A method comprising: capturing an image of bulk grain using an image source mounted along a grain elevator and/or a tailings elevator of a combine harvester; applying a feature extractor to the image to determine a feature of the bulk grain in the image; for each of a plurality of different sampling locations (132A, 132B, 132C, 132D, 132E) within the image, determining, based upon the feature of the bulk grain at the sampling location, a classification score representing the probability that the material at the sampling location (132A, 132B, 132C, 132D, 132E) belongs to a particular classification of material; calculating a signal indicating quality of the bulk grain of the image based upon an aggregate of the classification scores of the different sampling locations (132A, 132B, 132C, 132D, 132E), and outputting the signal indicating quality of the bulk grain of the image; characterized by the following steps: for each of the plurality of different sampling locations (132A, 132B, 132C, 132D, 132E) in the image, determining, based upon the feature of the bulk grain at the sampling location, a classification score representing the probability that the material at the sampling location (132A, 132B, 132C, 132D, 132E) belongs to a second particular classification of material; outputting the signal indicating quality of the bulk grain of the image also based upon an aggregate of the classification scores of the second classification at each of the sampling locations; and automatically adjusting operational settings of the combine harvester based upon the signal indicating quality of the bulk grain of the image. capturing an image of bulk grain using an image source mounted along a grain elevator and/or a tailings elevator of a combine harvester; applying a feature extractor to the image to determine a feature of the bulk grain in the image; for each of a plurality of different sampling locations (132A, 132B, 132C, 132D, 132E) within the image, determining, based upon the feature of the bulk grain at the sampling location, a classification score representing the probability that the material at the sampling location (132A, 132B, 132C, 132D, 132E) belongs to a particular classification of material; calculating a signal indicating quality of the bulk grain of the image based upon an aggregate of the classification scores of the different sampling locations (132A, 132B, 132C, 132D, 132E), and outputting the signal indicating quality of the bulk grain of the image; characterized by the following steps: for each of the plurality of different sampling locations (132A, 132B, 132C, 132D, 132E) in the image, determining, based upon the feature of the bulk grain at the sampling location, a classification score representing the probability that the material at the sampling location (132A, 132B, 132C, 132D, 132E) belongs to a second particular classification of material; outputting the signal indicating quality of the bulk grain of the image also based upon an aggregate of the classification scores of the second classification at each of the sampling locations; and automatically adjusting operational settings of the combine harvester based upon the signal indicating quality of the bulk grain of the image. for each of the plurality of different sampling locations (132A, 132B, 132C, 132D, 132E) in the image, determining, based upon the feature of the bulk grain at the sampling location, a classification score representing the probability that the material at the sampling location (132A, 132B, 132C, 132D, 132E) belongs to a second particular classification of material; outputting the signal indicating quality of the bulk grain of the image also based upon an aggregate of the classification scores of the second classification at each of the sampling locations; and automatically adjusting operational settings of the combine harvester based upon the signal indicating quality of the bulk grain of the image.