Hypotheses: we can add
value with computer vision
for
● Plant breeding
● Winemaking
● Corn and soy
(aerial/satellite imaging?)
1
2
3
4
5
6
7
8
9
10

0/4 winemakers could quantify revenue
benefit of yield prediction; costs low.
Yield prediction is a significant
problem for Foley [a major
winery]...but only spending
$50,000/yr on predictions.
-Al Wagner, VP Vineyards, Foley
1
2
3
4
5
6
7
8
9
10

World Ag Expo: More learning in one day
than in most other weeks!
1
2
3
4
5
6
7
8
9
10

Out of dozens of farmers we asked, none
said that better aerial data was a top need.
Nobody wants to pay for information!
-Dennis Donohue: Innovation Lead, Western Growers
1
2
3
4
5
6
7
8
9
10

But 6/6 corn and soy growers indicate
weeds either #1 concern or very important.
“Our biggest problem -
weeds!”
-Steve and Connie Swan,
Conventional Corn and soy
farmers
1
2
3
4
5
6
7
8
9
10

A product vision emerges...
1
2
3
4
5
6
7
8
9
10
The robot:
● Autonomous
● Jets of fire, not herbicide
● Computer vision for targeting
● Cameras and nozzles mounted on vertical bars to
penetrate below leaf canopy

And reception is very favorable
“A [non herbicide weeding robot]...
will be the ultimate gold standard greatest creation
in production agriculture that we’ll ever see”
-Justin Bruch, AgFunder Board Chairman;
Major corn and soy grower
1
2
3
4
5
6
7
8
9
10

It looks feasible, too.
“A works-like prototype of the weed
burning robot will take $2-3000 to build”
-Dr. Andrew Gillies, roboticist
1
2
3
4
5
6
7
8
9
10
Autonomy on a farm is a much easier
problem than on streets
-Russell Kaplan, Sr. Research Scientist
at Testla Autopilot

Total and beachhead market sizes are
promising
Weeding market size (US):
● Conventional corn and soy: $40/acre *
180M acres = $7.2B/yr
● Organic corn and soy: $150/acre * 1M
acres = $150M/yr.
a. Growing exponentially!
1
2
3
4
5
6
7
8
9
10

2/2 organic farmers have already signed up
for the paid pilot at $20/acre.
You’re singing our song - sounds like a dream come true!
Sign me up for the paid pilot!
-Jim Wirtz, Organic Corn and Soy Grower (6000 acres)
1
2
3
4
5
6
7
8
9
10

1. Organic corn
and soy growers
2. Conventional
corn and soy
growers
1.Initially: direct sales
and support
2
4
1.Farmer and weed
control specialist
relationships.
2.Proprietary hardware
3.Proprietary software
6
1.Sell robots per-unit and charge for service
OR
2.Charge per acre as a service
1.Development costs of technology, including testing.
1.Reduced
weeding cost
2.“Automatic
organic”
1.Hardware platform
development and
manufacturing
2. AI software
development
3.Farmer facing sales,
support and testing
1.Manufacturers
2.Ag supply dealerships
3.Weed control
specialists
4.University test farms,
field days etc.
1.Direct sales and
trade shows
2. Ag programs at
universities
3.Weed control
specialists
17
8
3
5
9

What is next
Crop counting as a beachhead
to Ai in specialty crops.
“Fire Roomba” for weeds;
Alex Full Time.
If you know a good
roboticist/mechanical
engineer, please reach out!
yakolch@stanford.edu

Appendix/Extra
Slides are below this
slide

Clone market type
now focused on specialty crops. [not exclusively]
Week 8: 10 interviews this week. 114 total
Picker Hacker Hustler Hacker Hustler
PhD Candidate,
Learning Sciences &
Tech Design
PhD Candidate, EE
Stanford Vision Lab,
Stanford AI Lab
MBA1, Stanford GSB PhD Candidate, CS
Stanford AI Lab
MBA1, Stanford GSB
Mentor:
Todd Basche

Current Status
For large growers and distributors of berries & wine grapes, who
seek to optimize pricing via more accurate and lower cost
forecasts, AgAi is a computer vision system that counts crops to
determine how many will be ready for each sales cycle at multiple
periods before harvest.
Unlike our competitors, we don’t guess, we count!
Leading crops of focus: wine grapes, berries, lettuce, broccoli

