Ghiglieno, I.; Tariku, G.; Sanchez Morchio, A.; Birolleau, C.; Facciano, L.; Gentilin, F.; Mangiapane, S.; Simonetto, A.; Gilioli, G. Vineyard Groundcover Biodiversity: Using Deep Learning to Differentiate Cover Crop Communities from Aerial RGB Imagery. Preprints2024, 2024110488. https://doi.org/10.20944/preprints202411.0488.v1
APA Style
Ghiglieno, I., Tariku, G., Sanchez Morchio, A., Birolleau, C., Facciano, L., Gentilin, F., Mangiapane, S., Simonetto, A., & Gilioli, G. (2024). Vineyard Groundcover Biodiversity: Using Deep Learning to Differentiate Cover Crop Communities from Aerial RGB Imagery. Preprints. https://doi.org/10.20944/preprints202411.0488.v1
Chicago/Turabian Style
Ghiglieno, I., Anna Simonetto and Gianni Gilioli. 2024 "Vineyard Groundcover Biodiversity: Using Deep Learning to Differentiate Cover Crop Communities from Aerial RGB Imagery" Preprints. https://doi.org/10.20944/preprints202411.0488.v1
Abstract
Biodiversity, as an important component of ecosystems, faces unprecedented decline due to factors such as habitat loss. Many ecosystem services depend on biodiversity and influence crop yield and quality. In viticulture, the importance of groundcover management, as cover crops or spontaneous grassing, is particularly important for the conservation of biodiversity and consequently, the preservation of ecosystem services. The integration of remote sensing technologies and artificial intelligence in this context remains limited, especially concerning groundcover classification in viticulture. In response to this challenge, our research focuses on developing a methodology for the identification and classification of groundcovers within vineyards inter-row. Employing an Unmanned Aerial Vehicle, equipped with RGB cameras, and advanced deep learning models, we categorize groundcovers into nine classes of which seven distinct cover crop communities groups. Our study aims to establish a method for identifying vineyard inter-row groundcover, with the ultimate goal of creating a comprehensive groundcover map. The results demonstrate that using the UNet model with backbone architectures significantly enhances classification performance. Specifically, the model with an EfficientNetB0 backbone achieved an accuracy of 85.4%, a mean IoU of 59.8%, and a Jaccard score of 73.0%. This study validates the capabilities remote sensing and deep learning technologies in supporting the monitoring of biodiversity in vineyards, laying the groundwork for future researches to allow the model to monitor larger surface with time and cost advantages and expanding the dataset to encompass a broader range of vineyard types, soil conditions, and geographic.
Environmental and Earth Sciences, Environmental Science
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
