ArticleHorticulture research2020
Deep learning image segmentation and extraction of blueberry fruit traits associated with harvestability and yield.
Article in Horticulture research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 128 citations in OpenAlex.
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- Image-Based Assessment of External Quality Traits in Chinese Bayberry (Foods (Basel, Switzerland) · 2026Article
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- Physiological and image-based characterization ofFrontiers in plant science · 2026Article
- Mapping of cotton bolls and branches with high-granularity through point cloud segmentation.Plant methods · 2025Article
- Cherry-Net: real-time segmentation algorithm of cherry maturity based on improved PIDNet.Frontiers in plant science · 2025Article
- BerryPortraits: Phenotyping Of Ripening Traits cranberry (Vaccinium macrocarpon Ait.) with YOLOv8.Plant methods · 2024Article
- Automatic plant phenotyping analysis of Melon (Cucumis melo L.) germplasm resources using deep learning methods and computer vision.Plant methods · 2024Article
- FruitSeg30_Segmentation dataset & mask annotations: A novel dataset for diverse fruit segmentation and classification.Data in brief · 2024Article
- Melon ripeness detection by an improved object detection algorithm for resource constrained environments.Plant methods · 2024Article
- Transforming Poultry Farming: A Pyramid Vision Transformer Approach for Accurate Chicken Counting in Smart Farm Environments.Sensors (Basel, Switzerland) · 2024Article
- AppleQSM: Geometry-Based 3D Characterization of Apple Tree Architecture in Orchards.Plant phenomics (Washington, D.C.) · 2024Article
- Development of a deep-learning phenotyping tool for analyzing image-based strawberry phenotypes.Frontiers in plant science · 2024Article
- Tomato maturity recognition with convolutional transformers.Scientific reports · 2023Article
- Convolutional neural networks in the qualitative improvement of sweet potato roots.Scientific reports · 2023Article
- Fruit Sizing in Orchard: A Review from Caliper to Machine Vision with Deep Learning.Sensors (Basel, Switzerland) · 2023Review
- An improved Deeplab V3+ network based coconut CT image segmentation method.Frontiers in plant science · 2023Article
- Real-Time Prediction of Growth Characteristics for Individual Fruits Using Deep Learning.Sensors (Basel, Switzerland) · 2022Article
- Multi-Information Model for Large-Flowered Chrysanthemum Cultivar Recognition and Classification.Frontiers in plant science · 2022Article
- BFP Net: Balanced Feature Pyramid Network for Small Apple Detection in Complex Orchard Environment.Plant phenomics (Washington, D.C.) · 2022Article
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
Fruit traits such as cluster compactness, fruit maturity, and berry number per clusters are important to blueberry breeders and producers for making informed decisions about genotype selection related to yield traits and harvestability as well as for plant management. The goal of this study was to develop a data processing pipeline to count berries, to measure maturity, and to evaluate compactness (cluster tightness) automatically using a deep learning image segmentation method for four southern highbush blueberry cultivars ('Emerald', 'Farthing', 'Meadowlark', and 'Star'). An iterative annotation strategy was developed to label images that reduced the annotation time. A Mask R-CNN model was trained and tested to detect and segment individual blueberries with respect to maturity. The mean average precision for the validation and test dataset was 78.3% and 71.6% under 0.5 intersection over union (IOU) threshold, and the corresponding mask accuracy was 90.6% and 90.4%, respectively. Linear regression of the detected berry number and the ground truth showed an
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.