Machine-Learning Tool Changer for High-Frequency Mushroom Harvesting
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Solution Overview
Problem
Commercial mushroom cultivation and harvest systems face challenges in optimizing yield and effectiveness due to manual labor inefficiencies, overgrowth issues, and the difficulty in frequent picking, leading to reduced quality and value of harvested mushrooms.
Innovation Solution
An automated mushroom cultivation and harvest system utilizing robotic mushroom crop managers equipped with machine-learning models to select and optimize crop management tools based on real-time mushroom bed data, including a tool exchange plate and sensors for continuous data collection and tool selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual labour is used to harvest mushrooms, then operational simplicity is maintained, but productivity is reduced due to limited picking frequency
Solution Approach 1:
The patent replaces manual mechanical harvesting with an automated robotic system that uses computer vision and machine learning to identify mushrooms and control robotic end effectors for harvesting. This substitution enables continuous operation without human labor constraints, dramatically increasing picking frequency while maintaining operational simplicity through centralized control.
Solution Approach 2:
The robotic harvesting system operates autonomously to harvest mushrooms, eliminating the need for manual labor. The system independently performs detection, decision-making, and harvesting actions, enabling continuous operation at high frequency without human intervention and thereby resolving the contradiction between operational simplicity and productivity.
2Manufacturing precision
If picking frequency is increased to prevent overgrowth, then mushroom quality is improved, but labor cost increases
Solution Approach 1:
The patent replaces expensive manual labor with an automated robotic system equipped with computer vision and machine learning algorithms. This substitution enables high-frequency picking to prevent overgrowth and maintain mushroom quality without incurring increased labor costs, as the robotic system operates autonomously at scale.
Solution Approach 2:
The system changes the operational parameter from manual intervention intervals to continuous automated monitoring and harvesting. By transforming the harvesting process into a continuous automated operation, the system maintains optimal mushroom quality through frequent picking while eliminating the proportional increase in labor costs that would otherwise accompany increased picking frequency.
3Productivity
If automated harvesting is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent divides the automated harvesting system into distinct functional modules: computer vision for detection, machine learning for decision-making, and robotic end effectors for harvesting. This segmentation allows each component to be optimized independently while working together to achieve high productivity, managing overall system complexity through modular architecture.
Solution Approach 2:
The robotic system integrates multiple functions into a single platform: detection, identification, decision-making, and harvesting. This multi-functionality reduces the need for separate systems for each task, managing complexity by consolidating operations while maintaining high productivity through coordinated execution of all functions.
4Productivity
If tool selection is optimized using machine learning, then yield is enhanced, but measurement precision requirements increase
Solution Approach 1:
The patent implements a feedback loop where the machine learning model continuously receives data from sensors and computer vision systems, processes this information to optimize tool selection, executes harvesting actions, and uses the outcomes to further refine its decisions. This feedback mechanism enables yield enhancement through adaptive optimization while managing measurement precision requirements through iterative learning from real-world results.
Solution Approach 2:
The system performs preliminary data collection and processing using sensors and computer vision before making tool selection decisions. By gathering and pre-processing measurement data in advance, the system reduces the immediate precision requirements during critical harvesting decisions, as the preliminary data provides a foundation for informed tool selection that enhances yield.
Data Source
AI summary
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.