EnviTronics Lab

I've combined two of my previous posts on sensor data processing pipeline into a single article to help understand and leverage the power of sensor data.  In the article, I provide an overview of the intricacies of leaf wetness and soil moisture sensors, explore the challenges of raw data, and provide a step-by-step approach to data processing and analysis.  Key topics covered: • 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲: From raw data to actionable insights • 𝗧𝗲𝗺𝗽𝗲𝗿𝗮𝘁𝘂𝗿𝗲 𝗖𝗼𝗺𝗽𝗲𝗻𝘀𝗮𝘁𝗶𝗼𝗻: Accurately accounting for temperature variations • 𝗡𝗼𝗶𝘀𝗲 𝗥𝗲𝗱𝘂𝗰𝘁𝗶𝗼𝗻: Filtering out unwanted noise • 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀: Classifying sensor data for specific events and conditions To further explore the specific methodologies and findings discussed in the article, I encourage you to consult the references provided at the end. Feel free to reach out with any questions or comments you may have.  Introduction In my r...

  Soil moisture sensors are essential tools for monitoring agricultural and environmental conditions. However, their accuracy can be compromised by various factors. While the impact of each factor may be small, collectively, they can significantly degrade the reliability of sensor measurements. Unraveling the Data Processing Pipeline: A Step-by-Step Guide The figure below outlines the steps involved in converting a raw moisture sensor (soil or soilless) signal into usable percentage measurements. The data processing pipeline includes several key steps: temperature compensation to account for temperature-induced variations, noise reduction to eliminate unwanted signal fluctuations, and baseline correction to establish a reference point for dry conditions. By applying these techniques, the processed data provides a clear representation of the moisture level, facilitating identification and analysis. While signal classification can be a useful tool, its reliability heavily depends on...

We have a leaf wetness sensor that measures leaf wetness by detecting changes in the dielectric constant of its surface. The sensor's analog output provides a continuous signal representing the degree of wetness, while the digital temperature output compensates for environmental temperature variations. The high signal-to-noise ratio (SNR) of the sensor ensures precise measurements, even in challenging conditions like fog or light dew.  Challenges with Raw Sensor Data The raw analog sensor signal is susceptible to noise and temperature variations, making it difficult to accurately correlate sensor readings with specific leaf wetness events like rainfall or dew. Without proper processing, the time-series data can be misleading, as it may be difficult to distinguish between true moisture signals and background noise.  Data Processing Pipeline To overcome the limitations of raw sensor data, I developed a data processing pipeline that involves several key steps: temperature compen...