ReviewHeliyon2021
Identification of citrus greening based on visual symptoms: A grower's diagnostic toolkit.
Review in Heliyon, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed, 27 citations in OpenAlex.
- Development of the sterile insect technique as a control strategy for the Asian citrus psyllid: determining the number of sterile individuals needed to suppress the growth of wild populations.Journal of economic entomology · 2026Article
- Analysis of Differential Metabolites in Citrus Leaves Infected with Huanglongbing and Screening of Biomarkers for Early Detection.Molecules (Basel, Switzerland) · 2026Article
- PCR-based detection and phylogenetic analysis of Candidatus Liberibacter asiaticus in citrus orchards across Nepal.PloS one · 2026Article
- Deep learning-based visual diagnosis of Huanglongbing and zinc deficiency in citrus leaves for sustainable and precision orchard management.Frontiers in plant science · 2026Article
- Hyperspectral Imaging and Machine Learning for Huanglongbing Detection on Leaf-Symptoms.Plants (Basel, Switzerland) · 2025Article
- Genome-Wide Identification of the DnaJ Gene Family in Citrus and Functional Characterization ofInternational journal of molecular sciences · 2024Article
- Diagnosis of Citrus Greening Using Artificial Intelligence: A Faster Region-Based Convolutional Neural Network Approach with Convolution Block Attention Module-Integrated VGGNet and ResNet Models.Plants (Basel, Switzerland) · 2024Article
- Metabolomic Analysis Revealed Distinct Physiological Responses of Leaves and Roots to Huanglongbing in a Citrus Rootstock.International journal of molecular sciences · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Citrus greening is one of the world's most serious diseases of citrus that affects all cultivars and causes the systematic death of trees worldwide. The disease is caused mostly by the bacteria
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.