ArticleFrontiers in artificial intelligence2022
Behind the Leaves: Estimation of Occluded Grapevine Berries With Conditional Generative Adversarial Networks.
Article in Frontiers in artificial intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Incoherent Region-Aware Occlusion Instance Synthesis for Grape Amodal Detection.Sensors (Basel, Switzerland) · 2025Article
- VitiForge: a new procedural pipeline approach for grapevine disease identification under data scarcity.Frontiers in plant science · 2025Article
- Data-driven crop growth simulation on time-varying generated images using multi-conditional generative adversarial networks.Plant methods · 2024Article
- Behind the Leaves: Estimation of Occluded Grapevine Berries With Conditional Generative Adversarial Networks.Frontiers in artificial intelligence · 2022Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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 highly probable 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.
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