Two years ago, I wrote an article on AI in the vineyard for WBM. Feel free to read that article here…if you want. Otherwise, my basic argument was that although AI will eventually play a role in how we farm grapes, it’s a long way off compared to other industries and even other crops. We who grow grapes are the last ones to see such innovation.
And since then, AI has grown exponentially. If two years ago you were playing around with Chat GPT to create bizarrely distorted images and learn about tax loopholes, you can now go onto the likes of Claude and have it just create a website for you from a single prompt. Chatbots like this have essentially eliminated the need for entry-level coders.
However Claude is a computer, so it makes sense that it’s gotten very good at writing code for other computers. Similarly Chat GPT has digested the entire internet, and curates any answer for you by plucking it from its vast network of information. Sometimes its correct, and other times … less correct.
Vines aren’t computers…that’s right. I went to college.
And that’s where my argument on AI in the vineyard remains unchanged. We need a lot of data to train machine learning models and we don’t have that in viticulture. Now my previous examples are generative AI’s, i.e. bots that build things. But they are a bellwether for the state of technology as a whole, which has also improved considerably. You can’t make up for a lack of information though.
Many other industries, the ones we see significantly altered in recent years, have this plethora of data. Let’s say you want to come up with a model that, for instance, maps the behavior of people in an airport. Any given airport on any given day produces millions of data points tracked via sales information, inventory, flight schedules, etc. If you wanted to target specific groups or even specific individuals most likely to buy a certain product, the footprint we leave constantly informs these models.
But in viticulture, you might have a dirty notebook somewhere or a few random excel files on cluster counts from blocks that were subdivided years ago. Even if we as an industry were better organized, gathering data in vines is hard. Seasons are variable. Blocks are variable. Measurements are time-consuming in a job where timing is everything.
