Cultivation Quality Assessment via Knowledge Graph
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Solution Overview
Problem
Farm cultivation quality is affected by various factors including soil type, operator expertise, and equipment usage, making it challenging to optimize tractor operations for enhanced crop yield and productivity.
Innovation Solution
A method utilizing a cultivation knowledge graph constructed through natural language processing and machine learning to assess cultivation quality, identify controllable variables, and improve farm operations by adjusting these variables in real-time, facilitated by a computer program product and system that integrates IoT data and agronomic knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If multiple factors (soil type, operator expertise, equipment usage) are considered to improve cultivation quality, then the system complexity increases
Solution Approach 1:
The patent segments the complex cultivation quality assessment into distinct modules: a knowledge graph construction module that processes agronomic literature, a real-time data collection module that gathers sensor data, and a quality assessment module that integrates both. This segmentation allows each module to handle specific aspects independently, reducing overall system complexity while maintaining comprehensive quality evaluation.
Solution Approach 2:
The patent introduces a knowledge graph as an intermediary structure that bridges agronomic knowledge and real-time operational data. The knowledge graph serves as a mediator that stores structured relationships between cultivation factors, enabling the system to process complex interactions without requiring direct complex computational models, thus simplifying the assessment mechanism.
2Productivity
If real-time data monitoring and assessment is implemented, then productivity improves, but information processing requirements increase
Solution Approach 1:
The patent performs preliminary action by pre-processing agronomic knowledge into a structured knowledge graph before real-time operations. This pre-structured knowledge base enables rapid querying and comparison during actual cultivation operations, reducing the information processing load during critical real-time decision-making while maintaining high productivity.
Solution Approach 2:
The patent implements feedback mechanisms where real-time sensor data is continuously compared against the pre-established knowledge graph, and assessment results are fed back to operators for immediate adjustments. This feedback loop enables productivity improvement through real-time optimization without requiring complex continuous processing, as the comparison framework is already prepared in advance.
Data Source
AI summary
A memory embodies instructions, and a processor is coupled to the memory and is operative by the instructions to facilitate: accessing a source of information regarding farm cultivation techniques; constructing a cultivation knowledge graph by parsing the source of information regarding farm cultivation techniques, using natural language processing; identifying cultivation quality assessment factors by applying machine learning to the cultivation knowledge graph; estimating quality of a farm cultivation task by comparing a stream of real-time data to the cultivation quality assessment factors, wherein the stream of real-time data is related to performance of the farm cultivation task; identifying from the stream of real-time data, using the cultivation knowledge graph, a controllable variable that affects the quality of the farm cultivation task; and improving the quality of the farm cultivation task by facilitating a change in the controllable variable. The controllable variable may be the identity of a tractor operator.