Behind the leaves -- Estimation of occluded grapevine berries with conditional generative adversarial networks

TL;DR

This paper introduces a deep learning method using conditional generative adversarial networks to estimate occluded grapevine berries, improving yield predictions by modeling hidden berries behind leaves.

Contribution

The novel approach leverages GANs to generate plausible scenarios of occluded berries, enhancing accuracy over traditional counting methods that rely on simple factors.

Findings

Estimated berry counts are closer to manual references.

Method adapts to local conditions by modeling hidden berries.

Can identify regions needing augmentation without explicit hidden area info.

Abstract

The need for accurate yield estimates for viticulture is becoming more important due to increasing competition in the wine market worldwide. One of the most promising methods to estimate the harvest is berry counting, as it can be approached non-destructively, and its process can be automated. In this article, we present a method that addresses the challenge of occluded berries with leaves to obtain a more accurate estimate of the number of berries that will enable a better estimate of the harvest. We use generative adversarial networks, a deep learning-based approach that generates a likely scenario behind the leaves exploiting learned patterns from images with non-occluded berries. Our experiments show that the estimate of the number of berries after applying our method is closer to the manually counted reference. In contrast to applying a factor to the berry count, our approach better adapts to local conditions by directly involving the appearance of the visible berries. Furthermore, we show that our approach can identify which areas in the image should be changed by adding new berries without explicitly requiring information about hidden areas.