Localized Plant Viability Prediction With AI and Augmented Reality
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Solution Overview
Problem
Existing gardening resources lack the ability to provide localized, detailed information on plant viability and care instructions based on specific climatic and environmental conditions, failing to consider variables such as sunlight, water, soil, and chemistry, and do not offer augmented reality views of plants in a specified location.
Innovation Solution
A system that aggregates and localizes climatic, environmental, and plant-specific variables using telemetry-driven weather stations and artificial intelligence to generate care tasks and viability scores, incorporating image recognition for plant identification and augmented reality displays.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If existing gardening resources are used, then general gardening information is available, but localized specific information considering climatic and environmental specificity is lacking
Solution Approach 1:
The patent introduces a computer system with database and processing capabilities as an intermediary between general gardening resources and users. This system aggregates, localizes, and delivers specific climatic and environmental information tailored to user locations, resolving the contradiction by providing localized information without requiring users to build complex information gathering systems themselves
Solution Approach 2:
The system segments gardening information by geographic location, climate zone, and environmental conditions. By dividing general gardening knowledge into location-specific segments, the system delivers precisely targeted information while managing complexity through structured data organization and delivery
2Measurement precision
If detailed localized yard analysis is performed, then plant viability prediction is improved, but information processing requirements increase
Solution Approach 1:
The system performs preliminary analysis by pre-processing and storing climatic, environmental, and plant-specific data in structured databases before viability assessment. This preliminary organization of data reduces computational energy requirements during actual plant viability predictions while maintaining high measurement precision
Solution Approach 2:
The system transforms multiple environmental parameters (sunlight, water, soil, chemistry) into standardized, comparable formats suitable for computational analysis. By changing parameters into uniform data structures, the system achieves precise viability predictions while optimizing computational efficiency
3Ease of operation
If augmented reality display is implemented, then plant visualization in specified location is enhanced, but device complexity increases
Solution Approach 1:
The system creates virtual copies of plants and their expected growth in specified locations using augmented reality technology. These digital replicas allow users to visualize plants in their yards without physical installation, enhancing ease of operation while managing complexity through software-based virtual modeling rather than physical systems
Data Source
AI summary
A system and method is disclosed for plant maintenance, identification and viability scoring. An application is located on consumer devices connected to the server. Images may be submitted to the server to identify a plant type through convolutional neural network image recognition. The invention uses another artificial neural network to predict a plant's viability score or optionally, arrives at a correlation value based on a fast abbreviation set of the plant profile and third party data. The server compares plant related input and local climactic data to determine the plant's viability score. Another aspect of the invention generates and displays an augmented reality display of a plant in the user's yard.