Who we talked to:
5 Agriculture technology experts | 1 VC in agtech |
1 Winemaker | 3 Agtech startup founders
What we expected:
Satellite imagery would have limited uses in providing farmers
interventions and not just more information, which they are
overloaded with.
What we learned:
Satellite images are not quite good enough today to provide
much info beyond crop ID and some yield prediction for some
crops.
However, satellite images seem to be improving rather
dramatically, to the point where they may be challenging the
need for sensors.
Robotics is the most promising avenue.
Interviews & Insights
“Wherever there is a field trial in
ag, there is the need for more
efficient expert eyes. If
computer vision could help with
this, we could do more with
less.”
--Jonathan Diniz
Manager, TS&L Seeds
“Farmers are overloaded with
information and data. You must
make it useful and tied to an
ROI.”
-- Dennis Donahue,
Leader of Western Growers
Innovation

Potential Partners
Forecasters Industry Associations
Why do we
need them?
Major influence on farmers prices
Event hosters, want to showcase
products at their events
Why might
they need us?
Allow them to be more
efficient/accurate
They provide growers data; we could
make that more accurate
Risks
May see our technology as
adversarial
Lack of trust
Costs
Initial upfront costs for hardware,
plus processing costs
Buying booth space at events

7
8
1 3
45
1.Distributor/farmer
relationships.
2. Hardware for data
collection.
3. Software: crop counting.
4. Agricultural+AI SME.
9
1. Development costs of technology, including testing.
2. Hardware and prototyping costs.
3. Customer acquisition costs, especially upfront.
4. Operating costs to continuously collect and process data.
1.Development of AI
platform for image recog.
& counting of crops
2.Analysis of crop count
for price optimization.
3.On-demand data
collection.
1. USDA and public
2. Certified Crop
Advisors (example).
3. Specialty crop
farmers*
4. Industry Associations
(i.e. California
Strawberry
Commission).
5. Technical
partnerships (i.e.
robotics and agricultural
data)
1. Provide an in-field service of computer vision=crop count
2. Sell analysis via platform for a subscription
3. Charge a % of revenues gained (TBD)
1.Direct (trade shows)
2.Crop-specific advisory
board workshops/
grower meetings
3.Top down from farm
contract holders i.e.
distributors
1. Trust: Ongoing data
analysis that
produces reliable,
usable data.
2. Solution= easy
AgAi Week [5]
1. Berry distributors
who need to set
prices before harvest
time.
2. Berry farmers with
perishable, multi-
harvests-per -
season crops.
3. Vegetable distributor
with per unit crops
(i.e. lettuce and
broccoli) that harvest
all at once.
1.Increase captured-
revenue through
optimized pricing.
2.Increase quality by
more optimal harvesting
time.
3.Reduce labor costs
previously needed to
count/estimate crop
count.

What we are trying to figure out before
class ends...
1. Build a list of exactly who would pay for more accurate crop counting
and why
2. Can satellites imagery do what we want to do cheaper/easier and
would people pay for that from us
3. Which 3 competitors offer the closest value proposition, and what do at
least 3 current users think about it.
4. How much value add is there to an autonomous scout+smart sprayer?
5. How hard will it be to design and build an autonomous scout+smart
sprayer?

AgAi: LLP Exploration Map
Weeks of LLP
PhysicalLevelofPlant-Data-Capture
Drone/ Satellite-
Level
Perspective
Plant-level perspective
PotentialMarketSize
+
-

1. Interview actual in-
field berry/grape
forecasters
Next Steps: Before Next Class, We
Will...
2. Send
grower/shipper our
understanding of
their ROI on better
forecasting.

1. Growers and distributors of continually-harvested produce experience high error rates in crop
count estimates.
2. Errors in crop count estimates cause errors in yield prediction, which have a significant and
measurable economic impact for growers and distributors of continually-harvested produce.
3. Large business size, short produce shelf life, and continuously harvested nature of a crop are the
key factors in making precise crop counting compelling to growers and distributors.
4. Growers and distributors of continually harvested produce incur significant and measurable labor
costs when making crop count estimates.
5. We could build a system at reasonable cost that significantly outperforms the current approaches
distributors/growers of continuously harvested produce use for making crop count estimates.
6. Wine grape growers would benefit from a pricing standpoint if they had more accurate yield
counts.
Next Week: Test These Hypotheses...

