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

Abstract

A computer generates a training set with annotated images () 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 (-A), changes the color-coding of the pixels to a second color-coding and thereby enhances the contrast (-C), assigns pixels in the second color-coding to binary values (-D), differentiates areas with contiguous pixels in the first binary value into non-insect areas and insect areas by an area size criterion (-E), identifies pixel-coordinates of the insect areas with rectangular tile-areas (-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.