ArticleScientific reports2018
Unsupervised Segmentation of Greenhouse Plant Images Based on Statistical Method.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Design and experimental investigation of the grasping system of an agricultural soft manipulator based on FMDS-YOLOv8.Frontiers in plant science · 2025Article
- Advances in the tea plants phenotyping using hyperspectral imaging technology.Frontiers in plant science · 2024Review
- Depth image conversion model based on CycleGAN for growing tomato truss identification.Plant methods · 2022Article
- Study of Multiscale Fused Extraction of Cropland Plots in Remote Sensing Images Based on Attention Mechanism.Computational intelligence and neuroscience · 2022Article
- Easy domain adaptation method for filling the species gap in deep learning-based fruit detection.Horticulture research · 2021Article
- Tomato Fruit Detection and Counting in Greenhouses Using Deep Learning.Frontiers in plant science · 2020Article
- A Monitoring System for the Segmentation and Grading of Broccoli Head Based on Deep Learning and Neural Networks.Frontiers in plant science · 2020Article
- Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review.Frontiers in plant science · 2020Review
- A two-step registration-classification approach to automated segmentation of multimodal images for high-throughput greenhouse plant phenotyping.Plant methods · 2020Article
- Greenotyper: Image-Based Plant Phenotyping Using Distributed Computing and Deep Learning.Frontiers in plant science · 2020Article
- Guava Detection and Pose Estimation Using a Low-Cost RGB-D Sensor in the Field.Sensors (Basel, Switzerland) · 2019Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Complicated image scene of the agricultural greenhouse plant images makes it very difficult to obtain precise manual labeling, leading to the hardship of getting the accurate training set of the conditional random field (CRF). Considering this problem, this paper proposed an unsupervised conditional random field image segmentation algorithm ULCRF (Unsupervised Learning Conditional Random Field), which can perform fast unsupervised segmentation of greenhouse plant images, and further the plant organs in the image, i.e. fruits, leaves and stems, are segmented. The main idea of this algorithm is to calculate the unary potential, namely the initial label of the Dense CRF, by the unsupervised learning model LDA (Latent Dirichlet Allocation). In view of the ever-changing image features at different stages of fruit growth, a multi-resolution ULCRF is proposed to improve the accuracy of image segmentation in the middle stage and late stage of the fruit growth. An image is down-sampled twice to obtain three layers of different resolution images, and the features of each layer are interrelated with each other. Experiment results show that the proposed method can segment greenhouse plant images in an unsupervised method automatically and obtain a high segmentation accuracy together with a high extraction precision of the fruit part.
Indexed as
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.