ArticleScientific reports2025
YOLO-LeafNet: a robust deep learning framework for multispecies plant disease detection with data augmentation.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- HGSM-YOLO: A Small-Lesion-Oriented Lightweight YOLO11n Framework for Citrus Leaf Disease Detection.Sensors (Basel, Switzerland) · 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
- Optimized CNN-based ensemble deep learning approach for potato leaf disease detection with data augmentation.Scientific reports · 2026Article
- Lightweight attention enhanced YOLOv11 for accurate multi class detection of brinjal diseases.Scientific reports · 2026Article
- A novel approach for disease and pests detection in potato production system based on deep learning.Scientific reports · 2026Article
- Lightweight scalable deep learning framework for real time detection of potato leaf diseases.Scientific reports · 2026Article
- Real-time detection of plant leaf diseases based on improved YOLOv13-LM in complex field environments.Frontiers in plant science · 2026Article
- A study on plant disease and pest detection and counting based on multi-scale enhancement and cross-scale fusion.Frontiers in plant science · 2026Article
- Mobile-assisted deep learning framework for identification of insect pests and diseases of maize from field images.Frontiers in plant science · 2026Article
- EffResViT-SE FusionNet: A Hybrid Deep Learning Framework for Accurate Classification of Coffee Leaf Diseases.Food science & nutrition · 2025Article
- Hybrid Deep Learning Model for Date Palm Disease Classification: A Fusion of HybridConv Mixer and Vision Transformer.Food science & nutrition · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Plant diseases significantly harm crops, resulting in significant economic losses across the globe. In order to reduce the harm that these diseases produce, plant diseases must be diagnosed accurately and timely manner. In this work, a YOLO-LeafNet approach is proposed for detecting diseases from leaf images of four distinct species, namely, grape, bell pepper, corn, and potato. About 8850 leaf images have been acquired for this work from five different publicly available datasets on Kaggle. All the acquired images were pre-processed by applying four different image pre-processing operations. The number of images in the training dataset was tripled for better model performance by applying five different augmentation operations. The augmented dataset was then used to train YoloV5, YoloV8, and the proposed YOLO-LeafNet. The performance of all three models is evaluated in terms of recall, precision, and Mean Average Precision (mAP). The YoloV5 attained a precision of 0.861, recall of 0.868, mAP50 of 0.944, and 0.815 of mAP50-95, and YoloV8 attained 0.977 precision, 0.975 recall, 0.984 of mAP50, and mAP50-95 of 0.915, whereas the proposed the YOLO-LeafNet attained precision of 0.985, recall of 0.980, mAP50 of 0.990, and mAP50-95 of 0.940. The experimental results reveal that the proposed YOLO-LeafNet outperformed YOLOv5 and YOLOv8 in terms of all performance metrics.
Indexed as
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.