Mobile & IoT

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Wine Clones, Drones, and Behavioral Cloning: Heretical IoT Thoughts on Winemaking

In addition to technology, I love cooking and craftsmanship. That naturally leads to an interest and wine, and especially, the winemaking process. Over the past few years, I have been lucky to meet a few great winemakers and discuss how they work their craft. This gave me an opportunity to learn not only how skilled they are, but also the incredible amount of work they and their teams do. They routinely begin their work at 3am and work past sunset. While much of this work is repetitive, the majority of it requires continuous application of expert judgment. The more I spoke to them the engineer in me started to think, “What could make their work easier without sacrificing their expertise?” That led me to this post.

Caveat: This post explores use of automation and IoT to augment (not replace) work by humans. The idea is not to replace people, but rather to take some of backbreaking labor out of their work and give them back more time with their families and friends. It is not intended to be as “heretical” as the idea may initially appear 😉

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If you visit a winery and get to speak with the winemaker, he or she will talk about the process of routinely going out and doing things such as:

  • Inspecting the vines for damage, disease, and general health
  • Pruning grapes to concentrate resources to the very best
  • Pruning leaves to control the amount of sun grape bunches get

Watching someone do this on one vine is amazing (you can see the expertise in action). Watching them repeat alone a single row of vines starts to give the idea of how much work it takes to make great wine. Staying around all day to see this done across acres of grapes (and considering this is done throughout the season) drives home how we should respect everyone who works in a vineyard. It also gives an appreciation for the effect of this labor on their backs, knees, fingers, and eyesight. The goal of IoT is to reduce danger, dirtiness and drudgery. This is where my idea started.

Imagine doing this 10,000 times, year-in, year-out

Imagine this

The team goes out in the morning, before sunrise to tend their grapes. For this day, let’s assume they are pruning bunches of grapes to concentrate resources on the very best. Each person grabs their section of the vineyard and starts his or her work. However, they are each partnered with two drones. The first drone watches what each person does, recording which grapes are pruned. The other drone picks up discarded grapes for retrieval and composting.

After the expert finishes a few rows of grapes, the drones fly back, plug in, and upload their video images. The wait for a machine learning program to complete construction of a new model that they then upload. Then the drones go out and finish the work, based on what they have learned from that wine expert, for that section of grapes, for that location, for that day’s weather and solar conditions. Much backbreaking (and eye-straining) work is saved. Allowing the team to do the umpteen other activities that require their expertise and attention. However, today they may work “only” ten hours instead of sixteen.

This is not replacing people

No jobs are lost. People do an enormous amount of work at wineries. With this technology they might now work “only” ten hours instead of sixteen. Imagine the benefits to their health of this.

Furthermore, human knowledge and expertise are not replaced. Every plot of land and every day bring new variables. Every workday, the human shares expertise that is used to reduce repetitive work for that day. If you remove the human, you remove the expert. Eventually the winemaking would get worse and worse and the drones do not have sufficient expertise. The vineyard would suffer and lose out to others who apply expert knowledge every day. What is reduces is drudgery.

This is not far-fetched

A decade ago this would have been more Star Trek than reality. However, the advances of the last few years in technologies for drone, autonomous vehicle, and machine learning have made this achievable. Let’s look at a few

A photo taken on September 9, 2014 shows a drone flying over vineyards of the Pape Clement castle, belonging to Bordeaux winemaker Bernard Magrez in the southwestern French town of Pessac. Magrez is the first winemaker to have bought last February a drone equipped with a infrared camera to determine the optimal maturity of the domain’s grapes and thus harvest them at different times. AFP PHOTO JEAN PIERRE MULLER.
Drone and Autonomous Tech

Over the last few years, growth in drone technology and its supporting infrastructure has exploded. It will hit $12 billion per year in the next four years (comparison: US wineries sold $34 billion of product last year). Major “Blue Chip” companies are now exploring active programs to embed drones in their supply chain. Major consultancies are now using drones in numerous aspects of farming. Furthermore, regulations are now clear. Two years ago, it cost you over $2,000 to get certified as drone pilot. Today you can do this with $200.

