Amazone and FieldView, Bayer’s platform for digital agriculture, are starting a strategic collaboration with the aim of further promoting and simplifying the adaptation of smart farming practices.
This collaboration aims to improve connectivity and create a link between FieldView’s advanced digital tools and Amazone’s precision machinery. Farmers will benefit from easy digital capture of all agronomically relevant parameters. Amazone and FieldView combine their expertise to make it easier for farms to adapt modern practices.
One of the main objectives of FieldView is to provide farmers deeper and more objective insights into the performance of their crops and fields. Therefore, the collaboration with Amazone aims to ensure integration and communication between Amazone machines and the FieldView Drive. Basic compatibility with Amazone crop protection sprayers and fertiliser spreaders was already in place in the past. Together the companies worked on improving the documentation for variable fertiliser rates on the basis of application maps. This way, if provided, Amazone fertiliser spreaders can apply a different application rate on the left and right side when processing application maps. In FieldView the recording with double precision is now also possible thanks to the co-operation with Amazone.
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Most recently, both partners dealt with Amazone’s seed drill technology. In particular, machines that can simultaneously apply seed, fertiliser and other inputs from different tanks. In one of the next FieldView Cab App versions, the newly created compatibility for Amazone seed drills will be made available to all customers. This further development will enable FieldView to fully capture all products applied during seeding and use them as a basis for insight and analysis.
In the future, FieldView farmers will benefit even more from Amazone’s technical expertise, with both partners intending to capture additional data layers, in addition to increasing precision and quality of the data collected. For example, captured weather data from Amazone machines would allow to make better decisions immediately during field work, or afterwards when assessing the quality of work.