||Plankton microorganisms constitute the base of the marine food web and play a great role in global atmospheric carbon dioxide drawdown. Moreover, being very sensitive to any environmental changes they allow noticing (and potentially counteracting) them faster than with any other means. As such they not only influence the fishery industry but are also frequently used to analyze changes in exploited coastal areas and the influence of these interferences on local environment and climate. As a consequence, there is a strong need for highly efficient systems allowing long time and large volume observation of plankton communities. This would provide us with better understanding of plankton role on global climate as well as help maintain the fragile environmental equilibrium. The adopted sensors typically provide huge amounts of data that must be processed efficiently without the need for intensive manual work of specialists. A new system for general purpose particle analysis in large volumes is presented. It has been designed and optimized for the continuous plankton monitoring problem; however, it can be easily applied as a versatile moving fluids analysis tool or in any other application in which targets to be detected and identified move in a unidirectional flux. The proposed system is composed of three stages: data acquisition, targets detection and their identification. Dedicated optical hardware is used to record images of small particles immersed in the water flux. Targets detection is performed using a Visual Rhythm-based method which greatly accelerates the processing time and allows higher volume throughput. The proposed method detects, counts and measures organisms present in water flux passing in front of the camera. Moreover, the developed software allows saving cropped plankton images which not only greatly reduces required storage space but also constitutes the input for their automatic identification. In order to assure maximal performance (up to 720 MB/s) the algorithm was implemented using CUDA for GPGPU. The method was tested on a large dataset and compared with alternative frame-by-frame approach. The obtained plankton images were used to build a classifier that is applied to automatically identify organisms in plankton analysis experiments. For this purpose a dedicated feature extracting software was developed. Various subsets of the 55 shape characteristics were tested with different off-the-shelf learning models. The best accuracy of approximately 92% was obtained with Support Vector Machines. This result is comparable to the average expert manual identification performance.