ArticleFrontiers in plant science2021
Outdoor Plant Segmentation With Deep Learning for High-Throughput Field Phenotyping on a Diverse Wheat Dataset.
Article in Frontiers in plant science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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24 citing papers in PubMed, 67 citations in OpenAlex.
- A Hybrid 2D-3D RGB-D Method for Growth Axis Estimation and Robotic Grasp Pose Generation of Phalaenopsis Orchid Buds.MethodsX · 2026Article
- Plant counting: origins, development, and future paths.Plant phenomics (Washington, D.C.) · 2026Review
- Machine Learning-Driven Construction of High-Yielding Cucumber Plant Architectures in Greenhouse Environments.Plant biotechnology journal · 2026Article
- A Leaf-Level Dataset for Soybean-Cotton Detection and Segmentation.Scientific data · 2026Article
- Root segmentation beyond species boundaries: A generalizable framework for anatomical analysis.Plant phenomics (Washington, D.C.) · 2026Article
- FIP 1.0 soybean data: Insights on soybean growth from eight years of high-throughput image field phenotyping.Scientific data · 2026Article
- An intelligent method and platform for obtaining lettuce canopy coverage.Frontiers in plant science · 2026Article
- The Global Wheat Full Semantic Organ Segmentation (GWFSS) dataset.Plant phenomics (Washington, D.C.) · 2025Article
- The blessing of Depth Anything: An almost unsupervised approach to crop segmentation with depth-informed pseudo labeling.Plant phenomics (Washington, D.C.) · 2025Article
- The FIP 1.0 Data Set: Highly resolved annotated image time series of 4,000 wheat plots grown in 6 years.GigaScience · 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
- Artificial intelligence in plant science: from image-based phenotyping to yield and trait prediction.Frontiers in plant science · 2025Review
- Unsupervised semantic label generation in agricultural fields.Frontiers in robotics and AI · 2025Article
- From Neglecting to Including Cultivar-Specific Per Se Temperature Responses: Extending the Concept of Thermal Time in Field Crops.Plant phenomics (Washington, D.C.) · 2024Article
- Drone-Based Digital Phenotyping to Evaluating Relative Maturity, Stand Count, and Plant Height in Dry Beans (Plant phenomics (Washington, D.C.) · 2024Article
- A Novel Method for Quantifying Plant Morphological Characteristics Using Normal Vectors and Local Curvature Data via 3D Modelling-A Case Study in Leaf Lettuce.Sensors (Basel, Switzerland) · 2023Article
- VegAnn, Vegetation Annotation of multi-crop RGB images acquired under diverse conditions for segmentation.Scientific data · 2023Article
- Identification of rice leaf diseases and deficiency disorders using a novel DeepBatch technique.Open life sciences · 2023Article
- Phenotypic Analysis of Diseased Plant Leaves Using Supervised and Weakly Supervised Deep Learning.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
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Robust and automated segmentation of leaves and other backgrounds is a core prerequisite of most approaches in high-throughput field phenotyping. So far, the possibilities of deep learning approaches for this purpose have not been explored adequately, partly due to a lack of publicly available, appropriate datasets. This study presents a workflow based on DeepLab v3+ and on a diverse annotated dataset of 190 RGB (350 x 350 pixels) images. Images of winter wheat plants of 76 different genotypes and developmental stages have been acquired throughout multiple years at high resolution in outdoor conditions using nadir view, encompassing a wide range of imaging conditions. Inconsistencies of human annotators in complex images have been quantified, and metadata information of camera settings has been included. The proposed approach achieves an intersection over union (IoU) of 0.77 and 0.90 for plants and soil, respectively. This outperforms the benchmarked machine learning methods which use Support Vector Classifier and/or Random Forrest. The results show that a small but carefully chosen and annotated set of images can provide a good basis for a powerful segmentation pipeline. Compared to earlier methods based on machine learning, the proposed method achieves better performance on the selected dataset in spite of using a deep learning approach with limited data. Increasing the amount of publicly available data with high human agreement on annotations and further development of deep neural network architectures will provide high potential for robust field-based plant segmentation in the near future. This, in turn, will be a cornerstone of data-driven improvement in crop breeding and agricultural practices of global benefit.
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