ReviewPlants (Basel, Switzerland)2019
Plant Disease Detection and Classification by Deep Learning.
Review in Plants (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 88 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
88 citing papers in PubMed, 1 synthesis or guideline pooled it, 805 citations in OpenAlex.
- Deep Learning in Controlled Environment Agriculture: A Review of Recent Advancements, Challenges and Prospects.Sensors (Basel, Switzerland) · 2022Pooled it
- YOLOv11-MPD: A Multi-Part Maize Disease Detection Algorithm for Complex Field Environments.Sensors (Basel, Switzerland) · 2026Article
- HGSM-YOLO: A Small-Lesion-Oriented Lightweight YOLO11n Framework for Citrus Leaf Disease Detection.Sensors (Basel, Switzerland) · 2026Article
- Cross Disease Similarity Awareness Learning (CDSAL) with DenseNet-EfficientNet embedding fusion for high-precision tomato leaf pathology classification with Grad-CAM explainability.Scientific reports · 2026Article
- Deep convolutional models for robust multi-crop disease recognition in real-world conditions.Scientific reports · 2026Article
- Quantifying the reliability gap in cross-domain plant disease classification: benchmarking the limited efficacy of standard mitigation techniques under controlled-to-field shift.Frontiers in plant science · 2026Article
- Robust multi-target multi-scale tomato leaf disease detection for precision agriculture applications.Frontiers in plant science · 2026Article
- A biologically structured hierarchical vision transformer-CNN framework for robust tomato leaf disease classification.Frontiers in artificial intelligence · 2026Article
- Article
- Transfer learning models for wheat ear detection on multi-source dataset.Scientific reports · 2025Article
- Optimised MobileNet for very lightweight and accurate plant leaf disease detection.Scientific reports · 2025Article
- Hybrid Multi-Scale Neural Network with Attention-Based Fusion for Fruit Crop Disease Identification.Journal of imaging · 2025Article
- Automated Guava Disease Detection Using Transfer Learning With ResNet-101.Food science & nutrition · 2025Article
- Hyperspectral image analysis for classification of multiple infections in wheat.Plant methods · 2025Article
- Applying the transfer learning models on the dataset on the effect of diseases on Nagvel-betel (Piper betle) leaves.Data in brief · 2025Article
- Integrating Artificial Intelligence and Biotechnology to Enhance Cold Stress Resilience in Legumes.Plants (Basel, Switzerland) · 2025Review
- Precision diagnosis of citrus leaf diseases using image enhancement and nonlinear fuzzy ranking ensemble approach NLFuRBe.Scientific reports · 2025Article
- Article
- InsightNet: A Deep Learning Framework for Enhanced Plant Disease Detection and Explainable Insights.Plant direct · 2025Article
- Leveraging YOLO deep learning models to enhance plant disease identification.Scientific reports · 2025Article
28 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Plant diseases affect the growth of their respective species, therefore their early identification is very important. Many Machine Learning (ML) models have been employed for the detection and classification of plant diseases but, after the advancements in a subset of ML, that is, Deep Learning (DL), this area of research appears to have great potential in terms of increased accuracy. Many developed/modified DL architectures are implemented along with several visualization techniques to detect and classify the symptoms of plant diseases. Moreover, several performance metrics are used for the evaluation of these architectures/techniques. This review provides a comprehensive explanation of DL models used to visualize various plant diseases. In addition, some research gaps are identified from which to obtain greater transparency for detecting diseases in plants, even before their symptoms appear clearly.
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.