ArticlePlant phenomics (Washington, D.C.)2023
Semi-supervised Counting of Grape Berries in the Field Based on Density Mutual Exclusion.
Article in Plant phenomics (Washington, D.C.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- CucumberAI: Cucumber Fruit Morphology Identification System Based on Artificial Intelligence.Plant phenomics (Washington, D.C.) · 2024Article
- MTSC-Net: A Semi-Supervised Counting Network for Estimating the Number of Slash pine New Shoots.Plant phenomics (Washington, D.C.) · 2024Article
- One to All: Toward a Unified Model for Counting Cereal Crop Heads Based on Few-Shot Learning.Plant phenomics (Washington, D.C.) · 2024Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automated counting of grape berries has become one of the most important tasks in grape yield prediction. However, dense distribution of berries and the severe occlusion between berries bring great challenges to counting algorithm based on deep learning. The collection of data required for model training is also a tedious and expensive work. To address these issues and cost-effectively count grape berries, a semi-supervised counting of grape berries in the field based on density mutual exclusion (CDMENet) is proposed. The algorithm uses VGG16 as the backbone to extract image features. Auxiliary tasks based on density mutual exclusion are introduced. The tasks exploit the spatial distribution pattern of grape berries in density levels to make full use of unlabeled data. In addition, a density difference loss is designed. The feature representation is enhanced by amplifying the difference of features between different density levels. The experimental results on the field grape berry dataset show that CDMENet achieves less counting errors. Compared with the state of the arts, coefficient of determination (
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Registered trials
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