ReviewPlants (Basel, Switzerland)2020
Review of the State of the Art of Deep Learning for Plant Diseases: A Broad Analysis and Discussion.
Review in Plants (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 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
33 citing papers in PubMed.
- FCDNet: An Efficient and Cost-Effective Strawberry Disease Detection Model for Smart Farming Management.Plants (Basel, Switzerland) · 2026Article
- Deep learning-based citrus plant disease classification using a computationally efficient CNN model.Scientific reports · 2026Article
- AI-driven drone technology and computer vision for early detection of crop disease in large agricultural areas.Scientific reports · 2025Article
- Hybrid Deep Learning Model for Date Palm Disease Classification: A Fusion of HybridConv Mixer and Vision Transformer.Food science & nutrition · 2025Article
- A Review on the Detection of Plant Disease Using Machine Learning and Deep Learning Approaches.Journal of imaging · 2025Review
- Bayesian optimized multimodal deep hybrid learning approach for tomato leaf disease classification.Scientific reports · 2024Article
- A novel hierarchical framework for plant leaf disease detection using residual vision transformer.Heliyon · 2024Article
- SeptoSympto: a precise image analysis of Septoria tritici blotch disease symptoms using deep learning methods on scanned images.Plant methods · 2024Article
- Early stage black pepper leaf disease prediction based on transfer learning using ConvNets.Scientific reports · 2024Article
- A novel method for vegetable and fruit classification based on using diffusion maps and machine learning.Current research in food science · 2024Article
- Siamese Unet Network for Waterline Detection and Barrier Shape Change Analysis from Long-Term and Large Numbers of Satellite Imagery.Sensors (Basel, Switzerland) · 2023Article
- Construction of deep learning-based disease detection model in plants.Scientific reports · 2023Article
- Artificial Intelligence: A Promising Tool in Exploring the Phytomicrobiome in Managing Disease and Promoting Plant Health.Plants (Basel, Switzerland) · 2023Review
- Automatic Clustering and Classification of Coffee Leaf Diseases Based on an Extended Kernel Density Estimation Approach.Plants (Basel, Switzerland) · 2023Article
- Rice pest identification based on multi-scale double-branch GAN-ResNet.Frontiers in plant science · 2023Article
- An advanced deep learning models-based plant disease detection: A review of recent research.Frontiers in plant science · 2023Review
- Crop-saving with AI: latest trends in deep learning techniques for plant pathology.Frontiers in plant science · 2023Review
- Deep learning for Chilean native flora classification: a comparative analysis.Frontiers in plant science · 2023Article
- PMVT: a lightweight vision transformer for plant disease identification on mobile devices.Frontiers in plant science · 2023Article
- Few-Shot Learning for Plant-Disease Recognition in the Frequency Domain.Plants (Basel, Switzerland) · 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning (DL) represents the golden era in the machine learning (ML) domain, and it has gradually become the leading approach in many fields. It is currently playing a vital role in the early detection and classification of plant diseases. The use of ML techniques in this field is viewed as having brought considerable improvement in cultivation productivity sectors, particularly with the recent emergence of DL, which seems to have increased accuracy levels. Recently, many DL architectures have been implemented accompanying visualisation techniques that are essential for determining symptoms and classifying plant diseases. This review investigates and analyses the most recent methods, developed over three years leading up to 2020, for training, augmentation, feature fusion and extraction, recognising and counting crops, and detecting plant diseases, including how these methods can be harnessed to feed deep classifiers and their effects on classifier accuracy.
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.