Agrilyst is a virtual agronomist platform that helps indoor farmers manage their crops using sensor data. Earlier this year, the company raised $1.5m. This is on top of the $1m funding Agrilyst received in 2016. Indeed, the company is proving to be highly successful.
In 2015, Agrilyst won the Disrupt San Francisco Startup Battlefield. In 2017, the company attracted 100 new customers, and since 2016, Agrilyst has seen a 500% growth in both revenue and customers. It offers its services in 10 countries and its tools support the production of over 50 vegetables and 800 crops.
The key to Agrilyst’s success
Agrilyst is at the forefront of a fast-growing industry: indoor farming. As Red Planet previously reported, urban agriculture could end up being necessary for our survival and critical to adapting to climate change. It is entirely possible that urban farming could provide all the food that city dwellers need. What Agrilyst is doing is making this revolution in farming more intelligent.
The company’s founder, Allison Kopf, has written about how AI in agtech is going to make it possible – and highly efficient – to feed a growing global population in a sustainable way. Agrilyst is also business savvy – Kopf emphasises that the company “has always been focused on doing one thing: helping growers increase profits significantly.”
In order to achieve this, Kopf says that companies trying to predict diseases using imaging also need to add climate and nutrient data into the mix. Otherwise, the predictions aren’t going to be very accurate. Agrilyst’s growing success is based on its drive to collect as much data as possible about crops and to take as many variables as possible into account so that farmers can produce higher yields.
It’s all about the data
Kopf has detailed what the company offers to its customers:
Agrilyst is a workflow management system. We’re focused on using data to automate the non-mechanical processes on a farm. Processes like production planning, crop scheduling, quality optimization, risk management (disease, pest, crop success), labor planning, sales, and inventory management all fall into our domain. We don’t make hardware and we’re not focused on automating processes that can be replaced by machines, like seeding, harvesting, processing, and packaging.
Our software is a critical part of running daily operations on a farm. Growers use Agrilyst to track crops from seed to harvest, store yield data, track pests, record nutrient metrics, and more. All of this user-generated data makes up the core of our data set.
We also have an open API to connect to any device a grower has on their farm. Growers who have sensors, climate control systems, nutrient dosing systems, or connected lighting can connect to Agrilyst through the API for real-time data collection.
All of this data combines to build a proprietary network of data that strengthens as it interacts.
In building AI-driven workflow, Agrilyst digitalises all of the manual data collection processes. This data is then used to figure out why something happened, which is known as diagnostic analytics. Agrilyst also offers predictive analytics (forecasting how crops will perform), prescriptive analytics (recommendations on what farmers should do), and cognitive analytics (fully automating the planning component of farming).
Kopf points out that the company’s most utilised algorithm is their yield forecast, “which helps growers understand their expected harvest yield 30-days in advance with 90% accuracy.”
AI drives accuracy, efficiency, and optimal results. And in this respect, increasing automation could be crucial for global food production that is truly sustainable.
About the author: Sam Woolfe @samwoolfe
Sam is a freelance writer who is particularly interested in space exploration, sustainability, tech, and agriculture.