Yield Prognosis for the Agrarian Management of Vineyards using Deep Learning for Object Counting

Abstract

In various applications, the counting of objects based on image data plays a pivotal role. In this paper we first conducted a literature review to display the state of the art in counting objects and summarized the results by extracting several important concepts that describe the counting problem as well as the solution. In a second step we applied this knowledge to yield prognosis in vineyards, where we used Deep Learning models to detect the objects. While these methods used in the detection step are state of the art and perform very well, several problems are usually introduced by the constraint of only counting an object once in the counting step. We provide a solution for this common problem by identifying unique objects and tracking them throughout a sequence of images in order to avoid counting objects more than once, resulting in an automated yield prognosis model for vineyards