ArticlePlant phenomics (Washington, D.C.)2022
SegVeg: Segmenting RGB Images into Green and Senescent Vegetation by Combining Deep and Shallow Methods.
Article in Plant phenomics (Washington, D.C.), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
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- Segmenting vegetation from UAV images via spectral reconstruction in complex field environments.Plant phenomics (Washington, D.C.) · 2025Article
- Predictive modelling employing machine learning, convolutional neural networks (CNNs), and smartphone RGB images for non-destructive biomass estimation of pearl millet (Frontiers in plant science · 2025Article
- Image analysis and polyphenol profiling unveil red-flesh apple phenotype complexity.Plant methods · 2024Article
- SeptoSympto: a precise image analysis of Septoria tritici blotch disease symptoms using deep learning methods on scanned images.Plant methods · 2024Article
- Affordable Phenotyping at the Edge for High-Throughput Detection of Hypersensitive Reaction Involving Cotyledon Loss.Plant phenomics (Washington, D.C.) · 2024Article
- Estimation of sorghum seedling number from drone image based on support vector machine and YOLO algorithms.Frontiers in plant science · 2024Article
- VegAnn, Vegetation Annotation of multi-crop RGB images acquired under diverse conditions for segmentation.Scientific data · 2023Article
- An Open-Source Package for Thermal and Multispectral Image Analysis for Plants in Glasshouse.Plants (Basel, Switzerland) · 2023Article
- Analyzing Changes in Maize Leaves Orientation due to GxExM Using an Automatic Method from RGB Images.Plant phenomics (Washington, D.C.) · 2023Article
- Combining High-Resolution Imaging, Deep Learning, and Dynamic Modeling to Separate Disease and Senescence in Wheat Canopies.Plant phenomics (Washington, D.C.) · 2023Article
- Enhancing Green Fraction Estimation in Rice and Wheat Crops: A Self-Supervised Deep Learning Semantic Segmentation Approach.Plant phenomics (Washington, D.C.) · 2023Article
- To What Extent Does Yellow Rust Infestation Affect Remotely Sensed Nitrogen Status?Plant phenomics (Washington, D.C.) · 2023Article
- Comparing CNNs and PLSr for estimating wheat organs biophysical variables using proximal sensing.Frontiers in plant science · 2023Article
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Authors and funding
7 authors.
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Abstract
Pixel segmentation of high-resolution RGB images into chlorophyll-active or nonactive vegetation classes is a first step often required before estimating key traits of interest. We have developed the SegVeg approach for semantic segmentation of RGB images into three classes (background, green, and senescent vegetation). This is achieved in two steps: A U-net model is first trained on a very large dataset to separate whole vegetation from background. The green and senescent vegetation pixels are then separated using SVM, a shallow machine learning technique, trained over a selection of pixels extracted from images. The performances of the SegVeg approach is then compared to a 3-class U-net model trained using weak supervision over RGB images segmented with SegVeg as groundtruth masks. Results show that the SegVeg approach allows to segment accurately the three classes. However, some confusion is observed mainly between the background and senescent vegetation, particularly over the dark and bright regions of the images. The U-net model achieves similar performances, with slight degradation over the green vegetation: the SVM pixel-based approach provides more precise delineation of the green and senescent patches as compared to the convolutional nature of U-net. The use of the components of several color spaces allows to better classify the vegetation pixels into green and senescent. Finally, the models are used to predict the fraction of three classes over whole images or regularly spaced grid-pixels. Results show that green fraction is very well estimated (
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