AI Robotic Cross-Pollination for Yield Prediction
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Solution Overview
Problem
Cross-pollination in farming relies on variables like pollination agents and vectors, leading to high waste rates and energy expenditure, and may introduce undesirable characteristics, necessitating a more controlled and efficient method for genetics recombination and hybrid vigor.
Innovation Solution
A computer system and method using AI-assisted robotic devices to analyze and facilitate cross-pollination in a greenhouse environment by identifying and positioning flora, calculating tensors for optimal pollen transfer, and instructing robotic pollinators based on neural network models for precise pollen transfer and monitoring growth and yield.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If natural cross-pollination is used with pollinators or wind, then genetics recombination and hybrid vigor can be achieved, but waste rates are high and energy expenditure is high
Solution Approach 1:
The patent replaces natural mechanical pollination systems (pollinators, wind) with an AI-controlled robotic system that uses computer vision and automated mechanisms to transfer pollen, eliminating the energy waste associated with natural pollination vectors while achieving the same genetic recombination goals
Solution Approach 2:
The patent introduces an AI system as an intermediary between the flora, using neural networks to analyze images, identify optimal pollination pairs, and control robotic pollinators, thereby mediating the pollination process to reduce waste and energy consumption
2Adaptability or versatility
If natural cross-pollination is used, then genetics recombination can occur, but undesirable characteristics may be introduced
Solution Approach 1:
The patent employs AI image analysis and neural networks to evaluate floral characteristics before pollination, providing feedback to select only desirable traits for combination, thereby preventing the introduction of undesirable characteristics while maintaining genetic recombination benefits
Solution Approach 2:
The patent performs preliminary analysis of floral attributes using computer vision and AI algorithms before executing pollination, identifying and selecting only desirable characteristics in advance to ensure optimal outcomes and avoid undesirable traits
3Manufacturing precision
If AI-assisted robotic devices are used for cross-pollination, then precision and control are improved, but device complexity increases
Solution Approach 1:
The patent integrates multiple functions into a single AI-controlled platform, combining computer vision, neural network analysis, robotic positioning, and pollen transfer mechanisms, thereby achieving high precision while managing complexity through functional integration
Data Source
AI summary
According to one embodiment, a method, computer system, and computer program product for flora yield prediction is provided. The embodiment may include identifying a plurality of florae and a location of each flora within the plurality of florae within a preconfigured space. The embodiment may also include identifying one or more attributes of each flora. The embodiment may further include generating a neural network model based on the plurality of florae, the location of each flora, and the one or more identified attributes. The embodiment may also include calculating tensors from an anther of each flora to one or more stigmas of each other flora within the plurality of florae based on the generated neural network model. The embodiment may further include performing cross-pollination of the plurality of florae based on the calculated tensors.