Over the last two years, agricultural OEMs have been in a rush to get data from their machines to their cloud servers. Claas uses Proemion’s connectivity solution. Agco, Grimme, Krone and a few others bet on AgriRouter running on SAP’s cloud servers. Holmer seems to favour Bosch’s IoT suite. All these solutions have one thing in common: They transfer little data, typically less than 50 data points per second. They top out at 100 data points. They are good for task or order management, asset tracking, fleet management, monitoring of machine health and remote diagnosis.
At least some of these companies seem to think that these connectivity solutions will help them with driver-less or autonomous farming like harvesting, seeding, spraying or fertilising. The wishful thinking would go like this.
The farm machines transfer data to the cloud. The data is used to train machine learning algorithms on extremely powerful compute servers. The model, the result of the learning process, automagically detects whether grains are dirty, maize leaves are dry or the heads of sugar beets are chopped off. Typically, this model runs in the cloud as well, because it requires more compute power than is available on the farm machine or because it adapts to new conditions and self-optimises by continuous learning. This is the way how voice assistants like Alexa and Siri understand spoken language or how medical software recognises cancer cells in MRT scans.
This approach does not work for farm machines. Here is why and what we can do about it.