And Finally, Make This Matrix Full...
Modified Wish List Driscolls California
Giant Berry
Farms
Naturipe Well-Pict Church
Brothers
Greens
Babe
Farming
Mazzi
Farming
Veg Crop
Company X
Director, Technical Services, Global
Research and Development
x x X
Director of Forecasting x x
Forecasting Analyst x
In-Field Forecaster
Sales Person
Shippers (if not the same company) x x x
Growers (x5) xxxx xxx

● Seek roboticists to help develop mechanisms to move camera through fields.
● Recruit Certified Crop Advisor/Forecaster in our specific crop as an advisor,
have them lead sales team development.
● Development of AI platform for image recog. & counting of crops.
● Analysis of crop count for price optimization.
● On-demand data collection (unless partnering with imagery companies).
Key Activities
Product
Development
Customer
Development
& Selling
● Create sales channel with forecasting, pricing, harvesting teams [need to
decide on one].
● Build pilots to prove out the concept with shippers/growers.
● Create marketing campaign around more accurate forecasting insight.
Internal
Capacity
Building
● Early sales from initial customers.
● Pre-seed funding to prove this out to different crops.Fundraising

Key Resources & Partners
Physical ● [short term] Stanford Venture Studio
Software
● Cloud computing
● Machine learning libraries
Human ● Machine learning researchers and engineers
● Potentially roboticists/mechanical engineers
IP
● Brand/trademark/logo
● Machine learning models and data
Partners ● Ag dealerships for distribution, support and repairs

Income Statement
2020 2021 2022
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
Active Acres 5000 7500 11250 16875 25312.5 37968.75 56953.125 85429.6875 128144.5313 192216.7969 288325.1953 288325.1953
New Acres 0 2500 3750 5625 8437.5 12656.25 18984.375 28476.5625 42714.84375 64072.26563 96108.39844 0
Forecasting Fee p/acre $35 $35 $35 $35 $35 $35 $35 $35 $35 $35 $35 $35
Gross Revenue $350,000 $525,000 $787,500 $1,181,250 $1,771,875 $2,657,813 $3,986,719 $5,980,078 $8,970,117 $13,455,176 $20,182,764 $20,182,764
Net Revenue $350,000 $525,000 $787,500 $1,181,250 $1,771,875 $2,657,813 $3,986,719 $5,980,078 $8,970,117 $13,455,176 $20,182,764 $20,182,764
Product Dev Costs $250,000 $250,000 $250,000 $250,000 $250,000 $250,000 $250,000 $250,000 $250,000 $250,000 $250,000 $250,000
Operating Costs $45,000 $54,000 $64,800 $77,760 $93,312 $111,974 $134,369 $161,243 $193,492 $232,190 $278,628 $334,354
Cash Burn -$295,000 -$304,000 -$314,800 -$327,760 -$343,312 -$361,974 -$384,369 -$411,243 -$443,492 -$482,190 -$528,628 -$584,354
Cash Balance $645,000 $829,000 $1,102,300 $1,509,010 $2,115,187 $3,019,787 $4,371,088 $6,391,321 $9,413,609 $13,937,366 $20,711,392 $20,767,117
Assumptions:
Starting Acres 5000
Growth Rate 50%
Number of forecasts per yr p/acre 2
Operation cost growth q/q 20%
Price per acre $35

Nonetheless, our main focus remains:
Crop Counting

Diagram of Crop Counting
Per acre crop
count of $35
Initial hardware
cost of $TBD
+
Amount saved/revenue
realized by Driscoll’s:
TBD

Fundraising & Operations Plan
Q1 Q2 Q3 Q4
2019 2020 2021 2022
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
Cashreserves
5M
10M
20M
30M
OperationsSoftware/
Hardware
development
Lean
Launch
pad
Build hardware
prototype
Test hardware & software
prototype
Disc.
PMF
TBD
Analyze
Results
Acquire
training
set for ML
Field test
software
Partnerships
with large
shippers, pilot

Will...
2. Current Yield
Prediction Accuracy
1. Financial Model for
Our Potential
Business Model
3. Evaluate
opportunity size
of phenotyping

