Machine-Learning Tool Changer for Precise 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 of frequent picking, which can affect mushroom quality and value.
Innovation Solution
An automated mushroom cultivation and harvest system utilizing a robotic mushroom crop manager that employs machine-learning-based models to select and optimize the use of crop management tools, continuously training on mushroom bed data to improve harvesting efficiency and yield.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual labour is used to harvest mushrooms, then operational flexibility is maintained, but productivity is insufficient due to the inability to pick frequently enough
Solution Approach 1:
The patent replaces manual mechanical harvesting with an automated robotic system that uses sensors, machine learning models, and automated tool selection to perform harvesting operations. The system substitutes human labor with automated machinery capable of operating at higher frequencies without the operational complexities associated with manual coordination.
Solution Approach 2:
The system employs machine learning models that continuously learn from harvesting data to autonomously optimize tool selection and harvesting parameters. The system self-adjusts and improves its performance over time without requiring external intervention or complex operational management.
2Manufacturing precision
If picking frequency is increased to prevent overgrowth, then yield quality is improved, but labor cost increases
Solution Approach 1:
The patent replaces expensive manual labor with an automated robotic system that can operate at high frequencies without proportionally increasing costs. The system uses computer vision and machine learning to achieve precise harvest timing, eliminating the need for expensive human labor while maintaining or improving harvest quality.
Solution Approach 2:
The system dynamically adjusts harvesting parameters such as tool selection, picking force, and timing based on real-time data from sensors and machine learning models. This allows optimization of harvest precision while controlling operational costs through automated decision-making.
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 modular components: sensor systems for detection, machine learning models for decision-making, robotic mechanisms for execution, and tool racks with multiple specialized end effectors. Each module operates semi-independently, allowing the system to achieve high productivity while managing complexity through modular architecture.
Solution Approach 2:
The system uses a universal robotic platform that can perform multiple harvesting functions by selecting from different end effectors in the tool rack. The same robotic arm and control system can handle different mushroom sizes and types by changing tools, reducing overall system complexity compared to having dedicated systems for each function.
4Adaptability or versatility
If multiple end effectors of different sizes are used, then adaptability to different mushroom sizes is improved, but device complexity increases
Solution Approach 1:
The system dynamically selects appropriate end effectors from the tool rack based on real-time analysis of mushroom size, shape, and position using machine learning models. The tool selection is not static but adapts continuously based on detected conditions, allowing the system to handle varying mushroom sizes with a standardized tool rack design.
Solution Approach 2:
The machine learning model acts as an intermediary between the sensor system and the physical end effectors. It processes sensor data, determines the appropriate tool selection, and translates detection results into actionable commands for the robotic system, simplifying the interface between perception and action.
Data Source
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AI summary
A robotic mushroom crop manager (100A, 100) periodically or continuously receives mushroom bed (400) data corresponding to a mushroom bed (400) including growing mushrooms (410) at a plurality of times. A trained mushroom bed model (260) is used to process the mushroom bed (400) data to generate mushroom bed (400) state vectors respectively characterizing corresponding states of the mushroom bed (400) at the plurality of times. Crop management equipment (300, 250, 750) is controlled to perform a crop management program (232) comprising a sequence of actions to be performed by crop management equipment (300) comprising, for each current action in the sequence of actions, selecting, based on corresponding a current mushroom bed (400) state vector, a selected crop management tool (510) from a plurality of crop management tools (510A, 510, 51). The crop management equipment (300) is controlled to use the selected crop management tool (510) to perform the current action on the mushroom bed (400).