Abstract
Monitoring groundcover diversity in vineyards is a complex task, often limited by the time and expertise required for accurate botanical identification. Remote sensing technologies and AI-based tools are still underutilized in this context, particularly for classifying herbaceous vegetation in inter-row areas. In this study, we introduce a novel approach to classify the groundcover into one of nine categories, in order to simplify this task. Using UAV images to train a convolutional neural network through a deep learning methodology, this study evaluates the effectiveness of different backbone structures applied to a UNet network for the classification of pixels into nine classes of groundcover: vine canopy, bare soil, and seven distinct cover crop community types. Our results demonstrate that the UNet model, especially when using an EfficientNetB0 backbone, significantly improves classification performance, achieving 85.4% accuracy, 59.8% mean Intersection over Union (IoU), and a Jaccard index of 73.0%. Although this study demonstrates the potential of integrating remote sensing and deep learning for vineyard biodiversity monitoring, its applicability is limited by the small image coverage, as data were collected from a single vineyard and only one drone flight. Future work will focus on expanding the model’s applicability to a broader range of vineyard systems, soil types, and geographic regions, as well as testing its performance on lower-resolution multispectral imagery to reduce data acquisition costs and time, enabling large-scale and cost-effective monitoring.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by. Read at the source ↗
The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 4
Claim · model-written summary
A UNet with an EfficientNetB0 backbone classified vineyard groundcover with 85.4% accuracy.
Verbatim
e1“achieving 85.4% accuracy, 59.8% mean Intersection over Union (IoU), and a Jaccard index of 73.0%”
e2“especially when using an EfficientNetB0 backbone, significantly improves classification performance”
e3“seven distinct cover crop community types”
Claim · model-written summary
The same UNet model reached a mean Intersection over Union of 59.8% for groundcover classes.
Verbatim
e1“achieving 85.4% accuracy, 59.8% mean Intersection over Union (IoU), and a Jaccard index of 73.0%”
e2“especially when using an EfficientNetB0 backbone, significantly improves classification performance”
e3“seven distinct cover crop community types”
Claim · model-written summary
The same UNet model reached a Jaccard index of 73.0% for groundcover classes.
Verbatim
e1“achieving 85.4% accuracy, 59.8% mean Intersection over Union (IoU), and a Jaccard index of 73.0%”
e2“especially when using an EfficientNetB0 backbone, significantly improves classification performance”
e3“seven distinct cover crop community types”
Claim · model-written summary
The UNet with the EfficientNetB0 backbone significantly improved classification performance relative to the other backbone structures evaluated.
Verbatim
e2“especially when using an EfficientNetB0 backbone, significantly improves classification performance”
e3“seven distinct cover crop community types”
e4“evaluates the effectiveness of different backbone structures applied to a UNet network”