Machine Learning Plant Cell Selection for Biotechnology Automation
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Solution Overview
Problem
Current high-throughput techniques for plant biotechnology have not been effectively translated from single cell organisms to crop plants, making it difficult to efficiently biotechnologically modify crops and cultivars, especially those traditionally recalcitrant to modification, and lack a formalized and directed process for plant biotechnological modifications.
Innovation Solution
A high-throughput plant biology system utilizing machine learning models and automation to identify optimal processes for biotechnological modifications, including selecting plant cells or tissue regions, determining modification protocols, and orchestrating robotic or automated systems for execution, thereby improving efficiency and consistency in genetic engineering and regeneration of plants.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual methods are used for plant biotechnological modification, then expertise and manual skill are required, but the process is slow, inconsistent, and difficult to scale
Solution Approach 1:
The patent replaces manual mechanical operations with automated robotic systems. Robots perform precise operations such as excising plant cells, delivering exogenous material, and regenerating plants, substituting human manual skill with automated control systems that provide consistency and scalability.
Solution Approach 2:
The system incorporates machine learning models that automatically select optimal plant cells, determine modification protocols, and guide robotic execution without human intervention. The system serves itself by using AI to make decisions about which cells to modify and how to proceed through the modification process.
2Productivity
If high-throughput techniques from single cell organisms are applied to crop plants, then processing speed increases, but recalcitrant crops remain difficult to modify due to lack of formalized protocols
Solution Approach 1:
The patent uses machine learning models to determine optimal parameters for modifying different plant species. The system analyzes characteristics of recalcitrant crops and adjusts modification protocols accordingly, changing parameters such as delivery methods, cell selection criteria, and regeneration conditions to suit each species' specific requirements.
Solution Approach 2:
The patent divides the plant modification process into discrete, automatable steps: cell selection, excision, exogenous material delivery, and regeneration. This segmentation allows each step to be optimized independently and executed by specialized robotic modules, making the overall process adaptable to different crop types.
3Measurement precision
If manual expert judgment is used to select plant cells for modification, then selection accuracy depends on expert skill, but the process lacks consistency and scalability
Solution Approach 1:
The patent replaces expert visual inspection and manual selection with machine learning-based image analysis systems. These systems automatically identify and select optimal plant cells based on trained criteria, providing consistent, scalable selection accuracy that does not depend on individual expert skill levels.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for using machine learning models for plant biotechnology. One of the methods includes obtaining a network input comprising an image depicting a plurality of plant cells or regions of plant tissue; processing the network input using a machine learning model to obtain an identification of one or more particular biotechnologically-modifiable plant cells or one or more particular biotechnologically-modifiable regions of the plant tissue; excising or delineating the one or more identified plant cells or the one or more identified regions of the plant tissue; and delivering exogenous material to the excised or delineated plant cells or regions of plant tissue.