Upstream Ag Insights - July 9th 2023
Essential research and analysis for agribusiness leaders
Welcome to the 176th Edition of Upstream Ag Insights!
Next week I have an exciting announcement regarding the future of Upstream Ag Insights and it’s focus for delivering improved analysis to you moving forward.
Stay tuned for the July 16th, 2023 Edition next Sunday!
Index for the week:
Regenerative Ag Doesn’t Have to be Contentious: A Follow-Up
AGvisorPRO Launches the First Large Language Model for Equipment Dealers
Enabling Agriculture to Invert the Firm
Bushel Farm Connected Data
Precision Survey: Ag Dealers Respond to Marketplace Shifts
Aigen Unveils an AI-driven, Solar-Powered, Agricultural Robotics Service
Shoots by Syngenta Accelerator
Farmers Edge/AGI Correction
How to do Great Work
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If you are new or were forwarded this e-mail, my name is Shane Thomas, and this is Upstream Ag Insights, a newsletter for agribusiness leaders breaking down the latest innovations and business dynamics in the agriculture industry.
1. Regenerative Ag Doesn’t Have to be Contentious: A Follow-Up - Upstream Ag Insights
Last year I wrote Regenerative Agriculture Doesn't Have to Be Contentious.
In it, I laid out my basic thoughts about breaking regenerative ag down to it’s component parts/principles and using a roadmap and scorecard to progress on each.
With the recent Bayer Crop Science initiative to lean into regenerative ag, there has been an increase in commentary around whether this is smart of Bayer, possible by Bayer etc. and what regenerative ag actually is.
I think it’s important to unpack some of this commentary, so check out this weeks’ write-up.
Related: Bayer rises - report on a possible spin-off of Crop Science - Market Screener
visorPRO is based on a Large Language Model (LLM) AI system which is specifically designed to extract accurate answers from technical, operational, and service manuals. This visorPRO AI solution is designed to solve current dealership challenges by automating referenced responses to repeated queries with a “human-in-the-loop” approach, freeing technical experts to focus on more complex issues.
There has been plenty of talk about LLMs in agriculture. I have talked about it primarily through the lens of agronomy, crop input retailing and software tooling to support agribusiness professionals in sales and marketing.
The use cases are significant, though and AGvisorPRO has unlocked a high utility use case: navigating equipment manuals for equipment dealership service providers.
The average dealership has dozens of different models of tractors of their own brand tractors (eg: John Deere for a Deere dealership), but then they also have to navigate manuals for their used equipment along with the other pieces of equipment they sell, like seeders and planters or manuals from various in-cab software (eg: Trimble, Raven).
One could assume this leads to upwards of 100+ manuals to navigate for service technicians or salespeople at each dealership to get a piece of equipment set properly or fixed.
Every time a technician gets a question, it could take several minutes to access the manual, navigate the manual, interpret and synthesize the information into a usable fix for the farmer.
Not only is it time-consuming, it requires a higher cognitive load for the individual. A natural opportunity to save time and effort is with LLMs.
It’s better that basic search because not only does it retrieve the information, it can also give a basic answer that can be lightly edited before sending to the customer or interpreting oneself.
The technology stack to enable generative AI is worth understanding and a helpful resource can be found from Liat Ben-Zur on Linkedin here.
A lot of what I suspect AGvisorPRO are using are generic tools available to their development team in the bottom half of the stack.
What does the tech stack look like? Something like this: