ArticleSensors (Basel, Switzerland)2022
Early Detection of Grapevine (
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review on the Advancements in Remote Sensing and Proximity Tools for Grapevine Disease Detection.Sensors (Basel, Switzerland) · 2024Pooled it
- A Mobile Application for Direct Light Compensation in Smartphone-Based Fruit Image Acquisition Systems.Sensors (Basel, Switzerland) · 2026Article
- Dataset of RGB images of healthy grapevine leaves and with downy mildew, powdery mildew, Esca complex, and erineum mite symptoms.Data in brief · 2026Article
- Optimizing chickpea yield prediction under wilt disease through synergistic integration of biophysical and image parameters using machine learning models.Scientific reports · 2025Article
- Artificial Intelligence Techniques in Grapevine Research: A Comparative Study with an Extensive Review of Datasets, Diseases, and Techniques Evaluation.Sensors (Basel, Switzerland) · 2024Review
- Article
- Advancements in Imaging Sensors and AI for Plant Stress Detection: A Systematic Literature Review.Plant phenomics (Washington, D.C.) · 2024Article
- Early Detection ofPlants (Basel, Switzerland) · 2023Article
- Combining biophysical parameters with thermal and RGB indices using machine learning models for predicting yield in yellow rust affected wheat crop.Scientific reports · 2023Article
- Food Image Segmentation Using Multi-Modal Imaging Sensors with Color and Thermal Data.Sensors (Basel, Switzerland) · 2023Article
- Early Detection of Grapevine (Sensors (Basel, Switzerland) · 2022Article
- A review of remote sensing for potato traits characterization in precision agriculture.Frontiers in plant science · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Agricultural industry is facing a serious threat from plant diseases that cause production and economic losses. Early information on disease development can improve disease control using suitable management strategies. This study sought to detect downy mildew (
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.