Abstract: An industrialized system and method for rice grain recognition. An optical image is taken and transmitted to a digital platform, wherein the system segments the optical image and extracts and/or measures appropriate grain features from the image describing different aspects of the grain. The image is processed by the system which includes a selector selecting different machine learning structures, applying the different machine learning structures to the extracted features for rice grain recognition, and selecting the best of the applied machine learning structures by a random sampling process. The selected best of the applied machine learning structures is further optimized by varying an appropriate threshold by a threshold trigger based on a confusion matrix comprising. An active learning structure based on the confusion matrix to the user. The system is retrained based on the feedback parameters of the feedback loop.

Abstract: The present invention relates to a device (1) for treating cells for stimulating cell growth, comprising a unit (2) for generating and emitting electric pulses and an electric field resulting therefrom, and a treatment space (3) that can be penetrated by the emitted electric pulses, wherein the device (1) further comprises a unit (4, 9) which can be moved into and out of said treatment space (3) such that cell material provided within a compartment (4a) of said unit (4), or provided in a container (5) on or in said unit (4, 9), can be moved into and out of said treatment space (3), and wherein said treatment space (3) is provided between two electrodes (2a, 2b) or plates of a capacitor parallel to each other.

Abstract: An industrialized system and method for rice grain recognition. An optical image is taken and transmitted to a digital platform, wherein the system segments the optical image and extracts and/or measures appropriate grain features from the image describing different aspects of the grain. The image is processed by the system which includes a selector selecting different machine learning structures, applying the different machine learning structures to the extracted features for rice grain recognition, and selecting the best of the applied machine learning structures by a random sampling process. The selected best of the applied machine learning structures is further optimized by varying an appropriate threshold by a threshold trigger based on a confusion matrix comprising. An active learning structure based on the confusion matrix to the user. The system is retrained based on the feedback parameters of the feedback loop.