Artificial intelligence controller for automatic multispectral camera parameter adjustment

For machine vision and remote sensing systems, image quality is the major factor directly affecting the final image processing results. Especially under natural lighting conditions, image color varies significantly as the lighting conditions change. Unfortunately, changing lighting conditions are inevitable in crop field remote sensing applications. This study was undertaken to develop an artificially intelligent controller based on an artificial neural network (ANN) and an adaptive neuro-fuzzy inference system (ANFIS). The aim of the implemented controller is to automatically adjust multispectral camera parameters, such as "gain" and "exposure time," to compensate for changing natural lighting conditions and to acquire white-balanced images. A high-resolution digital multispectral camera was used as the image sensor. A calibration panel with 56% reflectivity over the visible and near-infrared bands was used to provide the white-balance reference. The real-time white-balancing of the image was achieved for three channels of the camera under natural lighting conditions. It was shown through experiments that the developed algorithm was able to complete multispectral camera parameter control within three iterations for each channel. The convergence speed was faster than with conventional control methods.