Corn Fertilization Control Using Soft Soil Nitrogen Measurement
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Solution Overview
Problem
Current precision fertilization techniques for corn face challenges such as low accuracy, lack of soil feedback information, and high costs, necessitating a more efficient and cost-effective method for measuring soil nitrogen content and controlling fertilization.
Innovation Solution
A corn on-demand fertilization control system utilizing the OAV-IIW-WGWO-SCQPSO algorithm to optimize the BP neural network for real-time soil nitrogen content measurement, incorporating soil pH, moisture, and fertilization data to adjust fertilization quantities precisely.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional soil nitrogen measurement methods are used, then measurement accuracy is improved, but system complexity and cost increase due to requiring extensive soil sensors
Solution Approach 1:
The patent introduces an intermediary computational model (soft measurement model based on neural networks) that mediates between easily measurable variables (soil moisture, pH, fertilization records) and the difficult-to-measure variable (soil nitrogen content). This intermediary model enables indirect measurement without requiring direct nitrogen sensors, thus reducing system complexity while maintaining measurement accuracy.
Solution Approach 2:
The patent replaces the mechanical/physical sensor system for direct nitrogen measurement with a computational/software-based measurement system. Instead of using physical sensors to directly detect nitrogen content, the system uses software algorithms (neural networks) to calculate nitrogen content from other measurable parameters, substituting a mechanical measurement approach with a computational one.
2Reliability
If more soil sensors are deployed to improve measurement reliability, then measurement precision is improved, but system cost and complexity increase
Solution Approach 1:
The soft measurement model acts as a reliable intermediary that computes nitrogen content from multiple independent sensor inputs (moisture, pH, fertilization data), providing reliable nitrogen measurement without requiring direct nitrogen sensors. This computational intermediary enhances reliability through data fusion and algorithmic processing rather than through additional physical sensors.
Solution Approach 2:
The system makes existing multi-functional sensors (moisture sensors, pH sensors) serve multiple purposes: they not only measure their primary parameters but also provide input data for the soft measurement model to infer nitrogen content. This multi-functionality approach improves measurement reliability without adding dedicated nitrogen sensors, avoiding increased system complexity.
3Manufacturing precision
If conventional fertilization control methods are used, then ease of operation is maintained, but fertilization precision deteriorates leading to over or under fertilization
Solution Approach 1:
The patent implements a feedback control mechanism where the soft measurement model continuously provides nitrogen content information that feeds back to the fertilization control system. This feedback loop enables precision fertilization by adjusting fertilizer application based on actual soil nitrogen conditions, while the automated nature of the feedback maintains ease of operation through centralized control.
Solution Approach 2:
The system enables self-service precision fertilization where the control system automatically adjusts fertilization quantities based on data from the soft measurement model, without requiring manual intervention for each adjustment. The system serves itself by autonomously making fertilization decisions based on measured and computed parameters, achieving precision while maintaining operational simplicity.
Data Source
AI summary
Disclosed are a maize on-demand fertilization control system and a soil nitrogen soft measurement method. Sensors transmit real-time data to a control unit, which calculates soil nitrogen content using an optimized BP neural network model. Based on maize's nitrogen demand, the system adjusts fertilization amounts using solenoid valves, achieving precise, cost-effective fertilization.