Do I Need to Calibrate My Sensor Before Using?

This is a question I hear a lot, and I'll try to answer it generally, without focusing on any specific sensor brand.

Sensor manufacturers often offer calibration services, sometimes at a significant cost. Their sensors might even come pre-calibrated. You'll often hear (and read, even from people like me) that sensor calibration is essential! The idea is that calibration, or recalibration, improves accuracy.

So, let's explore what sensor calibration is and when you can (and can't) skip it.

First, some definitions to ensure we're all on the same page, especially in the context of agricultural sensing:

1. Sensor: In agriculture, this is often a device that measures a key environmental parameter like soil moisture (e.g., a capacitance sensor) or canopy temperature (e.g., an infrared thermal sensor) and converts it into an electrical output (analog voltage or current).

   
   

1. Analog-to-Digital Conversion: Most modern data loggers and controllers used in agriculture convert the sensor's analog output into digital raw values. Some sensors have built-in A-to-D converters, directly providing a digital raw output. These digital numbers are unitless. While they fluctuate with changes in the measured parameter, their values might not be immediately meaningful. For example, a soil moisture sensor might output a raw value of 16,000 for a certain soil water content, or a thermal sensor might give a raw reading of 2500.

1. Calibration: Sensor calibration is the process of establishing a reliable relationship between the sensor's raw output (digital values) and the actual, physically meaningful measurements of the target parameter (e.g., volumetric soil water content in %, or canopy temperature in °C). This usually involves developing a mathematical equation (or model) that describes this relationship. We use this equation to automatically convert the sensor's raw readings into values with units. This relationship can be as simple as a linear equation or as complex as a machine learning model. For example, you might calibrate a soil moisture sensor by comparing its raw output to gravimetric measurements of soil moisture content.

   
   

2. Accuracy: Sensor accuracy reflects how closely a sensor's measurement aligns with the true value. It describes the error or deviation between the sensor's reading and the real value. For example, a highly accurate soil moisture sensor will provide a reading very close to the actual soil water content.

   
   

3. Precision: Sensor precision, also known as repeatability, measures how consistently a sensor produces the same reading for the same input. A precise sensor will give nearly identical readings every time it measures the same value, though those readings might not be perfectly accurate. Imagine a thermal sensor that gives very consistent readings of canopy temperature, but those readings are consistently a few degrees higher than the actual temperature.

   
   

4. Resolution: Sensor resolution is the smallest change in a measured quantity that a sensor can detect and distinguish. It defines the level of detail the sensor can measure. A soil moisture sensor with a high resolution can detect even small changes in soil water content, which can be important for fine-tuning irrigation schedules. A thermal sensor with high resolution can detect subtle temperature differences across a canopy, potentially revealing stress areas.

   
   

Now, the million-dollar question: Do you need to calibrate your sensor?

Imagine you've bought a $10 capacitive soil moisture sensor online. You're excited to try it. You connect it to your Arduino and power it up. Seeing the raw readings in the serial window, you think, "Everything's working!" and are ready to install it. But hold on! It's highly recommended to make a few more measurements. What's the sensor's resolution? Its accuracy? Is it precise? How temperature-dependent are the readings? Will the readings drift over time? Will it hold up in the soil?

With a low-quality sensor, the answers to these questions will likely be discouraging. You won't have reliable readings to begin with. If your sensor isn't reliable—meaning it doesn't score well in the criteria we've discussed (precision, accuracy, resolution, temperature independence, and robustness)—calibration might not be as beneficial. You can't easily turn unreliable sensor output into reliable readings. Calibration isn't magic.

Similarly, machine learning can't magically transform bad sensor data into reliable insights. As they say: garbage in, garbage out!

However, with a high-quality sensor—a sensor that's inherently reliable (meaning it does score well in those criteria)—even without calibration or with only a basic calibration, you might be able to use it effectively.

Personally, I've used soil moisture sensors in projects for years without calibrating. I do calibrate them for research, but rarely for general crop monitoring. I can even interpret the raw outputs, but I rely on experience and focus on trends rather than absolute values. I use a specific methodology that involves establishing a baseline, monitoring relative changes, and correlating sensor readings with observed plant responses, as explained in this post.

You can even use uncalibrated thermal sensors or cameras for crop monitoring—if and only if they are reliable. Because canopy temperature-based approaches for crop monitoring often focus on changes in temperature, not absolute values, you might get away without calibration. However, this requires a robust methodology and isn't as simple as using an uncalibrated soil moisture sensor. The influence of environmental factors (air temperature, humidity, solar radiation) on thermal readings necessitates careful interpretation. We can discuss this in more detail another time.