ArticlePlant phenomics (Washington, D.C.)2023
The Gray Mold Spore Detection of Cucumber Based on Microscopic Image and Deep Learning.
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 14 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
14 citing papers in PubMed.
- Fungal morphotype detection and quantification in microscopic images with TU_MyCo-vision: a user-friendly deep learning object detection tool.Fungal biology and biotechnology · 2026Article
- YOLO-RBSD: an efficient and accurate rice blast spore detector based on improved YOLOv8.Plant methods · 2026Article
- Quantitative morphological phenotyping of infection structures in cucumber downy mildew and powdery mildew.Frontiers in plant science · 2026Article
- Detection and identification of insect processed animal proteins in microscopic images using deep learning.Current research in food science · 2026Article
- An intelligent method for detection of small target fungal wheat spores based on an improved YOLOv5 with microscopic images.Plant methods · 2025Article
- A privacy-protecting eggplant disease detection framework based on the YOLOv11n-12D model.Frontiers in plant science · 2025Article
- Detection techniques for tomato diseases under non-stationary climatic conditions.Frontiers in plant science · 2025Article
- Cucumber pathogenic spores' detection using the GCS-YOLOv8 network with microscopic images in natural scenes.Plant methods · 2024Article
- A deep semantic network-based image segmentation of soybean rust pathogens.Frontiers in plant science · 2024Article
- Lightweight cotton diseases real-time detection model for resource-constrained devices in natural environments.Frontiers in plant science · 2024Article
- Sensitivity Analysis ofPathogens & immunity · 2024Article
- Phenotyping of Drought-Stressed Poplar Saplings Using Exemplar-Based Data Generation and Leaf-Level Structural Analysis.Plant phenomics (Washington, D.C.) · 2024Article
- AFM-YOLOv8s: An Accurate, Fast, and Highly Robust Model for Detection of Sporangia ofPlant phenomics (Washington, D.C.) · 2024Article
- A Multiscale Point-Supervised Network for Counting Maize Tassels in the Wild.Plant phenomics (Washington, D.C.) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rapid and accurate detection of pathogen spores is an important step to achieve early diagnosis of diseases in precision agriculture. Traditional detection methods are time-consuming, laborious, and subjective, and image processing methods mainly rely on manually designed features that are difficult to cope with pathogen spore detection in complex scenes. Therefore, an MG-YOLO detection algorithm (
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.