Hi Nelson! What's your background, and what are you currently working on?

Hi, I’m Nelson and I’m a 29-year-old programmer based in Auckland, New Zealand. I’m currently freelancing and working on Contento, a website that makes it easy to guest post by connecting brands and publishers together.

Haptly was my startup that ran for a year during 2016. It was a solution for farmers to monitor their grass growth via drone and satellite imagery. I was the co-founder and CTO, in charge of building the software and machine learning algorithms.

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What motivated you to start Haptly?

In late 2015 I was freelancing and looking for a startup problem to tackle. Spurred on in part by the hype at the time around drones and their applications, we began searching for use cases. We started by cold calling people asking them about how they used their drones. At one point, we got feedback that vineyard owners and farmers would be worth chatting to, so we started to focus on these niches. It became clear quite quickly that there were a number of areas farmers thought drones could be useful to them:

Rounding up stock

Measuring grass levels, a.k.a dry matter

Surveying land

We discarded rounding up stock and surveying land, as they were already being worked on if not already available. From what we could tell, no one had found an accurate way of measuring dry matter from a drone camera. For context - dairy farmers in New Zealand (and other countries) grow grass for the cows to eat. They have a number of paddocks they rotate the stock through. It’s important to know the dry matter available on each paddock so they can budget the amount of feed available for their stock. E.g. if they predict a deficit in their feed budget, they can order supplementary feed ahead of time or apply extra fertilizer to the lagging paddocks.

My background is in primarily in software and mathematics. Although I hadn’t done any machine learning since university 6 years earlier, after speaking with some local experts in the field they suggested this was a problem well within the possibility of solving.

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How did you build it?

We figured the next steps would be to do some more validation with farmers about what an actual product would look like. We drove out to farms to get an idea about how their current dry matter estimation process looked like and asked a bunch of questions to help design the first prototype UI.

Our first demo product was something whipped up in React + Material UI framework. It took about a month as I was the only developer and also had to learn React as I went! The demo contained a basic layout of paddocks with dry matter totals and a few charts including a feed wedge. We presented this at a local farming trade show to get a further feel for demand and also some feedback on the UI. This was good validation as again we had a whole lot of farmers turn up wanting to speak to us about when our product would be ready.

Looking back, in this period we probably spent too much time on validation from farmers. If we had more domain experience, it would have been obvious that they have been looking for a better solution than ‘eye-balling’ the grass levels for years. There have been various different attempts, but no one has succeeded in making something both sufficiently better and easy to use. Most alternatives involve towing or moving a device around manually, which would take hours on an average size farm.

At this point, we started to collect as much data as possible from farmers on their grass levels, including a lot of historical data. I started to explore the use of satellite imagery instead of camera data from drones as it was clear if they could achieve the same outcome, it would have some pretty significant advantages. It would make the product a simple app for a farmer, instead of app + drone they would have configured and fly. This would minimize the work required on their behalf and would also allow an easier expansion into countries with pastoral farming that have more stringent drone regulations than NZ.