One harvest per season per crop Multiple harvests per season per crop
Generally non-
perishable
product
i.e. time to market
is less crucial
● Almonds
● Corn
● Processing tomatoes
● Rice
● Raisins
● Wine grape growers
● Pomegranates
● Cannabis
Generally
perishable
product
i.e. time to market
is crucial
● Bell peppers
● Stone fruit
● Potatoes/onions
● Berries
● Broccoli
● Lettuce
Low willingness to pay High willingness to pay
BOLD=interviewed
Last week on AgAi...Revisit
Customer Segments: Specialty Crop Farmers
Where we are
exploring:
Crop Curious:
● Corn
● Cannabis
● Soy
● Wine Grapes
Task Curious:
● Phenotyping
● QA
● BRIX counting

LEARNING GOAL SCORE
R/Y/G
COMMENTS EXPLAINING YOUR SCORE AND/OR
WHAT YOU WILL CHANGE
1. Form hypotheses Past: Have created a wide range of hypotheses
Future: Will be more focused
2. Design and conduct customer, partner, and
supplier interviews
Past: Quantity has been high
Future: Moving towards conducting interviews in a repeatable, scientific manner.
3. Design and build minimum viable products
(MVPs)
Past: Iterated on MVPs that illustrate the concept
Future: Building functional demos
4. Design and run experiments Past: During need finding, interviews resembled conversations more than experiments
Future: Will run repeatable experiments as we move past need finding
5. Determine if hypotheses are true or false based
on interview and experimental results
Past: Have ruled out hypotheses using interview data
Future: Will utilize experimental results
6. Work effectively in teams under pressure Past: Teamwork has been effective
7. Communicate your progress in weekly
presentations
Past: Team communication through structured and informal presentations has been effective
8. Form new hypotheses based on learning Past: Have created new hypotheses after invalidating past hypotheses
9. Pivot (adapt strategy) based on new data Past: Pivoted from almonds to berries
10. Achieve (or know you have not achieved)
product-market fit
Past: Have successfully identified the lack of product-market fit
Future: Are working to achieve product-market fit
Learning Goals: Midpoint Self-Assessment

Input Suppliers
- Advisors, Agronomists, CCAs, PCAs
Landowners
Farm/Land Management Companies
Independent Advisors
University Extension Employees
USDA Field Staff
FARMERS
GROWERS
OPERATORS
PRODUCERS
Advisors, Input Suppliers
Information, Land, Inputs
Aggregators, Processors
& Distributors
Retail, Restaurants,
Consumers
Large commodity
traders, like
Cargill and ADM,
who often also
process and
distribute goods
from farmers
For strawberries,
Driscoll is a major
aggregator and
distributor
Aggregators/Processors also can be input
suppliers (like Cargill)
Capital Providers
Banks
Farm Credit Agencies
Crop Insurance Providers
USDA grants and subsidies
Some farmers do
sell direct to
restaurants or
consumers;
usually these are
players in
local/regional
economies

Customer Acq. Cost
Direct to Shipper
Maintenance2
$2.00 p/yr per
1000 acres
Estimate
Farm Trials1
$1500 p/1000
acres
$2500 p/1000 acres
Customer Acquisition Cost
Annual Subscription
Pay per acre per year for 1000 acre farm
A. YEARLY REVENUE
at $25 per acre free p/yr
$2500
B. YEARLY CHURN
2%
C. CAC
$1500
D. GROSS LTV
A*(1-B) / (B)
$122,500
E NET LTV
GROSS LTV - CAC $121,000
Customer LTV
● Pending questions:
○ Churn rate for agtech?
○ CAC for farm trials for new agtech

● Pending questions:
a. What could our technology do in
terms of reducing error rates?
b. Value added by AgAi per forecast?
c. Costs saved by AgAi per forecast?
d. The differences of strawberry value
saved/created vs. other berries?
e. Other benefits to making forecasters
more efficient/effective?
Unit Economics for Forecasting (in progress)
A. Cost of labor per acre to
forecast per year
$50
B. Berry acres in the U.S.
289,000
C. Current error rates for
Driscoll’s Forecasters
+30%
D. Acres “overseen” per
forecaster
~1000
E. Value added by AgAi per
forecast
TBD
F. Cost saved by AgAi per
forecast
TBD

Recurring Value
- Maintenance
- New insight into their data
- Cheaper the more you run the
technology
Berry Grower/Shipper Funnel
Workshops and Shows
- Farm shows
- Berry workshops
- Industry associations
Upsell
- Provide PCA services
- Sell additional tools
Referrals
- PCA/CCAs
- Shippers to growers
Acquire Activate Keep Customers Up-Sell Next-Sell
KEEP
Cross-Sell Referrals
GROWGET

