Farming data analytics firm Uptake raises $117m
Uptake, an agriculture analytics firm,  has raised $117m in a series D funding round. The company uses sensors to track the performance of a range of farming machinery and uses machine learning to determine optimal usage and if repairs are needed. The round was led by the UK's Baillie Gifford, with US investors also involved, and has brought the company's total fundraising to $264m, valuing it at $2.3bn.

Uptake, a data analytics startups that uses sensor data to optimize machinery including farming equipment, has raised a $117 million Series D round.

Baillie Gifford, one of the UK’s largest independent investment management firms, led the round, which also included GreatPoint Ventures and Revolution Growth, a Washington, DC-based VC helped by AOL cofounder Steve Case.

This brings Uptake’s total fundraising to $264 million, $130 million of which was raised in two rounds earlier this year, and it is now valued at $2.3 billion. Uptake is cash flow positive, according to TechCrunch, but said in a statement that the new funds will allow it to speed up its growth.

The company was founded by two cofounders of flash deal site Groupon on the idea that most commercial equipment comes outfitted with some sensors, but little use has been made of the data they produce. Using these sensors, plus additional sensors from outside partners in some cases, Uptake tracks the performance and usage of combines, carts, trucks, and semis, to name a few, in order to gauge optimal use and also machine performance. The software is able to make recommendations to optimize machinery expenses by minimizing downtime among other things.

Uptake uses machine learning and predictive analytics to anticipate needed servicing and prevent problems, allowing growers to optimize the timing of repairs and order parts in advance. The company says that using predictive analytics can also help to avoid safety concerns like combine fires, which cause 40 to 50 serious injuries and $20 million in property losses each year in the US, according to a study by the University of Minnesota.

“Utilizing machine learning we can figure out the least amount of machine downtime so that growers get the crop out expediently. Utilizing the sensor data from the [Controller Area Network] CAN-BUS, we can perform data analytics on the onboard sensors to find the subtle precursors which cause combine fires,” wrote Tim Marquis, agriculture lead for Uptake in PrecisionAg.

The company claims that machine learning allows them to work in multiple industries in addition to agriculture including rail, energy, mining, aviation, and construction.

The company’s website says its platform can help growers “understand field conditions before dispatching equipment and crews,” and “maximize asset efficiency by improving routes, fuel consumption, and operator productivity.”

“It comes to recommendations. For example, we’ve worked with imagery data in the past to demonstrate our capabilities in combining agronomic datasets, like historic yield and planting data from a combine, weather, and imagery, to create predictive yield models and planting date estimation,” said a company spokesperson to AgFunderNews.

The company’s site says that its capabilities can serve growers, food processing plant managers, equipment growers, precision agriculture product managers, and equipment manufacturers.

Also working with machine data analytics for agriculture is Brazilian company Solinftec, which received funding from alternative and renewable Technologies growth equity platform TPG ART in July.

Solinftec has a suite of technologies including proprietary hardware, a telemetry communications network, and a software-as-a-service platform. The hardware, which includes a proprietary onboard computer for tractors and can integrate with any machine brand, gives operations information about the status of their machines and their progress based on their positioning and what activity they’re undertaking.