Capacitive Soil Moisture Sensor Calibration with Arduino

Soil moisture can be measured using a variety of different techniques: gravimetric, nuclear, electromagnetic, tensiometric, hygrometric, among others. The technique explored here uses a gravimetric technique to calibrate a capacitive-type electromagnetic soil moisture sensor. Capacitive soil moisture sensors exploit the dielectric contrast between water and soil, where dry soils have a relative permittivity between 2-6 and water has a value of roughly 80. Accurate measurement of soil water content is essential for applications in agronomy and botany - where the under- and over-watering of soil can result in ineffective or wasted resources. With water occupying up to 60% of certain soils by volume, depending on the specific porosity of the soil, calibration must be carried out in every environment to ensure accurate prediction of water content. Luckily, the accuracy of measurement devices has been increasing while the cost of the sensors have been decreasing. In this experiment, an Arduino board will be used to read the analog signal from the capacitive sensor, which will output voltage values which can be calibrated to volumetric soil moisture content via gravimetric methods.

High Resolution Thermal Camera with Raspberry Pi and MLX90640

Thermal cameras are similar to standard cameras in that they use light to record images. The most significant distinction is that thermal cameras detect and filter light such that only the infrared region of the electromagnetic spectrum is recorded, not the visible region [read more about infrared cameras here]. Shortly after the discovery of the relationship between radiation and the heat given off by black bodies, infrared detectors were patented as a way to predict temperature via non-contact instrumentation. In recent decades, as integrated circuits shrink in size, infrared detectors have become commonplace in applications of non-destructive testing, medical device technology, and motion detection of heated bodies. The sensor used here is the MLX90640 [datasheet], which is a 768 pixel (24x32) thermal camera. It uses an array of infrared detectors (and likely filters) to detect the radiation given off by objects. Along with a Raspberry Pi computer, the MLX90640 will be used to map and record fairly high-resolution temeperature maps. Using Python, we will be able to push the RPI to its limits by interpolating the MLX90640 to create a 3 frame-per-second (fps) thermal camera at 240x320 pixel resolution.

MPS20N0040D Pressure Sensor Calibration with Arduino

Pressure is defined as an evenly distributed force acting over a surface with a given area. The accurate measurement of pressure is essential for applications ranging from material testing to weighing scales, aircraft altitude prediction, and evaluating biological functions in humans relating to respiration and blood flow In this tutorial, a digital pressure transducer and analog pressure manometer will be used to measure gauge pressure - where the analog manometer is used as the calibration tool for the digital pressure sensor. Arduino will be used to read the digital pressure transducer, an MPS20N0040D, and a 3D printed manometer will be used to measure analog pressure manually.

Accelerometer, Gyroscope, and Magnetometer Analysis with Raspberry Pi Part I: Basic Readings

A Raspberry Pi will be used to read the MPU9250 3-axis acceleration, 3-axis angular rotation speed, and 3-axis magnetic flux (MPU9250 product page can be found here). The output and limitations of the MPU9250 will be explored, which will help define the limitations of applications for each sensor. This is only the first entry into the MPU9250 IMU series, where in the breadth of the articles we will apply advanced techniques in Python to analyze each of the 9-axes of the IMU and develop real-world applications for the sensor, which may be useful to engineers interested in vibration analysis, navigation, vehicle control, and many other areas.