One harvest per season per crop Multiple harvests per season per crop
Generally non-
perishable
product
i.e. time to market
is less crucial
● Almonds
● Corn
● Processing tomatoes
● Rice
● Raisins
● Wine grape growers
● Pomegranates
● Cannabis
Generally
perishable
product
i.e. time to market
is crucial
● Bell peppers
● Stone fruit
● Potatoes/onions
● Berries
● Broccoli
● Lettuce
Low willingness to pay High willingness to pay
Customer Segmentation:
Specialty Crop Farmers [hypothesis]
BOLD=interviewed

Channels: Strawberry Produce Seller
Direct (B2B) Fixed Revenue +
Variable Revenue
= Life-Time Value
(LTV)
$TBD
Channel Partner Interests:
● Working with Pest Control/Certified Crop Advisors
● Working directly with shippers/sellers/traders of product
Potential Revenue Model
Fixed Revenue Variable Revenue
Current variable costs to forecast: $30/ppl/hr x
10 hr/ppl/wk x 5 ppl
1% increased revenue from being able to sell
more produce at higher pre-committed prices
Total: $1500 per week x 7 months = $42k p/yr Total: TBD
$XYZ
Variable costs based
on size of farm
$ABC
Testing and farm trials
$XYZ
Profit

Our Value Proposition:
Increase revenue captured (by
optimizing price) and decrease labor
costs by counting crops that will be
ripe up to 6 weeks before harvest.
Customer #1:
Produce Seller
1. Customer Archetype
- Medium to large strawberry seller/distributor (US based)
- Needs to set price in advance of each harvest to sell, but
not oversell, each crop
2. Workflow Today
Plant strawberry → Monitor crop size and ripening approximately
with humans → 6 weeks before harvest, sign contracts for ~50%
of crop → Continue to monitor crop → Sell more in 3 weeks→
Monitor → Sell more in 1 week → Harvest and sell any surplus (or
deal with undersupply!)
3. Current Solutions for Crop Counts
- Human labor counts small sample of field, extrapolates
- Use historical data to predict strawberry count

Gains Creators
● Reducing costs.
● Increasing yields
Pains
● Yield uncertainty.
● High labor cost
● Potential yield not
captured.
Customer Jobs
● Decide how much
fertilizer/irrigation
to apply.
● Plan their
expenses 7-9
months ahead
based on the
potential of yield.
● Stay in the black
with unpredictable
cash flow.
● Develop contracts
with buyers based
on projected yields
Products / Services
● Crop counting for
yield prediction,
and cost reduction.
● Providing insight
on how much to
irrigate/fertilize.
Value Proposition: Customer Segment: Farmers
Value Proposition Canvas - Farmers
Gains
● Staying profitable.
● Visibility into crop
health to get ahead
of issues
● Reducing input
costs
Pain Relievers
● Providing yield
insight as early as
possible.
● Reducing
operational costs.

Products / Services
● Field crop
count/health
monitor
Pain Relievers
● Greater insight into
crop status
throughout season
Gains Creators
● Better data for
optimizing agricultural
decisions
● Better data for
optimizing marketing
decisions
Value Proposition: Customer Segment: Agronomists
Pains
● Accuracy is difficult
to obtain (in yield
estimates and
nutrient
recommendations),
leading to
uncertainty and risk
Gains
● Advice is accurate
and reliable
● Clients come back
and refer
● Clients trust you
Customer Jobs
● Advising farmer
clients on nutrient
application
amounts and
timing throughout
the season
Value Proposition Canvas - Agronomists

% of farmers that say yes
If you knew exactly how many fruits/nuts
were on your trees at any point in the year
that you wanted, would your operations
costs change?
Test 001: Crop Count
HYPOTHESIS
OUR TEST
METRIC
WE’RE RIGHT IF
Real-time crop count would allow
for significantly increased
optimization in operations costs.
>50% say yes
RESULT
TBD
NOW WHAT
[1] TBD
[2] TBD
Test Card
ONGOING

