Knowledge-Graph Irrigation and Fertilization for Salinity-Aware Planning
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for fertilization planning lack an integrated recommendation system for salt-controlled irrigation and fertilization, leading to inefficient fertilizer use, soil degradation, and environmental pollution, particularly in saline-alkali lands.
Innovation Solution
An integrated recommendation method and system based on a knowledge graph that vectorizes salt-controlled irrigation and fertilization information, calculates similarity with soil conditions, and predicts a score for recommendations, optimizing fertilizer application and irrigation strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If mathematical models are used for fertilization planning, then fertilization amount can be calculated, but suggestions on fertilizer varieties and fertilization methods are not provided
Solution Approach 1:
The patent combines mathematical models with knowledge graphs to integrate quantitative fertilization amount calculation with qualitative recommendations on fertilizer varieties and application methods. The knowledge graph stores expert knowledge about different fertilizer types, application techniques, and best practices, which complements the numerical outputs from mathematical models.
Solution Approach 2:
The system is designed to provide multiple functions: calculating fertilization amounts, recommending fertilizer varieties, suggesting application methods, and providing salt control advice. This multi-functional approach allows a single system to address all aspects of fertilization planning rather than requiring separate tools for each function.
2Ease of operation
If expert knowledge methods are used for fertilization planning, then suggestions on fertilization technology and fertilizer ratio are provided, but the system lacks integration with salt control and irrigation management
Solution Approach 1:
The patent merges expert knowledge methods with salt control and irrigation management systems. The knowledge graph integrates information about fertilizer application with soil salinity data, irrigation schedules, and crop requirements, enabling comprehensive recommendations that consider all these factors together rather than in isolation.
Solution Approach 2:
The system provides universal applicability across different farming scenarios by integrating multiple functions: fertilization planning, salt control, irrigation management, and crop-specific recommendations. This allows the system to adapt to various soil conditions, crop types, and environmental factors within a single unified platform.
3Measurement precision
If artificial intelligence models are used for fertilization recommendation, then high-precision results are obtained, but the system lacks interpretability and integration with agricultural knowledge bases
Solution Approach 1:
The knowledge graph serves as an intermediary between AI models and agricultural practitioners. It structures and stores domain knowledge in a interpretable format, allowing AI predictions to be explained through references to known agricultural principles and relationships stored in the knowledge graph, thus bridging the gap between black-box AI and interpretable expert systems.
Solution Approach 2:
The system combines the predictive power of AI models with the interpretability of knowledge-based systems. The knowledge graph enables the system to provide not only accurate recommendations but also explanations based on agricultural science principles, making the system both precise and interpretable simultaneously.
4Productivity
If traditional fertilization methods are used in saline-alkali lands, then crop yield may be maintained, but fertilizer utilization rate is low and environmental pollution increases
Solution Approach 1:
The patent applies local quality by providing site-specific fertilization recommendations tailored to the particular soil conditions, salinity levels, and crop requirements of each location. Rather than using uniform fertilization approaches, the system customizes recommendations based on local soil testing data and environmental factors, optimizing fertilizer use for each specific context.
Solution Approach 2:
The system dynamically adjusts fertilization parameters such as fertilizer type, amount, timing, and application method based on real-time soil conditions, salinity levels, and crop growth stages. This parameter optimization ensures maximum fertilizer efficiency while minimizing environmental impact by applying the right amount at the right time rather than excessive uniform application.
Data Source
AI summary
Provided are an integrated recommendation method and a system for salt-controlled irrigation and fertilization based on a knowledge graph. The method includes following steps: obtaining a vector set of salt-controlled irrigation and fertilization information and a quantitative value set corresponding to the vector set; calculating similarity between the each salt-controlled irrigation and fertilization information according to the quantitative value set; calculating interactive similarity with soil conditions in a salt-controlled irrigation and fertilization case base based on soil salinity, moisture and nutrient status of information of plots with crops located; calculating fusion similarity of the salt-controlled irrigation and fertilization information according to the similarity between the each salt-controlled irrigation and fertilization information and the interactive similarity; and predicting a score of recommendation of the salt-controlled irrigation and fertilization according to the fusion similarity, and completing the recommendation of the salt-controlled irrigation and fertilization according to the score of the recommendation.