Farm Versioning System for Crop Portability and State Traceability
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Solution Overview
Problem
Conventional approaches for managing farms and utilizing data from various sources fail to optimize farm operations due to lack of traceability of factors influencing farm state changes and inability to apply practices across similar conditions without adverse effects, and there is no system to store data, information, and knowledge in a versioned manner.
Innovation Solution
A processor-implemented method and system for monitoring and managing farm versions, which captures and preprocesses parameters from multiple farms, computes a farm portability score, identifies target farms based on similarity, and generates optimal traversal routes using semantic ontological databases and knowledge graphs to transform farms to desired states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If farm data from multiple sources is collected and monitored, then the quantity and variety of information increases, but the ability to traceability of factors influencing farm state changes deteriorates due to lack of systematic storage and versioning
Solution Approach 1:
The system segments farm data into distinct versions, each representing a specific state of the farm at a particular time. This segmentation allows systematic organization of large quantities of data while maintaining traceability through version history, resolving the contradiction between data quantity and traceability.
Solution Approach 2:
The system performs preliminary actions by pre-processing and storing farm data in versioned formats before analysis. This preliminary organization of data structure enables subsequent traceability analysis to function effectively, allowing the system to handle large data quantities while maintaining factor traceability.
2Ease of operation
If conventional farm management approaches are used, then current farm operations are maintained, but the ability to apply practices to other farms with similar conditions deteriorates due to lack of portability assessment
Solution Approach 1:
The system changes the approach by introducing portability scores and similarity indices as new parameters to assess and compare farm conditions. This enables conventional farm management practices to be systematically evaluated for their applicability to other farms, improving adaptability while maintaining ease of operation through structured comparison metrics.
Solution Approach 2:
The system creates virtual copies of farm state data in versioned formats, allowing practices from one farm to be copied and evaluated for applicability to other farms with similar conditions. This copying mechanism enables practice transfer without disrupting current operations, resolving the contradiction between maintaining ease of operation and improving adaptability.
3Ease of manufacture
If farm practices are applied without portability assessment, then implementation is simple, but adverse effects on other crops occur and optimization of farm operations is not achieved
Solution Approach 1:
The system introduces feedback mechanisms through portability scores and similarity indices that evaluate the potential impact of farm practices before implementation. This feedback loop identifies practices that may cause adverse effects on other crops, allowing for modified implementation strategies that maintain simplicity while preventing harmful outcomes.
Solution Approach 2:
The system performs preliminary assessment using portability scores to identify and prevent potential adverse effects on other crops before practices are implemented. This preliminary anti-action approach filters out harmful practices while maintaining the simplicity of implementation for safe practices, resolving the contradiction between ease of manufacture and prevention of harmful factors.
4Measurement precision
If comprehensive farm monitoring is implemented, then data collection coverage increases, but the complexity of managing and analyzing the data increases without systematic storage solutions
Solution Approach 1:
The system segments comprehensive farm monitoring data into versioned, pre-processed records that are systematically stored and organized. This segmentation reduces data management complexity by creating structured, manageable units while maintaining comprehensive measurement coverage through complete version history tracking.
Solution Approach 2:
The system performs preliminary data processing and storage organization before analysis, reducing the complexity of managing comprehensive monitoring data. By pre-processing and structuring data in versioned formats, the system enables comprehensive measurement coverage while simplifying subsequent management and analysis operations.
Data Source
AI summary
Conventional approaches lack in traceability of various factors that led to changes in farms states. Further, conventional approaches for managing farms, and associated data obtained from various sources do not fully allow for employing farm's practices to other similar conditions as they may have adverse effects over other identical/similar crops during cultivations. Present disclosure present systems and methods that codify/pre-process and store farm parameters, associated versions and farm interactions in a systematic manner, along with associated knowledge, inferences, that bring changes to a farm's state, which can be referred as farm versions. The inferences, and relationships between farm configurations, versions and the like are used by the system to generate farm portability score for target farm and identify farm state portability. Once the current farm state is identified, the system further creates an optimized routing mechanism for the target farm to attend an optimal state.