Autonomous technology is growing as fast or faster. There will be 10+ million autonomous vehicles on the road by 2020. Autonomous vehicles are no longer just an idea in Silicon Valley. All “Big Three” US automotive manufacturers have autonomous vehicle programs. Even major shipping companies are now exploring combined drone and autonomous technology for cargo ships.

Computing and Machine Learning (ML) Infrastructure

Thanks to computer gaming, GPU costs have dropped dramatically. You can know get resources to build models on-demand and pay by the minute. The libraries to process imagery (e.g., OpenCV) and to build neural networks (e.g., TensorFlow, Keras) are advancing new major versions every 3-4 months. Some have even wrote that the “Object Detection Problem” is now solved (a great technical, but to technical write here). As one of my friends said to me this week—over a 2012 Tempranillo—“It’s a great time to be in tech.”

However, having the ML infrastructure is only the start. Building models takes lots of time, trial and error, and compute time. Luckily we now have models that recognize images with greater accuracy than humans. Using “Knowledge Transfer” we can start with these basic models and extend them to add new knowledge (here is an example of what Gilt did to extend Microsoft’s ReNet vision recognition model to detect clothing styles). Combine this with “Behavioral Cloning” (an approach widely used to teach autonomous vehicles how to drive; here is one example I have used) we can clone and graft winemaking knowledge to these existing models—just a winemaker grafts a wine clone to his or her vines.


The foundations are in place and costs are coming down drastically. Making this cost-feasible for small business is just a matter of time (and perhaps the startup to focus on it).

Finally, this does not eliminate nuance and individuality

One of the most enjoyable aspects of exploring wine and vineyards is seeing how each winemaker executes his or craft (just as going to new restaurants lets you explore how each chef interprets his or her craft). This technology does not remove the nuance and individuality of winemaking.

Even if every vineyard used this technology and started with the same baseline drones and machine learning models, they would each evolve differently. Thanks to behavioral cloning and knowledge transfer, each vineyards model would evolve weekly as they learn how the winemakers and his or her team applies their expertise day-by-day, wine block-by-wine block, year-by-year. These models really would not even be trade secrets that could be stolen as they would evolve to literally fit specific terriors—just as best practices in winemaking are.

Spark Streaming and Expert Systems for the Industrial IoT

This week, at the Washington DC Spark Interactive, Savi Engineering shared some of our work on using Spark Streaming and Expert Systems technology (Drools) to analyze the Industrial IoT in near-real time.

At Savi, we use a hybrid Lambda Architecture (see my post on why Lambda is so important). By “hybrid” we mean that unlike pure Lambda Architectures, we cannot restate the past 100% as we have already notified humans of critical IoT events (e.g., theft, safety risk). We can only enrich and auto-resolve these as more data becomes available. You can find tips on how do this — in general with streaming technologies and specifically with Spark — in the following presentation. You can also learn more about tackling real-world IoT challenges:

In addition, at Savi we combine fully explicit rules with real-time machined learning algorithms to perform risk and performance analytics in near-real time (see my post on the differences in focus areas between our Data Engineers and Data Scientists).  James Nowell of our Engineering team provided a great presentation on how we run Drools inside Spark RDDs (yes–Drools, we do this without performance penalties) to create linear-scale expert systems to analyze all that IoT as if we were an omniscient human. You can find his presentation here:

In future presentations, we will expand on areas such as:

  • The differences in use of Spark (using the same data) between Data Scientists and Engineers
  • How we scale machine learning algorithms for real-time, sub-second execution (thousands of times per second)
  • Creating a DAG that combines hardware device edge intelligence with cloud-based intelligence

If you like what you see here, Savi is hiring. Take a look at here.