If you had perfect information about your soil
and plant moisture levels everywhere in your
spread all of last growing season, would you
have saved money on water/ increased yield?
Test 002: Crop Count
HYPOTHESIS
OUR TEST
METRIC
WE’RE RIGHT IF
The limiting factor in precision
irrigation/fertilizer is the right
hardware
>50% say yes
RESULT
TBD
NOW WHAT
[1] TBD
[2] TBD
Test Card
ONGOING

Over the past 5 growing seasons, has the
amount you’ve spent on pest
identification and control been a major
cost for you?
Test 003: Pest Control
HYPOTHESIS
OUR TEST
METRIC
WE’RE RIGHT IF
Farmers are currently experiencing
high costs from labor of identifying
pests and/or controlling them.
>50% say yes
RESULT
TBD
NOW WHAT
[1] TBD
[2] TBD
Test Card
ONGOING

Over the past 5 growing seasons, would
you have been able to significantly save
costs if you had real-time maps of tissue
sample data over your entire spread?
Test 004: Tissue sampling
HYPOTHESIS
OUR TEST
METRIC
WE’RE RIGHT IF
Farmers could significantly improve
their operations with data from
granular, fast tissue sampling.
>50% say yes
RESULT
TBD
NOW WHAT
[1] TBD
[2] TBD
Test Card
ONGOING

% of yield increase due to precision
fertilization or irrigation.
Find 5 real cost analysis/operation
budgets showcasing precision
fert./irrigation has led to more yield.
Test 001: Compelling Value Prop.
HYPOTHESIS
OUR TEST
METRIC
Perfectly targeted irrigation/
fertilization would increase yields/
decrease costs enough.
WE’RE RIGHT IF
Growers see average increase in
yield > X% per acre (X is TBD)
Test Card
RESULT
Haven’t been able to
find those cost analysis
of precision
irrigation/fertilization.
NOW WHAT
[1] Reach out to precision
fertilizer sales people.
[2] Reach out to hardware
manufacturers of precision
irrigation.
ONGOING

Ask suppliers and salespeople of
precision fertilizer solutions for %
market share of almond farmers.
% of the people that tell us that
>75% of their customers do
precision fertilizer through irrigation
hardware
Test 002: Precision Irrigation
HYPOTHESIS
OUR TEST
METRIC
WE’RE RIGHT IF
Precision fertilizer is primarily done
by precision irrigation systems.
>75% tell us that >75% of their
customers use irrigation systems
for precision fertilizer
RESULT
Depends on crop! Row
crops use variable rate
spreaders, many
specialty crops use
fertigation
NOW WHAT
[1] TBD
[2] TBD
Test Card
ONGOING

% of farmers that express
dissatisfaction with the imprecision
of their yield estimates.
Survey 10 farmers about their
impression of their own yield
prediction process.
Test 003: Yield Estimation
HYPOTHESIS
OUR TEST
METRIC
WE’RE RIGHT IF
Yield estimation would allow for
significantly increased optimization
in operations costs.
>50% are dissatisfied
RESULT
Asked 8 farmers
NOW WHAT
[1] TBD
[2] TBD
Test Card
ONGOING

Competitor Leaf Diagram
General Competitive Area:
Precision agriculture that can predict
yield and then create direct fertilizer
and irrigation optimization
opportunities for farmers.
AgAi

Total Addressable Market, per year: $47.58M
(1.33M acres of almonds in California) * ( 119 avg. # of trees per acre) * ($0.10 per tree) * (3 “fruit counts” per year)
Served Available Market, per year: $37.98M
(80% of 1.33M acres of almonds comfortable actually use demand based irrigation, proxy for being open to new yield
prediction technology) * ( 119 avg. # of trees per acre) * ($0.10 per tree) * (3 “fruit counts” per year)
Target Market, per year: $8.73M
23% of the California almond acreage, which is self-opted into California Almond Sustainability Program, which we use
as a proxy for the target market initially.
Y1-Y3 Revenue: $357K, $1.43M, $3.57M
Estimating $0.10 per tree * 119 trees per acre * 3x “fruit counts” a year * subscribed acreage (10,000; 40,000; 100,000)
Market Size
Sources: USDA NASS Almond Board 1 Almond Board 2

Clone market type,
now focused on specialty crops.
Week 2: 12 interviews this week. 25 total
Picker Hacker Hustler Hacker Hustler
PhD Candidate,
Learning Sciences &
Tech Design
PhD Candidate, EE
Stanford Vision Lab,
Stanford AI Lab
MBA1, Stanford GSB PhD Candidate, CS
Stanford AI Lab
MBA1, Stanford GSB

