Industrialized system for rice grain recognition, the system comprising an optical capturing device for capturing optical images, the optical capturing device comprising a data interface for transmitting the optical images to a digital platform over a data transmission network, wherein the optical images are analyzed by the digital platform and a response to the rice recognition is provided to the optical capturing device and/or a user device, wherein the digital platform comprises a segmentation module segmenting the captured optical image for grains by identifying optical image segments capturing grains or grain parts, and wherein the digital platform comprises a feature extractor extracting measurable grain features from the identified grains or grain parts in the optical image, characterized in that the features describing different parametrized aspects of the grains by a feature extraction process at least comprise shape parameter values and color parameter values and/or spatial parameters and/or geometric parameters, in that, in a learning mode, the digital platform comprises a binary classifier with a selector for sequential selecting of a plurality of machine learning structures (11211,...,1121i), the selector applying the different machine learning structures (11211,...,1121i) to the extracted grain features for rice grain recognition, and selecting an applied machine learning structure of the applied machine learning structures (11211,...,1121i), which has a number of grains classified as false positive as low as possible while keeping the overall accuracy as high as possible, and in that the selected machine learning structure of the applied machine learning structures (11211,...,1121i) is further optimized by varying a discrimination threshold by means of a threshold trigger, wherein the classified false positives are further minimized to a determined threshold probability for a grain to be classified as good, so that the variation of the discrimination threshold varies the diagnostic ability of the binary classifier related to the variation of the discrimination threshold. in that the features describing different parametrized aspects of the grains by a feature extraction process at least comprise shape parameter values and color parameter values and/or spatial parameters and/or geometric parameters, in that, in a learning mode, the digital platform comprises a binary classifier with a selector for sequential selecting of a plurality of machine learning structures (11211,...,1121i), the selector applying the different machine learning structures (11211,...,1121i) to the extracted grain features for rice grain recognition, and selecting an applied machine learning structure of the applied machine learning structures (11211,...,1121i), which has a number of grains classified as false positive as low as possible while keeping the overall accuracy as high as possible, and in that the selected machine learning structure of the applied machine learning structures (11211,...,1121i) is further optimized by varying a discrimination threshold by means of a threshold trigger, wherein the classified false positives are further minimized to a determined threshold probability for a grain to be classified as good, so that the variation of the discrimination threshold varies the diagnostic ability of the binary classifier related to the variation of the discrimination threshold.