QUANTIFYING PLANT INFESTATION BY ESTIMATING THE NUMBER OF BIOLOGICAL OBJECTS ON LEAVES, BY CONVOLUTIONAL NEURAL NETWORKS THAT USE TRAINING IMAGES OBTAINED BY A SEMI-SUPERVISED APPROACH

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.