Saga Robotics has entered into a three-way data partnership with Bitwise Agronomy and Chambers Farm, aimed at integrating robotics, AI, and commercial farming practices in the UK’s strawberry sector. Saga’s Thorvald robots, already used for UV-C treatment on a significant share of the country’s tabletop strawberries, will now also collect detailed plant imagery twice weekly at Chambers Farm. The footage will be processed onboard to count and classify millions of fruits and flowers, with results sent to Bitwise Agronomy’s platform. There, the counts will be combined with environmental data and crop models to produce yield forecasts intended to support planning for harvest, labour, and sales. Chambers Farm will serve as the initial testbed for the system, which aims to provide growers with more consistent and timely production estimates based on automated, in-field data collection.
Already trusted for its UV-C treatment of approximately 20% of the UK’s tabletop strawberries, Thorvald is now adding a powerful new dimension to its services. Twice per week, as Thorvald treats crops with UV-C light at Chambers Farm, it simultaneously captures detailed video footage of the plants. Using onboard AI, each robot is estimated to count over 3 million fruits and flowers per night, assesses ripeness on a scale from 0 (flower) to 7 (overripe), and maps this information to exact field rows.
That processed data is then uploaded to Bitwise Agronomy’s platform. There, it’s combined with weather data, field sensor readings, and Bitwise’s proprietary ripening curves to generate precise yield forecasts, giving growers a dependable edge for planning labour, harvests, and sales.
“This is about putting real numbers in the hands of growers,” said Damian Flynn, CPO of Saga Robotics. “Thorvald is already out there, delivering UV-C treatments. Now, it’s doubling its value, capturing detailed fruit data that gives growers clarity, not guesswork. By combining our platform with Bitwise’s agronomy intelligence, we’re delivering what the industry has been asking for: reliable, scalable yield prediction based on automated data collection.”
Chambers Farm, a long-time innovator in sustainable berry production and an early adopter of both Bitwise and Thorvald, is the proving ground for this collaboration.
“These are robots that work where it counts: in the field,” said Tim Chambers, Managing Director of Chambers. “We’ve seen how Thorvald’s nightly data runs help us understand flower density, ripening rates, and where to focus picking resources. With Bitwise’s forecasting layered in, we’re seeing the potential to take our operational planning to a new level.”
Bitwise Agronomy, known for its machine learning models and global track record in yield prediction, sees the integration with Thorvald as a natural next step.
“Thorvald is uniquely positioned to collect high-quality data, consistently and non-invasively, throughout the growing season,” said Fiona Turner, CEO of Bitwise Agronomy. “What we’re doing is amplifying that data with proven agronomic models to deliver forecasts growers can use to make informed decisions days, weeks, and months ahead of time.”
This initiative marks a step change in Thorvald’s capabilities. What began as a UV-C treatment platform is now an integrated and autonomous IPM and crop data solution, helping growers make informed decisions to protect margins and streamline operations.
Looking forward, Saga Robotics is continuing to expand Thorvald’s data collection and analytics offering. New crop detections are on the horizon as is a new digital platform to experience Thorvald and the data it collects. This collaboration is just the first of many partnerships Saga Robotics is building to bring real, field-ready value to growers.
“Growers don’t need more complexity,” said Damian Flynn. “They need proven tools that slot into their operations and deliver clear insight. This partnership delivers on that promise, and it’s just the beginning.”
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Read the orginal article: https://arcticstartup.com/saga-robotics-bitwise-agronomy-chambers-farm-partner/
