ArticlePlant phenomics (Washington, D.C.)2023
Instance Segmentation and Berry Counting of Table Grape before Thinning Based on AS-SwinT.
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 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 24 citations in OpenAlex.
- VT-SCC: Visual-text semantic alignment via multimodal guidance for unified staple crop organ counting.aBIOTECH · 2026Article
- DNAwhisper: An Integrated Deep Learning Pyramidal Framework for Multi-Trait Genomic Prediction and Adaptive Marker Prioritisation.Plant biotechnology journal · 2026Article
- Detection method for munage grape clusters and abnormal berries under color-similar backgrounds.Frontiers in plant science · 2026Article
- Incoherent Region-Aware Occlusion Instance Synthesis for Grape Amodal Detection.Sensors (Basel, Switzerland) · 2025Article
- Framework for smartphone-based grape detection and vineyard management using UAV-trained AI.Heliyon · 2025Article
- A precise berry counting method for in-cluster grapes to guide berry thinning.Frontiers in plant science · 2025Article
- SDC-DeepLabv3+: Lightweight and Precise Localization Algorithm for Safflower-Harvesting Robots.Plant phenomics (Washington, D.C.) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Berry thinning is one of the most important tasks in the management of high-quality table grapes. Farmers often thin the berries per cluster to a standard number by counting. With an aging population, it is hard to find adequate skilled farmers to work during thinning season. It is urgent to design an intelligent berry-thinning machine to avoid exhaustive repetitive labor. A machine vision system that can determine the number of berries removed and locate the berries removed is a challenge for the thinning machine. A method for instance segmentation of berries and berry counting in a single bunch is proposed based on AS-SwinT. In AS-SwinT, Swin Transformer is performed as the backbone to extract the rich characteristics of grape berries. An adaptive feature fusion is introduced to the neck network to sufficiently preserve the underlying features and enhance the detection of small berries. The size of berries in the dataset is statistically analyzed to optimize the anchor scale, and Soft-NMS is used to filter the candidate frames to reduce the missed detection of densely shaded berries. Finally, the proposed method could achieve 65.7
Identifiers
What OpenQuestion holds
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.