Patent classifications
A01G18/70
MACHINE-LEARNING-ENABLED TOOL CHANGER FOR MUSHROOM CROP MANAGEMENT SYSTEM
A robotic mushroom crop manager periodically or continuously receives mushroom bed data corresponding to a mushroom bed including growing mushrooms at a plurality of times. A trained mushroom bed model is used to process the mushroom bed data to generate mushroom bed state vectors respectively characterizing corresponding states of the mushroom bed at the plurality of times. Crop management equipment is controlled to perform a crop management program comprising a sequence of actions to be performed by crop management equipment comprising, for each current action in the sequence of actions, selecting, based on corresponding a current mushroom bed state vector, a selected crop management tool from a plurality of crop management tools. The crop management equipment is controlled to use the selected crop management tool to perform the current action on the mushroom bed.
MACHINE-LEARNING-ENABLED TOOL CHANGER FOR MUSHROOM CROP MANAGEMENT SYSTEM
A robotic mushroom crop manager periodically or continuously receives mushroom bed data corresponding to a mushroom bed including growing mushrooms at a plurality of times. A trained mushroom bed model is used to process the mushroom bed data to generate mushroom bed state vectors respectively characterizing corresponding states of the mushroom bed at the plurality of times. Crop management equipment is controlled to perform a crop management program comprising a sequence of actions to be performed by crop management equipment comprising, for each current action in the sequence of actions, selecting, based on corresponding a current mushroom bed state vector, a selected crop management tool from a plurality of crop management tools. The crop management equipment is controlled to use the selected crop management tool to perform the current action on the mushroom bed.
MACHINE-LEARNING ENABLED FUNGICULTURE THINNING
A robotic mushroom crop manager periodically or continuously receives mushroom bed data corresponding to a mushroom bed including growing mushrooms at a plurality of times, and uses a trained mushroom bed model to process the mushroom bed data to generate mushroom bed state vectors respectively characterizing corresponding states of the mushroom bed. Control crop management equipment is used to perform a crop management program comprising a sequence of actions on the mushroom bed, the sequence of actions including culling actions on at least some of mushrooms determined for culling based on the mushroom bed state vectors. A trained mushroom thinning model determines mushrooms for culling based on the mushroom bed state vectors, and/or mushrooms are determined for culling when a stem-cap growth rate ratio exceeds a preconfigured threshold. An end effector for culling mushrooms has a minimum probe height-width ratio to able culling without contacting or damaging neighbouring mushrooms.
MACHINE-LEARNING ENABLED FUNGICULTURE THINNING
A robotic mushroom crop manager periodically or continuously receives mushroom bed data corresponding to a mushroom bed including growing mushrooms at a plurality of times, and uses a trained mushroom bed model to process the mushroom bed data to generate mushroom bed state vectors respectively characterizing corresponding states of the mushroom bed. Control crop management equipment is used to perform a crop management program comprising a sequence of actions on the mushroom bed, the sequence of actions including culling actions on at least some of mushrooms determined for culling based on the mushroom bed state vectors. A trained mushroom thinning model determines mushrooms for culling based on the mushroom bed state vectors, and/or mushrooms are determined for culling when a stem-cap growth rate ratio exceeds a preconfigured threshold. An end effector for culling mushrooms has a minimum probe height-width ratio to able culling without contacting or damaging neighbouring mushrooms.
METHODS FOR THE PRODUCTION OF MYCELIAL BIOMASS
Provided are methods to produce an edible filamentous fungal biomass using an aqueous media which has a carbon source and a nitrogen source into which is inoculated filamentous fungal culture followed by culturing in a submerged fungal culture to produce an edible filamentous fungal biomass, wherein the fungal culture comprises Pleurotus spp. The culture may be grown to at least about 25 g/L (dry weight) with a productivity of at least 2.5 g/L/day (dry weight) during the culturing step. Also provided herein are compositions including an edible filamentous fungus.
METHODS FOR THE PRODUCTION OF MYCELIAL BIOMASS
Provided are methods to produce an edible filamentous fungal biomass using an aqueous media which has a carbon source and a nitrogen source into which is inoculated filamentous fungal culture followed by culturing in a submerged fungal culture to produce an edible filamentous fungal biomass, wherein the fungal culture comprises Pleurotus spp. The culture may be grown to at least about 25 g/L (dry weight) with a productivity of at least 2.5 g/L/day (dry weight) during the culturing step. Also provided herein are compositions including an edible filamentous fungus.
Machine-learning virtualization-enabled harvesting
A harvesting program system iteratively generates current harvesting programs for performance by harvesting equipment on a mushroom bed. The system receives current mushroom bed data corresponding to the mushroom bed including growing mushrooms at the current times. The system processes the current mushroom bed data using a mushroom bed model to generate current virtual mushroom beds corresponding to current states of the mushroom bed at the current times. The mushroom bed model is trained using labelled training mushroom bed data including known values of the mushroom bed, and using previously-generated virtual mushroom beds corresponding to predicted states of the mushroom bed. The system generates using the mushroom bed model predicted virtual mushroom beds corresponding to predicted states of the mushroom bed at future times. The system generates current harvesting programs based on the predicted virtual mushroom beds, and transmits them performance by the harvesting equipment on the mushroom bed.
Machine-learning virtualization-enabled harvesting
A harvesting program system iteratively generates current harvesting programs for performance by harvesting equipment on a mushroom bed. The system receives current mushroom bed data corresponding to the mushroom bed including growing mushrooms at the current times. The system processes the current mushroom bed data using a mushroom bed model to generate current virtual mushroom beds corresponding to current states of the mushroom bed at the current times. The mushroom bed model is trained using labelled training mushroom bed data including known values of the mushroom bed, and using previously-generated virtual mushroom beds corresponding to predicted states of the mushroom bed. The system generates using the mushroom bed model predicted virtual mushroom beds corresponding to predicted states of the mushroom bed at future times. The system generates current harvesting programs based on the predicted virtual mushroom beds, and transmits them performance by the harvesting equipment on the mushroom bed.
Mushroom conveying and packaging
Some examples show a conveying device for conveying an individual mushroom, a system adapted to convey mushrooms which comprises a plurality of conveying devices for conveying individual mushrooms, a method for conveying a mushroom, and a use of a conveyor to convey mushrooms.
MACHINE-LEARNING VIRTUALIZATION-ENABLED HARVESTING
A harvesting program system iteratively generates current harvesting programs for performance by harvesting equipment on a mushroom bed. The system receives current mushroom bed data corresponding to the mushroom bed including growing mushrooms at the current times. The system processes the current mushroom bed data using a mushroom bed model to generate current virtual mushroom beds corresponding to current states of the mushroom bed at the current times. The mushroom bed model is trained using labelled training mushroom bed data including known values of the mushroom bed, and using previously-generated virtual mushroom beds corresponding to predicted states of the mushroom bed. The system generates using the mushroom bed model predicted virtual mushroom beds corresponding to predicted states of the mushroom bed at future times. The system generates current harvesting programs based on the predicted virtual mushroom beds, and transmits them performance by the harvesting equipment on the mushroom bed.