ReviewFrontiers in plant science2023
An advanced deep learning models-based plant disease detection: A review of recent research.
Review in Frontiers in plant science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 80 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.
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Who cites it
80 citing papers in PubMed, 401 citations in OpenAlex.
- Attention-driven YOLOv11n-DeiT model for enhanced detection of tomato leaf diseases.Scientific reports · 2026Article
- Explainable CNN framework for accurate crop disease detection using plant leaf images.Scientific reports · 2026Article
- Deep learning based apple leaf disease detection using spatially modulated continuouslayer.Scientific reports · 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
- A hybrid deep learning model with adaptive feature fusion for automated rice leaf disease detection and classification.Scientific reports · 2026Article
- Integrating deep learning and field validation into a decision support system for Northern Corn Leaf Blight management in maize.BMC plant biology · 2026Article
- Adaptive fuzzy deep learning with multimodal sensor fusion for enhanced plant disease detection.Scientific reports · 2026Article
- Deep convolutional models for robust multi-crop disease recognition in real-world conditions.Scientific reports · 2026Article
- AgroDualNet: a dual deep learning-based crop disease forecasting and fruit ripening detection.Scientific reports · 2026Article
- Leaf-DETR: Progressive adaptive network with lower matching cost for dense leaves detection.Plant phenomics (Washington, D.C.) · 2026Article
- RiceDetect-Net: a lightweight real-time detection framework for rice diseases.BMC plant biology · 2026Article
- A Large-Scale In-the-wild Dataset for Plant Disease Segmentation.Scientific data · 2026Article
- A biologically structured hierarchical vision transformer-CNN framework for robust tomato leaf disease classification.Frontiers in artificial intelligence · 2026Article
- Comparative evaluation of deep learning models for plant disease classification with edge-aware performance analysis.PloS one · 2026Article
- LSL-YOLO11n: a YOLO11n-based model for maize leaf disease detection in complex field environments.Frontiers in plant science · 2026Article
- AGMSF-VMUNet: attention-guided multi-scale feature fusion VM-UNet for crop pest detection.Frontiers in plant science · 2026Article
- ConvGeM-next: a deep learning framework for plant disease detection.Frontiers in plant science · 2026Article
- A resource-efficient framework for plant disease classification: integrating reduced-order modeling with treatment-based label engineering.Frontiers in plant science · 2026Article
- PlantPathNet: a novel network based on cross-layer feature integration for plant disease classification.Frontiers in plant science · 2026Article
- YOLOv11n-DualPC-Lite: a lightweight, high-precision real-time detection model for maize leaf diseases.Frontiers in plant science · 2026Article
20 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
9 authors at 7 institutions in 7 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation.
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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.