Novel image processing approach for solving the overlapping problem in agriculture

A general problem in computer vision is the detection of objects when they are partially occluded. This problem also extends to the identification of horticultural/agricultural products (e.g., plants and crops), where recognition can be very cumbersome due to the heavy overlapping situations that one can find. This paper presents a novel approach to solve the recognition of plantlets under such conditions. The methodology consists of two major steps: (1) The simplification of the complexity of leaf shapes by using ellipse approximation. (2) The clustering of the leaves (ellipses) found into plantlets using active shape models. Shape models of experimental plants with 2, 3 and 4 leaves were tested to analyse the ability of the method to overcome the overlapping problem. The results indicate that the presented technique is able to perform identification of individual plantlets under overlapping situations, by first decreasing the complexity of their form and then using these simplified characteristics in a statistical shape model.

► Identification of seedlings under overlapping situations is carried out.
► A simplification of the original shape using ellipses is performed.
► A new ellipse matching system to identify leaves is proposed.
► Plants are a set of landmarks extracted from the detected ellipses.
► Active shape models are use to cluster leaves to form plants.