Precision agriculture has the potential to greatly improve yields and reduce costs through optimized variable rate applications based on geospatial data. However, there are several hurdles preventing its widespread adoption. Key hurdles include a lack of high quality, uniform sensor data; proprietary data that is not shared; difficulty synthesizing data from multiple sources; and providing actionable advice to farmers given unpredictable factors and the risk of financial losses from incorrect recommendations. Overcoming these hurdles will require improved sensors, data sharing incentives, advances in deep learning, and advice systems that demonstrate clear economic benefits to farmers.

1. 1
What is in the way of full Precision Agriculture development?
The promise of Precision Agriculture: New techniques allow for improved geospatial information, remote sensing, self learning databases, and
Internet of Things. The developments in agronomical knowledge unlock new yield levels, and possibilities to save on costs. Combined this can
have tremendous positive impact on the agricultural business and society.
It seems simple….
Precision Ag needs to offer a actionable advice to a farmer.
- Optimizing returns by apply the right amount, from the right source, in right place, right time and right manner (R’s of agriculture)
- It can be expected that technology from other industries can be used to solve the data challenge. Much technology is readily available in mass
applications to create a system of GPS, GIS mapping, remote sensing cloud service, and analytics.
- Deep learning will allow for processing not uniform data
- Adoption rates for seed genetics and precision steering have been high due to high visibility and compelling economics.
Why is this not there yet?
The“R”s of
Precision
Agriculture
the
Right
time
the
Right
amount
the
Right
place
the
Right
Source
the
Right
manner
Source: Amercian society of agronomy

2. 2
The main hurdles to a comprehensive roll-out of Precision Agriculture
x
GPS
agronomy
Software
IT
Sensors
GIS
Hurdle 1: Local operations data Hurdle 2: Access to data Hurdle 4: Actionable advise
1. Quality sensors and measurement
2. Local knowledge agronomists
1. Collect local operations data
2. Isolated Agronomist
3. APIs for corporate platforms
1. Compelling economics
2. Usable algorithms
3. Training users
1. Self learning databases
2. Deep learning to capture
unstructured and massive
amount of interacting
factors
Hurdle 3: Synthesize Data

3. 3
Getting from hyper local & proprietary to sharing to actionable advise
Hurdle 1: Knowledge building is complicated as measurements are often informal, not uniform and lack full context
§ Cheaper, real-time and uniform measurement tools (sensors) will greatly improve the basis of any advice or decision
§ Routine agronomy and management must further optimize to offer compelling economics for next hurdle
Hurdle 2: Data and measurements are proprietary and typically not shared outside trusted networks
§ Need to tap into farmers and agronomist knowledge and data for context
§ Monetizing data with income differentiators that support investment in uniform measurement and data collection
• Comparing results internally, in groups or industry
• Traceability, certification, compliance
• Accounting, control and performance measurement
Hurdle 3: Synthesize data – Only in data rich situations data-driven models can become appropriate
§ In data poor situations, knowledge-driven models may be less accurate but will preferred by the farmer.
§ Self learning databases and Deep learning may provide solutions for processing large quantities of not uniform data, attaching values and
contexts of other databases
Hurdle 4: Actionable advise
§ Need for compelling economics:
• Significant unpredictable (weather) factors affect profitability
• possible earn back period systems
§ Fear for “getting it wrong”:
• Savings are marginal, down-side is significant.
• Fixed costs cause mistakes to disproportionally hurt the bottom line.
§ Fear for technology and useless data: interfaces and actionable advise are crucial - just provide data is not enough