Computer vision system for automated cell pressure probe operation

An automated cell pressure probe was designed to allow robust measurements of cellular water relations without the limitations caused by operator skill levels (rapid response time, good hand–eye coordination, and manual dexterity). The system was based upon a real-time (30 Hz) computer vision system that employed a radial basis function, artificial neural network using direct greyscale template matching for meniscus recognition and control. The system did not require any custom-built hardware but used readily available off-the-shelf components and was designed to be highly portable, scalable, and relatively simple to set up. The system was robust in the presence of debris or dust on or in the glass capillary, and for tapered capillary probes, tolerant of variations in contrast due to changes in the velocity or travel direction of the meniscus. The system was capable of controlling the pressure within ±0.0005 MPa and could perform pressure-clamp experiments at ±0.001 MPa or greater pressure steps. The automatic meniscus system recognition could accurately detect the meniscus location with a mean error of under 6 μm and a standard deviation of less than 3.2 μm. Mean errors in automatic volume estimation ranged from 0.07 to 0.6%, depending upon capillary diameter.