ArticleHeliyon2024
Early detection and spectral signature identification of tomato fungal diseases (
Article in Heliyon, 2024. 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
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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.
- Hyperspectral imaging-based early stress and disease detection for sustainable crop management.Planta · 2026Review
- Fungal infections: Classification performance and detectability with machine learning models.Scientific reports · 2026Article
- VNIR Hyperspectral Signatures for Early Detection and Machine-Learning Classification of Wheat Diseases.Plants (Basel, Switzerland) · 2025Article
- A Novel Hybrid Technique for Detecting and Classifying Hyperspectral Images of Tomato Fungal Diseases Based on Deep Feature Extraction and Manhattan Distance.Sensors (Basel, Switzerland) · 2025Article
- Separating Chickens' Heads and Legs in Thermal Images via Object Detection and Machine Learning Models to Predict Avian Influenza and Newcastle Disease.Animals : an open access journal from MDPI · 2025Article
- A hyperspectral imaging and machine learning approach for rapid and non-invasive diagnosis of cassava bacterial blight.Frontiers in plant science · 2025Article
- Research on grape leaf disease recognition method based on improved YOLOv8n model.Frontiers in plant science · 2025Article
- Exploring Imaging Techniques for Detecting Tomato Spotted Wilt Virus (TSWV) Infection in Pepper (Plants (Basel, Switzerland) · 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early identification of plant fungal diseases is critical for timely treatment, which can prevent significant agricultural losses. While molecular analysis offers high accuracy, it is often expensive and time-consuming. In contrast, image processing combined with machine learning provides a rapid and cost-effective alternative for disease diagnosis. This study presents a novel approach for detecting four common fungal diseases in tomatoes,
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Identifiers
What Socratic 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 Socratic graph.