ReviewPlant methods2025
Recent advances in plant disease detection: challenges and opportunities.
Review in Plant methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 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
28 citing papers in PubMed.
- Hyperspectral imaging-based early stress and disease detection for sustainable crop management.Planta · 2026Review
- Multimodal Deep Learning and Foundation Models for Early Detection and Forecasting of Plant Diseases.Plants (Basel, Switzerland) · 2026Review
- Early Warning of Cucumber Angular Leaf Spot by Estimating Airborne Pathogen Aerosols with Particulate Matter Sensors.Plants (Basel, Switzerland) · 2026Article
- Computer Vision from Tea Cultivation to Quality Evaluation.Foods (Basel, Switzerland) · 2026Review
- A labelled dataset of healthy and diseased common bean (Data in brief · 2026Article
- YOLOv8n-DSLW: A Deployment-Oriented AI-Enabled Vision-Sensing Model for Tiny Strawberry Disease and Pest Detection in Greenhouse Images.Sensors (Basel, Switzerland) · 2026Article
- Article
- Beyond the Visual Spectrum: From RGB-Based Learning to Hyperspectral Intelligence for Plant Disease Detection-Challenges and Opportunities.Sensors (Basel, Switzerland) · 2026Review
- CKM-YOLO11: A Lightweight Maize Foliar Disease Detection Model for Complex Natural Field Environments.Sensors (Basel, Switzerland) · 2026Article
- Rose leaf disease classification and severity estimation using an interpretable vision transformer-based multi-task framework.BMC plant biology · 2026Article
- Review
- Enhanced corn leaf disease detection using sharpness-aware minimization optimized CNNs.Plant methods · 2026Article
- Article
- Fast Forward the Future: What Are the Key Drivers in Intelligent Sensing for Agriculture?Plants (Basel, Switzerland) · 2026Article
- A Survey of Crop Disease Recognition Methods Based on Spectral and RGB Images.Journal of imaging · 2026Review
- Lightweight Vision-Transformer Network for Early Insect Pest Identification in Greenhouse Agricultural Environments.Insects · 2026Article
- Editorial: Innovative field diagnostics for real-time plant pathogen detection and management.Frontiers in plant science · 2026Article
- Pre-symptomatic detection of wheat stem rust using hyperspectral imaging and deep learning.Frontiers in plant science · 2026Article
- Integrating Kolmogorov-Arnold networks and sparse attention for robust visual plant disease symptom identification across diverse agricultural crops.Frontiers in plant science · 2026Article
- Anisotropic boundary-aware detection for cotton leaf diseases with boundary-decoupled regression and lightweight feature adaptation.Frontiers in plant science · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Plant diseases cause approximately 220 billion USD in annual agricultural losses, driving demand for automated detection systems. This systematic review analyzes deep learning approaches for plant disease detection using RGB and hyperspectral imaging, examining their evolution from classical image processing to modern neural architectures. We evaluate state-of-the-art models across 11 benchmark datasets, revealing significant performance gaps between laboratory conditions (95-99% accuracy) and field deployment (70-85% accuracy). Transformer-based architectures demonstrate superior robustness, with SWIN achieving 88% accuracy on real-world datasets compared to 53% for traditional CNNs. Our analysis identifies three critical deployment constraints: environmental variability sensitivity, economic barriers (500-2000 USD for RGB vs. 20,000-50,000 USD for hyperspectral systems), and interpretability requirements for farmer adoption. Case studies of successful platforms (Plantix with 10+ million users) highlight the importance of offline functionality and multilingual support. We establish evidence-based guidelines prioritizing deployment viability over laboratory optimization and identify key research directions including lightweight model design, cross-geographic generalization, and explainable multimodal fusion. This review provides a comprehensive framework for advancing plant disease detection from research prototypes to practical agricultural tools that can improve global food security.
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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.