For large US farmers who seek to optimize input costs,
AgAi is software+hardware system that counts crops ahead of
harvest, allowing farmers to adjust fertilizer needs in real-time and
make important marketing decisions heading into harvest, thus
decreasing costs and increasing revenue.
Unlike our competitors, we provide plant level data that farmers
can act on to reduce costs and uncertainty.
Current Status

7
8
1. Specialty crop
farmers and/or farm
managers/operators
2. Certified Crop
Advisors (CCAs) &
agronomists in
specialty crops in the
U.S.
1.Trust: Ongoing data
analysis and precise
recommendations for
farmers.
2.Insight: Analysis tools for
strategic management to
increase value as a
Certified Crop Advisor.
2
1.Direct (trade shows)
2.Through crop-specific
advisory board
workshops/monthly
grower meetings
3.Through existing
agrochemical/fertilizer
sales channels1 3
45
1.Farmer relationships.
2.Proprietary hardware for
data collection.
3.Proprietary software for
crop counting.
4.Agricultural+AI SME.
6
9
1.Sell analysis via platform for a subscription/per usage fee.1.Development costs of technology, including testing.
1.USDA and public
WHY: To evangelize and
validate tech to farmers.
2.Certified Crop Advisors
(example). WHY: To
evangelize, as they are in
direct communication with
farmer buying decisions.
3.Specialty crop farmers*
WHY: Primary end user,
potential buyer.
4.Industry Associations
(i.e. Almond Board of
California). WHY:
To evangelize and validate
technology to farmers.
AgAi Week [2]
1.[gtm] Development of AI
platform for image recog.
& counting of crops
2.[on-going] Analysis of
crop growth trends for farm
owners/ managers
3.[on-going] On-demand
data collection.
1.Reduced costs
through targeted
water/fertilizer
application).
2.Increased revenue
through increased yield
from optimized fertilizer,
and other tests.
3.Increased revenue
from refined harvest
screening and QA (esp.
for tomatoes).
4.Increased revenue
through increased quality
of insight of a farmer’s
field, leading to more
targeted, higher-return
marketing efforts and
more referrals (esp. in a
word-of-mouth industry).

Who we talked to:
7 Almond growers
2 AgTech founders (1 also a row crop farmer)
1 Soil sensor researcher
1 Pest consultant
1 Industry-wide board member
What we expected:
Real-time crop count would allow for increased optimization
in operations costs.
What we learned:
Research proves that the technology exists to accurately
count fruits as they grow, but adoption is seemingly scarce.
Crop counting will benefit farmers’ marketing efforts as well
as their cost reduction efforts.
Water supply (irrigation) remains the most pressing issue for
almond growers--few solutions are provided by existing
firms.
Real-time crop counting
would “help farmers
make marketing and
nutrient decisions.”
- Almond Grower
Interviews & Insights

Products / Services
• Field crop count/health
monitor
Pain Relievers
• Greater insight into crop
status throughout season
Gains Creators
• Better data for optimizing
agricultural decisions
• Better data for optimizing
marketing decisions
Value Proposition: _______ Customer Segment: Agronomists
Pains
● Accuracy is difficult
to obtain (in yield
estimates and
nutrient
recommendations),
leading to
uncertainty and risk
Gains
● Advice is accurate
and reliable
● Clients come back
and refer
● Clients trust you
Customer Jobs
• Advising farmer clients
on nutrient application
amounts and timing
throughout the season
Value Proposition Canvas - Agronomists

Will...
Test these hypotheses:
• Real-time crop count would allow for significantly increased optimization in operations
costs.
• The limiting factor in precision irrigation/fertilizer is the right hardware -- the information
piece is easy to obtain while the action/hardware piece is the bottleneck.
• Farmers are currently experiencing high costs from labor of identifying pests and/or
controlling them.
• Farmers could significantly improve their bottom line by optimizing operations with data
from granular, fast tissue sampling.
Create an “MVP” to show customers:
• Fake images and/or video to show:
– Turning an image of a tree/trees into a count of fruits/nuts
– A map showing crop counts, nutrition/pest status over a field

AgAI engr 245 lean launchpad stanford 2019

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AgAI engr 245 lean launchpad stanford 2019