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: In performing a computer-implemented method to quantify biotic damage in leaves of crop-plants, the computer receives a plant-image (410) showing a crop-plant, showing the aerial part of the plant, with stem, branches, and leaves and showing the ground on that the plant is placed. A segmenter module obtains a segmented plant-image being a contiguous set of pixels that shows in a contour (460A) of the aerial part, the contour (460A) having a margin region (458) that shows the ground partially. The computer uses convolutional neural network that processing the segmented plant-image by regression to obtain a damage degree, the convolutional neural network having been trained by processing damage-annotated segmented plant-images.

Abstract: To quantify biotic damage in leaves of crop plants, a computer receives (701A) a leaf-image taken from a particular crop plant. The leaf-image shows at least one of the leaves of the particular crop plant. Using a first convolutional neural network (CNN, 262), the computer processes the leaf-image to derive a segmented leaf-image (422) being a contiguous set of pixels that show a main leaf of the particular plant completely. The first CNN has been trained by a plurality of leaf-annotated leaf-images (601A), wherein the leaf-images are annotated to identify main leaves (461). Using a second CNN (272), the computer processes the single-leaf-image by regression to obtain a damage degree (432).

Abstract: Systems and methods for determining an operating condition of a chemical production plant including at least one catalytic reactor are provided. Via a communication interface operating data and a catalyst age indicator are received (10). At least one target operating parameter for the operating condition of a scheduled production run or a current production run are determined (14). The at least one target operating parameter for the operating condition may be used for monitoring and/or controlling the chemical production plant.