Optimizing harvest window evaluation for stone fruit with artificial intelligence - test of machine learning analysis with cherry production in Sogn, Western Norway. (OptiFrukt)

The project aims to investigate the prerequisites for- and use of artificial intelligence (machine learning) for more precise planning of harvest-time for cherries produced in Sogn. Sogn Fruits and Vegetables AS (SFG), which operates and owns Norway's largest packing house for cherries, is a partner in the project. The 2023 season, characterized by a significant amount of waste, has demonstrated a substantial need to develop and test new methods that can contribute to ensuring fruit quality and reducing waste in the primary sector. A goal of the ML-modelling is to enable a precise separation of harvest time forecasts for different varieties and farms with a common reception site. The methodology developed and tested in the project have potential to be used for better and more predictable planning of harvesting for farmers and reception for packing structures, distributors, and sales structures, which has the potential to optimize planning and decision-making for farmers, packing houses, and further up in the value chain to induce more predictability and efficiency along the production-consumption line.

Start date:
End date:
Financed by:
Forskingsmidlar for jordbruk og matindustri (FFL/JA)
In cooperation with:
Sogn frukt og grønt AS