Microclimate Prediction Using Neural Networks and Sensor Grids
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Solution Overview
Problem
Current methods for monitoring microclimate characteristics in geographical regions, such as farms, are costly due to the need for a large number of sensors and redundant data collection, especially in regions with diverse climate and land composition, making it prohibitive to gather and retain knowledge of climate and land conditions effectively.
Innovation Solution
The use of a non-uniform grid of sensors based on climate and land composition attributes, combined with deep learning techniques to predict physical characteristics like leaf wetness, solar radiation, evapotranspiration, and soil moisture using fewer and less expensive sensors, and a neural network to estimate future values from received sensor data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of sensors are deployed to monitor microclimate characteristics in diverse geographical regions, then measurement precision and reliability improve, but device complexity and cost increase significantly
Solution Approach 1:
The geographical region is divided into multiple zones based on climate and land composition attributes. Sensors are strategically placed in representative locations within each zone rather than uniformly distributed, reducing the total number of sensors needed while maintaining measurement accuracy for diverse microclimate conditions.
Solution Approach 2:
A single sensor platform is designed to measure multiple microclimate parameters simultaneously (temperature, humidity, wind speed, rainfall, solar radiation). This multi-functional approach replaces what would otherwise require multiple separate sensor deployments, reducing overall system complexity.
2Loss of information
If traditional sensor networks are used to collect climate and land condition data, then data coverage improves, but cost and data collection burden increase
Solution Approach 1:
Machine learning models serve as intermediaries that infer unmeasured microclimate characteristics from measured sensor data. The models predict parameters like soil moisture, evapotranspiration, and leaf wetness based on correlations learned from historical data, eliminating the need for expensive specialized sensors while maintaining information completeness.
Solution Approach 2:
The system transforms physical sensor measurements into predicted values for multiple derived parameters through machine learning modeling. This parameter transformation allows estimation of difficult-to-measure quantities (e.g., soil moisture from temperature and humidity data) without direct physical measurement, reducing material costs.
3Ease of operation
If uniform sensor distribution is used across geographical regions, then ease of deployment improves, but adaptability to diverse climate zones deteriorates
Solution Approach 1:
The sensor deployment strategy adapts to local conditions by placing sensors in representative locations within each climate zone based on terrain analysis and climate attributes. Each zone has optimized sensor placement rather than uniform distribution, allowing the system to adapt to diverse geographical conditions while maintaining deployment simplicity through automated zone identification.
Data Source
AI summary
A method can include receiving sensor data from at least three different types of sensor situated in the geographic area, the types of sensors including an air temperature sensor, relative humidity sensor, dewpoint sensor, soil moisture sensor, soil temperature sensor, average wind speed sensor, maximum wind speed sensor, and a rainfall sensor, producing a feature vector including a time series of values corresponding to the received sensor data, and using a neural network, estimating the physical characteristics, the physical characteristics including at least one of (a) a leaf wetness, (b) a solar radiation, (c) an evapotranspiration, (d) a future soil moisture, and (e) a future soil temperature.