Adaptive Parameter Sampling for Precision Agriculture IoT
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Solution Overview
Problem
In precision farming, existing IoT deployments face challenges in energy efficiency due to high data traffic and power consumption from frequent data collection and transmission, particularly in outdoor agricultural settings where solar power is limited, and conventional sampling methods do not adapt effectively to changing disease conditions, leading to unnecessary resource usage and potential delays in disease detection.
Innovation Solution
A method and system that implement adaptive sampling of agronomic and agromet parameters using a processor-based system, which employs a pre-trained disease prediction model to label data points as favorable or unfavorable for disease onset, computes weighted moving averages and standard deviations to identify valid changes, and adjusts the sampling rate based on these changes, reducing power consumption and data transmission only when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data is collected and transmitted at a higher frequency, then disease detection accuracy is improved, but power consumption and resource usage increase
Solution Approach 1:
The sampling rate is made dynamic and adaptive rather than fixed. The system continuously monitors parameter changes and adjusts the sampling frequency accordingly - increasing it when significant changes are detected and decreasing it when conditions are stable, thus optimizing both detection accuracy and energy consumption
Solution Approach 2:
The system changes the sampling rate parameter based on the observed state of agricultural parameters. When parameters show significant deviation from normal ranges or rapid changes, the sampling rate increases to capture critical data points; otherwise, the sampling rate decreases to conserve energy
2Measurement precision
If data is collected and transmitted at a higher frequency, then disease detection accuracy is improved, but data transmission load increases
Solution Approach 1:
The data transmission frequency is dynamically adjusted based on the significance of parameter changes. The system transmits data at high frequency only when critical changes are detected that may indicate disease onset, and reduces transmission frequency during stable periods, thereby reducing overall data volume while maintaining detection accuracy
Solution Approach 2:
The system extracts and transmits only the essential data points that indicate significant changes in agricultural parameters. By filtering out redundant data during stable periods and focusing transmission on critical events, the system reduces data transmission volume while preserving disease detection capability
3Loss of information
If fixed high sampling rate is used, then data availability is improved, but energy efficiency deteriorates
Solution Approach 1:
The sampling rate transitions from a fixed high value to a dynamic value that adapts to actual conditions. The system maintains high sampling rates only when necessary to capture critical disease indicators, and reduces sampling rates during stable periods, thus preserving data availability while improving energy efficiency
Solution Approach 2:
The system uses feedback from continuous monitoring of agricultural parameters to adjust the sampling rate. By analyzing trends and deviations in real-time, the system determines when high-frequency sampling is necessary to maintain data availability and when lower-frequency sampling suffices, optimizing energy efficiency
Data Source
AI summary
This disclosure relates to precision agriculture that relies on monitoring micro-climatic conditions of a farm to make accurate disease forecasts for better crop protection and improve yield efficiency. Conventional systems face challenge in managing energy and bandwidth of transmission considering the humongous volume of data generated in a field through IoT based sensors. The present disclosure provides energy-efficient adaptive parameter sampling from the field by optimally configuring the parameter sampling rate thereby maximizing energy-efficiency. This helps reduce unnecessary traffic to a cloud while extending network lifetime.