Abstract: A device for determining the chemical composition of a liquid metallurgical product emitting electromagnetic radiations. The device including a collection probe configured to acquire the electromagnetic radiations emitted by the metallurgical product in a predetermined wavelength range ??, a spectroscopic device connected to the collection probe and configured to generate a spectral signal of the acquired electromagnetic radiations and processing means including a database of reference radiances. A method using the device is also provided.

Abstract: Quantifying plant infestation is performed by estimating the number of biological objects (132) on parts (122) of a plant (112). A computer (202) receives a plant-image (412) taken from a particular plant (112). The computer (202) uses a first convolutional neural network (262/272) to derive a part-image (422) that shows a part of the plant. The computer (202) splits the part-image into tiles and uses a second network to process the tiles to density maps. The computer (202) combines the density maps to a combined density map in the dimension of the part-image and integrates the pixel values to an estimate number of objects for the part. Object classes (132(1), 132(2)) can be differentiated to fine-tune the quantification to identify class-specific countermeasures.

Abstract: A computer generates a training set with annotated images (473) to train a convolutional neural network (CNN). The computer receives leaf-images showing leaves and biological objects such as insects, in a first color-coding (413-A), changes the color-coding of the pixels to a second color-coding and thereby enhances the contrast (413-C), assigns pixels in the second color-coding to binary values (413-D), differentiates areas with contiguous pixels in the first binary value into non-insect areas and insect areas by an area size criterion (413-E), identifies pixel-coordinates of the insect areas with rectangular tile-areas (413-F), and annotates the leaf-images in the first color-coding by assigning the pixel-coordinates to corresponding tile-areas. The annotated image is then used to train the CNN for quantifying plant infestation by estimating the number of biological object such as insects on the leaves of plants.

Abstract: A computer-implemented method, computer program product and computer system (100) for detecting plant diseases. The system stores a convolutional neural network (120) trained with a multi-crop dataset. The convolutional neural network (120) has an extended topology comprising an image branch (121) based on a classification convolutional neural network for classifying the input images according to plant disease specific features, a crop identification branch (122) for adding plant species information, and a branch integrator for integrating the plant species information with each input image. The plant species information (20) specifies the crop on the respective input image (10). The system receives a test input comprising an image (10) of a particular crop (1) showing one or more particular plant disease symptoms, and further receives a respective crop identifier (20) associated with the test input via an interface (110).

Abstract: A system (100), method and computer program product for determining plant diseases. The system includes an interface module (110) configured to receive an image (10) of a plant, the image (10) including a visual representation (11) of at least one plant element (1). A color normalization module (120) is configured to apply a color constancy method to the received image (10) to generate a color-normalized image. An extractor module (130) is configured to extract one or more image portions (11e) from the color-normalized image wherein the extracted image portions (11e) correspond to the at least one plant element (1).

Abstract: A system (100), method and computer program product for determining plant diseases. The system includes an interface module (110) configured to receive an image (10) of a plant, the image (10) including a visual representation (11) of at least one plant element (1). A color normalization module (120) is configured to apply a color constancy method to the received image (10) to generate a color-normalized image. An extractor module (130) is configured to extract one or more image portions (11e) from the color-normalized image wherein the extracted image portions (11e) correspond to the at least one plant element (1).